# Attribution-aware Weight Transfer: A Warm-Start
Initialization for Class-Incremental Semantic Segmentation

Dipam Goswami†§ René Schuster† Joost van de Weijer‡ Didier Stricker†
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
† DFKI - German Research Center for Artificial Intelligence, Kaiserslautern
§ Birla Institute of Technology and Science, Pilani ‡ Computer Vision Center,
Barcelona

###### Abstract

In class-incremental semantic segmentation (CISS), deep learning architectures
suffer from the critical problems of catastrophic forgetting and semantic
background shift. Although recent works focused on these issues, existing
classifier initialization methods do not address the background shift problem
and assign the same initialization weights to both background and new
foreground class classifiers. We propose to address the background shift with
a novel classifier initialization method which employs gradient-based
attribution to identify the most relevant weights for new classes from the
classifier’s weights for the previous background and transfers these weights
to the new classifier. This warm-start weight initialization provides a
general solution applicable to several CISS methods. Furthermore, it
accelerates learning of new classes while mitigating forgetting. Our
experiments demonstrate significant improvement in mIoU compared to the state-
of-the-art CISS methods on the Pascal-VOC 2012, ADE20K and Cityscapes
datasets.

##  1 Introduction

Semantic segmentation assigns a class label to every pixel of an image. The
emergence of deep neural networks as well as the availability of pixel-level
annotated datasets [21, 57, 14] has achieved state-of-the-art performance on
semantic segmentation tasks [33, 55, 13]. The majority of papers in the field
considers that all classes are labelled in all training data, and that all
training data is jointly available. However, for many applications this is an
unrealistic scenario, and the algorithm has to learn to segment all classes
from partially labelled data, and every moment (called _step_ in CISS) only
has access to a limited batch of training data. This restriction is imposed
either by data storage limitations or data privacy and data security
considerations [15]. Incremental learning [15, 36] proposes algorithms for
this setting where the main challenge is to prevent _catastrophic forgetting_
[37] which refers to a drop in performance on classes learned in previous
steps.

Figure 1: Comparison of classifier initialization methods. Classifier
initialization was previously found to be crucial to obtain good plasticity
[7, 18, 8, 49, 54] of an incremental learner. However, the method did not
address the _background shift_. Previous methods (left) copy all filter
weights from previous background (bkg) to initialize _new classes_ , our
attribution-based weight selection (right) explicitly addresses the _semantic
background shift_ by selecting only the relevant weights for the
classification of _new classes_. This allows us to combine the plasticity of
the previous initialization method with a further reduction of catastrophic
forgetting.

Another critical challenge faced by CISS approaches is the _semantic
background shift_. This challenge does not exist for incremental image
classification, and is a result of the multi-class nature of image
segmentation. The ground truth at any step provides labels for pixels
belonging to current classes only and considers all other pixels as background
while the model should correctly classify the old and current class pixels to
their original labels and the future class pixels to the background. So, the
background class includes the real background class, the previously seen
classes, and the future classes. As a result, there exists ambiguity due to
changing definition of the background class from step to step.

In this paper, we highlight the importance of the initialization of the
classifier’s weights for the new classes. Since at every step, the final
classifier layer has to predict the new classes in addition to the past
classes, the classifier’s weights for the new classes should be well aligned
with the features extracted by the model. Thus, the initialization of
classifier’s weights is critical for stable training of the model and faster
convergence on the new classes resulting in less forgetting of past classes.
MiB [7] adapted weight imprinting [41] for segmentation and initialized the
classifier’s weights for the new classes and the background with the
classifier’s weights for the previous background. This initialization has been
followed in most of the recent approaches [18, 8, 49, 54] but it does not
address the semantic background shift problem. Instead, we propose a new warm-
start classifier initialization method that explicitly tackles the background
shift, differentiating the current foreground classes from the previous
background at the classifier level as depicted in Figure 1.

We propose to transfer the learning of the future classes from the previous
background to the new classes by weight transfer from relevant classifier
input channels. The proposed method follows the strict incremental setting
[36], which does not need access to any data from past or future classes. Our
method can be used with various CISS approaches. We validate our proposed
approach using multiple CISS settings, class orders and ablation experiments.
The main contributions can be summarized as follows:

  * •

To better address the background shift, we apply an attribution method to
identify the most relevant classifier channels for predicting the new classes
as previous background and transfer only those channel weights.

  * •

Our method reduces catastrophic forgetting on old classes while improving
plasticity on new classes, owing to quicker convergence on new classes.

  * •

We experimentally show that our method significantly outperforms the state-of-
the-art approaches on several incremental settings and datasets.

##  2 Related Work

Semantic Segmentation: Commonly used segmentation approaches are based on
Fully Convolutional Networks (FCNs) [33]. These methods improve the accuracy
by using multiscale representations [32], retaining more spatial information
by using atrous convolution [10] or convolution with upsampled filters,
modelling contextual cues [10], or by using attention mechanisms [12, 56].
Recent approaches used strip pooling [23] along the width or height dimensions
to capture both global and local statistics. In our work, we use the Deeplabv3
[11] architecture which employs atrous convolution in parallel manner in order
to capture multi-scale context to segment objects at multiple scales.

Incremental Learning: Most studies in incremental learning have focused on
object detection and classification problems [6, 31, 42, 44, 48]. Some of
these works use replay-based approaches, which store samples from previous
tasks [42, 6] or generate training data [26, 43]. Some methods extend the
initial architecture to learn new classes [50, 30] or use embedding networks
[52] or use classifier drift correction to account for changing class
distributions [3, 4]. Distillation-based methods constrain the learning of the
model on new tasks by penalizing updates on the weights [27, 1] or the
gradients [34, 9] or the intermediate features [24, 17, 20]. Our work focuses
on the distillation-based approaches for semantic segmentation.

Class-Incremental Semantic Segmentation: Recently, incremental learning has
been studied for semantic segmentation [7, 8, 18, 19, 25, 39, 28, 38, 51].
Initial approaches use relevant examples from old tasks and perform rehearsal
for segmentation in medical imaging [40] and remote sensing data [47].
Michieli _et al_. [38] considered an incremental setting where labels for old
classes are available when learning new tasks. Cermelli _et al_. [7] was the
first to highlight the semantic background shift and proposed a novel
distillation method to tackle the shift. Douillard _et al_. [18, 19] proposed
using multi-scale spatial distillation loss to preserve short and long range
dependencies. Cha _et al_. [8] proposed SSUL which considers a separate class
apart from the semantic background class for old and future classes in
addition to freezing the backbone and past classifiers. UCD [49] proposed to
enforce similarity between features for pixels of same classes and reduce
feature similarity for pixels of different classes. RCIL [54] decoupled the
learning of both old and new classes and introduced pooled cube knowledge
distillation on channel and spatial dimensions.

Replay of samples from previous classes has also been used for CISS either by
storing images from old tasks [8] or by recreating them using generative
networks [35]. Self-training approach using unlabelled data [51] has also been
proposed. We propose to model the semantic background shift for the classifier
initialization used in [7, 18, 8, 49, 54] without using any data from the
previous steps.

Attribution Methods: Feature attribution methods assign importance scores to
the features for a given input which are responsible for the class prediction.
Existing attribution methods are based on perturbation or backpropagation.
Perturbation methods [53] compute the attributions of input features by
removing or masking them and then do a forward pass to measure the difference
in outputs. Backpropagation methods compute the attributions for the input
features by doing one forward and backward pass. Some of these methods are
DeepLIFT [45], Integrated Gradients [46] and Layer-wise Relevance Propagation
(LRP) [2]. We use the popular Integrated Gradients [46] which requires no
modification to the network and is simple to implement.

##  3 Proposed Weight Transfer Method

###  3.1 Class-Incremental Semantic Segmentation

Consider an image $x$ and label space $\mathcal{C}$, semantic segmentation
aims to assign a label $c_{i}\in\mathcal{C}$ to every pixel in $x$. Provided
with a training set $\mathcal{T}$, a model $f_{\theta}$ having parameters
$\theta$ is learned which maps the input image to the pixel-wise class
probabilities. In an incremental setup, the model is learned in $t=1...T$
steps. The training set at incremental step $t$ is
$\mathcal{T}^{t}=\\{(x_{1}^{t},y_{1}^{t}),...,(x_{n^{t}}^{t},y_{n^{t}}^{t})\\}$
where $x_{i}^{t}\in X^{t}$ is the set of images, $y_{i}^{t}\in Y^{t}$ is the
set of corresponding ground truth maps and a new set of classes
$\mathcal{C}^{t}$ is added to the existing set of classes
$\mathcal{C}^{1:t-1}$. Since the background class is present in all the
incremental steps, we denote it as $b^{t}$ at step $t$. The model at step $t$
is denoted as $f_{\theta^{t}}$ which learns the parameters $\theta^{t}$.

For an image $x_{i}^{t}\in X^{t}$, the ground truth segmentation map
$y_{i}^{t}\in Y^{t}$ only provides the labels of current classes
$\mathcal{C}^{t}$ while collapsing all other labels (old classes
$\mathcal{C}^{1:t-1}$ and future classes $\mathcal{C}^{t+1:T}$) into the
background class $b^{t}$. The model after step $t$ is expected to predict all
classes learned over time $\mathcal{C}^{1:t}$. Here, both the real background
class pixels and the future class $\mathcal{C}^{t+1:T}$ pixels should be
predicted as background $b^{t}$. Hence, the future classes classified as
background after the first step gradually become the foreground during the
incremental steps. During the inference of the final step, only the real
background class should be classified as the background.

###  3.2 Problems with Existing Initialization Method

We discuss the existing initialization approach and the problems that are yet
to be addressed. Since the pixels of $\mathcal{C}^{t}$ are learned as
background $b^{t-1}$ at the previous step, the old model $f_{\theta^{t-1}}$
will most likely assign these pixels to class $b^{t-1}$. To account for this
initial bias on predictions of $f_{\theta^{t}}$ for pixels of
$\mathcal{C}^{t}$, Cermelli _et al_., [7] proposed to initialize the
classifier’s weights for the classes in $\mathcal{C}^{t}$ (including
background) with the weights for the previous background class so that the
background class probability is uniformly spread among the classes in
$\mathcal{C}^{t}$ ($b^{t}\in\mathcal{C}^{t}$). It is important to note, that
this classifier initialization was found to be crucial to achieve good
plasticity. For several settings, classifier initialization more than doubles
performance on the classes learned after the first step (see for example Table
3 in [7]).

However, this direct transfer of classifier’s weights from background to new
classes does not directly address the shift of classes from background to
foreground across time, which is one of the main challenge for CISS problems.
The background classifier weights are learned for the real background and
future classes $\mathcal{C}^{t+1:T}$ but the direct transfer guides the model
to initially assign high probabilities for pixels of $\mathcal{C}^{t+1:T}$ and
real background class to $\mathcal{C}^{t}$ instead of $b^{t}$.

Figure 2: Attribution maps for the background (_bkg_) class corresponding to
different channels of the classifier layer. The new classes (sheep, sofa,
monitor) belong to the _bkg_ of the previous step. Attribution map (a) has
high contribution towards predicting the real _bkg_ pixels and does not
predict the new class while the attribution map (b) contributes more towards
predicting the new class pixels as _bkg_. Figure 3: Overview of AWT: Images
from the current step are given to the old model at step $t-1$. The inputs to
the classifier layer are used to generate the layer attributions. Here, the
attribution map (b) is more significant for new class pixels than map (a). We
mask the attribution maps for the background pixels using the ground truth.
Masked attributions from all images of step $t$ are averaged and max-pooled to
obtain channel attributions. The significant channels are then selected using
a threshold $k$ and these channel weights are transferred to the classifier
weights for the new classes.

###  3.3 Novel Warm-Start Classifier Initialization

To explicitly address the background shift at the initialization stage, we
propose Attribution-aware Weight Transfer (AWT) as illustrated in Figure 3.
AWT aims to transfer only the significant classifier channel weights from the
background $b^{t-1}$ to the new classes in $\mathcal{C}^{t}$. We use
attribution methods to select the classifier weights for the background at the
previous step, which have significant contributions in predicting the pixels
of new classes in $\mathcal{C}^{t}$ as $b^{t-1}$ (as depicted in Figure 2).
Here, we exploit the ability of the background classifier to learn different
representations using different channels. This selection separates the
classifier level weights for the new classes in $\mathcal{C}^{t}$ and the
future classes. AWT aims not to transfer the significant classifier weights
for the future classes $\mathcal{C}^{t+1:T}$ to the new classes thereby
maintaining the stability of the model and accelerating learning of the new
classes.

####  3.3.1 Attribution-aware Channel Selection

Integrated Gradients [46] approximates the integral of the gradients of the
model’s output with respect to the inputs along a straight-line path from
baselines to inputs. Here, baseline refers to the starting point from which
integral is computed and is taken as blank (zero) input. We employ the
Integrated Gradients attribution method, which assigns importance scores to
the inputs of the classifier layer for predicting the background $b^{t-1}$.
More details of Integrated Gradients are provided in the supplementary
material.

We use the images from the current training set $X^{t}$ and the old model
$f_{\theta^{t-1}}$ for computing the attribution maps for each of the input
channels to the classifier layer. Since, the old and future class pixels are
labelled as $b^{t}$ in the ground truth maps at step $t$, we obtain a ground
truth mask $\bar{y}^{t}_{i}$ for new classes only by masking the pixels $m$
belonging to $b^{t}$ as follows:

${\bar{y}}^{t}_{i}[m]=\begin{cases}0&\text{if $y^{t}_{i}[m]=b^{t}$}\\\
1&\text{otherwise}\end{cases}$ (1)

We use the ground truth masks $\bar{y}^{t}_{i}$ to obtain the attributions
corresponding to the pixels of new classes in $\mathcal{C}^{t}$ only. Since we
consider only one set of channel mask for each new class in $\mathcal{C}^{t}$,
we take the mean of the masked attributions from all the images in $X^{t}$.
Let $A$ be the set of classifier layer attribution maps, each of size $W\times
H\times C$ for all images in $X^{t}$ obtained using Integrated Gradients, then
we obtain the mean attribution as follows:

$A_{avg}=\frac{1}{n^{t}}\sum_{i=1}^{n^{t}}A(x^{t}_{i})\odot{\bar{y}}_{i}^{t}$
(2)

where $\odot$ refers to an element-wise multiplication along the spatial
dimensions.

Max-Pooling is performed on the mean attribution $A_{avg}$ to get an
attribution value for each of the channels of classifier’s weights for
background $b^{t-1}$. We transform the mean attribution map of size $W\times
H\times C$ to channel attribution $A_{c}$ of size $C$ with pooling along the
channel axis as follows:

$A_{c}[c]=\max_{w\in[1,W],h\in[1,H]}(A_{avg}[w,h,c])$ (3)

where $c\in[1,C]$. Note that we choose max-pooling over average-pooling based
on experiments in Section 4.3.

####  3.3.2 Classifier Initialization

A threshold $k$ is applied on the channel attribution to obtain a channel mask
$c_{mask}$ to select the most contributing channels as follows:

$c_{mask}[c]=\begin{cases}1&\text{if $A_{c}[c]>k$}\\\
0&\text{otherwise}\end{cases}$ (4)

where $A_{c}$ denotes the channel attribution and $c$ refers to the channels.
Let the classifier’s weights for the class $c$ at step $t$ be $w^{t}_{c}$ and
the classifier’s weights for the background at step $t-1$ be $w^{t-1}_{b}$. We
propose to initialize the classifier’s weights for the new classes with the
selected channel’s weights as follows:

$w^{t}_{c}=\begin{cases}w^{t*}_{c}+w^{t-1}_{b}\cdot c_{mask}&\text{if
$c\in\mathcal{C}^{t}\setminus b^{t}$}\\\
w^{t-1}_{c}&\text{otherwise}\end{cases}$ (5)

where $w^{t*}_{c}$ refers to the default initialized weights. We transfer the
masked weights by adding them on top of the default weights and thus we avoid
having zero weights for the remaining channels. We show in Section 4.3 that
adding the weights is beneficial compared to copying.

Table 1: Experimental results on Pascal VOC 2012. Improvements using AWT underlined. Best among columns in bold. $\dagger$: results excerpted from [54]. * implies results come from re-implementation. Other results come from the respective papers. | 15-5 (2 steps) | 15-1 (6 steps) | 5-3 (6 steps) | 10-1 (11 steps)
---|---|---|---|---
Method | 0-15 | 16-20 | all | 0-15 | 16-20 | all | 0-5 | 6-20 | all | 0-10 | 11-20 | all
ILT† [38] | 67.8 | 40.6 | 61.3 | 9.6 | 7.8 | 9.2 | 22.5 | 31.7 | 29.0 | 7.2 | 3.7 | 5.5
SDR† [39] | 76.3 | 50.2 | 70.1 | 47.3 | 14.7 | 39.5 | - | - | - | 32.4 | 17.1 | 25.1
PLOP [18] | 75.7 | 51.7 | 70.1 | 65.1 | 21.1 | 54.6 | 17.5 | 19.2 | 18.7 | 44.0 | 15.5 | 30.5
MiB+UCD [49] | 78.5 | 50.7 | 71.5 | 51.9 | 13.1 | 42.2 | - | - | - | 33.7 | 26.5 | 31.1
RCIL† [54] | 78.8 | 52.0 | 72.4 | 70.6 | 23.7 | 59.4 | 59.3 | 33.8 | 41.1 | 55.4 | 15.1 | 34.3
MiB [7] | 75.5 | 49.4 | 69.0 | 35.1 | 13.5 | 29.7 | - | - | - | 12.3 | 13.1 | 12.7
MiB* [7] | 76.4 | 49.4 | 70.0 | 48.1 | 15.8 | 40.4 | 58.2 | 41.3 | 46.1 | 14.1 | 13.8 | 13.9
MiB+AWT (Ours) | 77.3 | 52.9 | 71.5 | 59.1 | 17.2 | 49.1 | 61.8 | 45.9 | 50.4 | 33.2 | 18.0 | 26.0
SSUL [8] | 77.8 | 50.1 | 71.2 | 77.3 | 36.6 | 67.6 | 72.4 | 50.7 | 56.9 | 71.3 | 46.0 | 59.3
SSUL+AWT (Ours) | 78.0 | 50.2 | 71.4 | 77.0 | 37.6 | 67.6 | 71.6 | 51.4 | 57.1 | 73.1 | 47.0 | 60.7
Joint | 79.8 | 72.4 | 77.4 | 79.8 | 72.4 | 77.4 | 76.9 | 77.6 | 77.4 | 78.4 | 76.4 | 77.4

##  4 Experiments

###  4.1 Experimental settings

Datasets: We conduct experiments on the segmentation datasets namely Pascal
VOC 2012 [21], ADE20K [57] and Cityscapes [14] using different incremental
splits. Pascal VOC 2012 [21] covers 20 object (or _things_) classes and one
background class. ADE20K [57] is a large scale dataset containing 150 classes
of both _things_ and _stuff_ (uncountable or amorphous regions like sky or
grass). Cityscapes [14] has 19 classes having both _things_ and _stuff_ and
covering scenes from 21 different urban cities.

CISS Protocols: Two different CISS settings introduced by [7] are _disjoint_
and _overlapped_. While the _disjoint_ setting assumes that the future classes
are known and removes images with future classes from the current step, the
_overlapped_ setting is more realistic and has no such assumption. Similar to
[18, 8], we also follow the _overlapped_ setting in our experiments. We denote
the different settings as X-Y where X is the number of classes in the initial
step followed by Y number of classes at every step until all the classes are
seen. We train 15-5 (15 classes followed by 5 classes), 15-1 (15 then 1 class
in each step), 5-3 and 10-1 settings on VOC. Similarly, we train 100-50,
100-10, 100-5 and 50-50 on ADE20K and 14-1 and 10-1 settings on Cityscapes.

Metrics: The mean Intersection over Union (mIoU) metric is calculated after
the last step for the initial set of classes, the incremental classes, and for
all the classes. The mIoU for the initial classes reflects the stability of
model to catastrophic forgetting. The mIoU for the incremental classes
reflects the plasticity of the model to learn new classes and the overall mIoU
metric signifies the overall performance.

Implementation Details: Deeplab-v3 [11] with a ResNet-101 [22] backbone
pretrained on ImageNet [16] having output stride of 16 is used for the
experiments. Similar to [54], we use a higher initial learning rate and obtain
an improved baseline for MiB. We train MiB and MiB+AWT models with SGD and a
learning rate of $2\times 10^{-2}$ for the first step only and $10^{-3}$ for
the incremental steps. The models are trained with a batch size of 24 using 2
GPUs for 30 epochs per step for VOC and Cityscapes and 60 epochs for ADE20K.
Specific to SSUL models, we follow the same training settings as [8] since it
performs freezing of weights and requires different training hyperparameters.
The final results are reported on the validation set of the datasets. Since
Cityscapes does not have a real background class, we merge the unlabeled
classes into a virtual background class.

We use layer integrated gradients from [29] for obtaining the attributions and
a threshold $k$ to select the 25% most significant channels for new classes
based on experiments provided in the supplementary material. We obtain a
unique set of channels mask for each new class for all settings having 5 or
lesser class increments. For settings like 100-10, 100-50 and 50-50 on ADE20K,
we use a single channel mask for all the new classes. Code is publicly
available111https://github.com/dfki-av/AWT-for-CISS.

Baselines: We compare our approach with the recent state-of-the-art methods
ILT [38], MiB [7], SDR [39], PLOP [18], SSUL [8], RCIL [54] and UCD [49]. We
apply AWT on two methods, MiB [7] and SSUL [8]. We also compare with the upper
bound (Joint model learned in non-incremental manner). We do not consider
approaches using data from past steps [35] or auxiliary unlabeled data [51].

Table 2: Experimental results on ADE20K. Improvements using AWT underlined.
Best among columns in bold. $\dagger$: results excerpted from [54]. * implies
results come from re-implementation. Other results come from the respective
papers.

| 100-50 (2 steps) | 100-10 (6 steps) | 50-50 (3 steps)
---|---|---|---
Method | 1-100 | 101-150 | all | 1-100 | 101-110 | 111-120 | 121-130 | 131-140 | 141-150 | all | 1-50 | 51-100 | 101-150 | all
ILT† [38] | 18.3 | 14.8 | 17.0 | 0.1 | 0.0 | 0.1 | 0.9 | 4.1 | 9.3 | 1.1 | 13.6 | 12.3 | 0.0 | 9.7
PLOP [18] | 41.9 | 14.9 | 32.9 | 40.6 | 15.2 | 16.9 | 18.7 | 11.9 | 7.9 | 31.6 | 48.6 | 30.0 | 13.1 | 30.4
PLOP+UCD [49] | 42.1 | 15.8 | 33.3 | 40.8 | - | - | - | - | - | 32.3 | 47.1 | - | - | 31.8
SSUL* [8] | 38.0 | 20.5 | 32.2 | 36.5 | 16.5 | 29.0 | 21.7 | 16.4 | 13.5 | 30.8 | 44.1 | 23.0 | 18.6 | 28.7
RCIL† [54] | 42.3 | 18.8 | 34.5 | 39.3 | 14.6 | 26.3 | 23.2 | 12.1 | 11.8 | 32.1 | 48.3 | 31.3 | 18.7 | 32.5
MiB† [7] | 40.5 | 17.7 | 32.8 | 38.3 | 12.6 | 10.6 | 8.7 | 9.5 | 15.1 | 29.2 | 45.3 | 26.1 | 17.1 | 29.3
MiB* [7] | 41.5 | 22.9 | 35.3 | 38.9 | 10.3 | 13.8 | 12.3 | 5.1 | 13.0 | 29.6 | 46.1 | 27.1 | 21.8 | 31.8
MiB+AWT (Ours) | 40.9 | 24.7 | 35.6 | 39.1 | 14.3 | 31.9 | 24.4 | 20.6 | 15.2 | 33.2 | 46.6 | 30.1 | 23.6 | 33.5
Joint | 44.3 | 28.2 | 38.9 | 44.3 | 26.1 | 42.8 | 26.7 | 28.1 | 17.3 | 38.9 | 51.1 | 38.3 | 28.2 | 38.9

###  4.2 Quantitative Evaluation

Pascal VOC 2012: We show the quantitative experiments on VOC 15-5, 15-1, 5-3
and 10-1 settings in Table 1. We observe that while ILT struggle on all
settings, other methods show significant improvements. Pooling-based
distillation methods like PLOP and RCIL do better in 15-5, 15-1 and 10-1
settings but these methods perform poorly on the 5-3 setting where the number
of classes is less in the initial step.

AWT with MiB outperforms MiB significantly on all the settings. On 15-5, our
model outperforms MiB by 1.5 percentage point ($p.p$) on the overall mIoU
metric. On the 15-1 setting, our model reduces the forgetting of the initial
classes by 11 $p.p$ while the overall performance improves by 8.7 $p.p$. On
the 5-3 setting having multiple class increments, AWT improves the overall
mIoU by 4.3 $p.p$ over MiB. On the most challenging setting of 10-1 having 11
steps, AWT reduces the forgetting of the initial classes by 19.1 $p.p$ and
improves the learning of new classes by 4.2 $p.p$.

AWT with SSUL [8] performs similar to SSUL for the 15-5, 15-1 and 5-3
settings, while for the challenging 10-1 setting, it reduces forgetting of the
initial classes by 1.8 $p.p$ and improves the performance on new classes by
1.0 $p.p$. SSUL makes use of saliency maps targeted for _things_ or objects
and moves them from background to an unknown class for representing the future
classes. This label augmentation improves performance on all settings of VOC
since this dataset has only object classes. On the contrary, this saliency-
based modelling is not applicable for ADE20K, Cityscapes and other datasets
which have both _things_ and _stuff_ classes, and SSUL suffers from high
forgetting as we observe in Tables 2 and 4.

ADE20K: ADE20K [57] is a difficult dataset with 150 classes and has the joint
model mIoU of only 38.9%. We report the experimental results on ADE20K 100-50,
100-10 and 50-50 in Table 2 with analysis of performance on the incremental
sets of classes. We also consider a long setting of 100-5 (11 tasks) in Table
3.

Table 3: Experimental results on the 100-5 setting on ADE20K. Improvements using AWT underlined. Best among columns in bold. $\dagger$: results excerpted from [54]. * implies results come from re-implementation. | 100-5 (11 steps)
---|---
Method | 1-100 | 101-150 | all
ILT† [38] | 0.1 | 1.3 | 0.5
PLOP [18] | 39.1 | 7.8 | 28.8
RCIL† [54] | 38.5 | 11.5 | 29.6
SSUL* [8] | 36.0 | 18.2 | 30.1
MiB† [7] | 36.0 | 5.6 | 25.9
MiB* [7] | 36.9 | 5.4 | 26.5
MiB+AWT (Ours) | 38.6 | 16.0 | 31.1
Joint | 44.3 | 28.2 | 38.9

On 100-50, our model improves the overall performance over MiB by 0.3 $p.p$.
On 50-50 setting, our model achieves an overall improvement of 1.7 $p.p$ over
MiB and 1.0 $p.p$ over RCIL. Moving to the longer sequence of 100-10 with 6
steps, our model improves MiB by 3.6 $p.p$ and PLOP+UCD by 0.9 $p.p$. On the
11 step setting of 100-5, AWT improves MiB by 4.6 $p.p$ and its nearest
contender SSUL by 1.0 $p.p$. MiB+AWT achieves state-of-the-art results on all
settings of ADE20K indicating the robustness towards predicting both _things_
and _stuff_ classes.

Cityscapes: We perform CISS experiments on two long sequence settings of 14-1
(6 tasks) and 10-1 (10 tasks) of Cityscapes [14] dataset. We introduce the
10-1 setting where we initially train on 10 classes (road, sidewalk, building,
wall, fence, pole, light, sign, vegetation, terrain) and add each of the 9
classes (sky, person, rider, car, truck, bus, train, motorcycle, bicycle) one
at a time. We evaluate naive fine-tuning (FT), PLOP, RCIL, SSUL, MiB, and AWT
with both SSUL and MiB, and report the mIoU results in Table 4.

We observe that while FT has very low overall mIoU, PLOP, RCIL and MiB have
improved overall performance on both settings. SSUL shows higher performance
on incremental classes but with very high forgetting on the initial classes
compared to others. On the 14-1 setting, AWT with SSUL improves the overall
mIoU over SSUL by 0.8 $p.p$ and MiB+AWT outperforms MiB by 1.5 $p.p$ with a
significant improvement of 7.3 $p.p$ on the performance of incremental classes
(15-19). On the longer 10-1 setting, SSUL+AWT increases the overall mIoU by
0.5 $p.p$ over SSUL while MiB+AWT improves over MiB by 3.0 $p.p$ with a good
margin of 7.1 $p.p$ improvement on the incremental classes (11-19). AWT
significantly improves the plasticity of the models to better learn the new
classes in both settings.

Table 4: Experimental results on Cityscapes. Improvements using AWT
underlined. Best among columns in bold. All results come from our
implementation.

| 14-1 (6 steps) | 10-1 (10 steps)
---|---|---
Method | 1-14 | 15-19 | all | 1-10 | 11-19 | all
FT | 0.0 | 10.1 | 2.5 | 0.0 | 4.8 | 2.2
PLOP [18] | 55.7 | 12.3 | 44.8 | 52.2 | 24.1 | 39.6
RCIL [54] | 55.7 | 7.1 | 43.6 | 51.0 | 17.4 | 35.9
SSUL [8] | 43.2 | 33.0 | 40.7 | 38.6 | 38.1 | 38.3
SSUL+AWT | 43.9 | 35.1 | 41.5 | 38.6 | 39.0 | 38.8
MiB [7] | 56.3 | 12.5 | 45.4 | 51.6 | 30.1 | 41.9
MiB+AWT | 55.9 | 19.8 | 46.9 | 51.2 | 37.2 | 44.9
Joint | 56.7 | 54.3 | 56.1 | 51.7 | 61.4 | 56.1

###  4.3 Ablation Study

We analyze the effectiveness of our approach with ablation experiments on
Pascal-VOC 2012 for the 15-1 setting.

Selective weight transfer: We analyze the importance of the selective weight
transfer approach in Table 5. The weight transfer proposed by MiB [7] is a
better choice compared to the case when no weights are transferred. We show
that our proposed AWT ensures the selection of the most significant channels
for new classes by performing experiments with random selection of channels
without using attributions. We observe that randomly selecting the same number
of channels (25% of total channels) and transferring their weights in the same
way as AWT performs poorly on both initial and incremental sets of classes.

Design choices: We consider the alternative ways of selecting the significant
channels and analyze them in Table 6. In AWT, we take the mean of the
attribution maps from all images of the current step and then perform max-
pooling. Here, we consider the alternative of pooling the channels first for
all the images and then take the mean of the pooled values. We also consider
using average-pooling instead of max-pooling. We experimentally show that mean
followed by max-pool is the best choice for channel selection.

Table 5: Ablation study for selective channel weights transfer on Pascal-VOC
2012.

| VOC (15-1)
---|---
Strategy | % of filters | copy | add | 0-15 | 16-20 | all
No transfer | 0 | $\times$ | $\times$ | 45.7 | 5.3 | 36.1
MiB [7] | 100 | $\checkmark$ | $\times$ | 48.1 | 15.8 | 40.4
Random | 25 | $\times$ | $\checkmark$ | 46.3 | 6.1 | 36.8
AWT | 25 | $\checkmark$ | $\times$ | 58.3 | 14.8 | 47.9
AWT | 25 | $\times$ | $\checkmark$ | 59.1 | 17.2 | 49.1

Table 6: Ablation study for different design choices using MiB [7] \+ AWT on Pascal-VOC 2012. | VOC (15-1)
---|---
MiB+AWT | 0-15 | 16-20 | all
Max-Pool $\Longrightarrow$ Mean | 55.2 | 14.5 | 45.5
Avg-Pool $\Longrightarrow$ Mean | 58.3 | 14.1 | 47.8
Mean $\Longrightarrow$ Avg-Pool | 57.6 | 14.2 | 47.2
Mean $\Longrightarrow$ Max-Pool | 59.1 | 17.2 | 49.1
Figure 4: Boxplots of the mIoU of initial, new, and all classes for 10 random
class orders. Figure 5: Visualization of predictions using MiB and MiB+AWT in
14-1 setting for Cityscapes. MiB is highly biased towards the new classes and
classifies the _bus_ as _train_ (row 1) while MiB+AWT correctly classifies the
_bus_ (row 2).

Random class ordering: The order of classes plays an important role in CISS
settings. We experiment with 10 different class orderings on VOC 15-1 setting.
We show the average performance for MiB and MiB+AWT in Figure 4. We also plot
the difference between MiB+AWT and MiB for every class order to demonstrate
the robustness of our method using random class sequences.

Computational Complexity: The time taken for the attribution module depends on
the number of new-class images. We use two Nvidia RTXA6000 GPUs for training
the models. For each image, it takes approximately 0.68 seconds to compute the
attributions. For VOC 15-1, MiB+AWT takes 10.32 hours for training while the
attribution module for all steps only takes 37 minutes (6% of the total
training time). Thus, the computational time for the attribution module is
considerably less compared to the entire training process.

For further analysis, refer to the supplementary paper.

###  4.4 Qualitative Evaluation

Figure 5 shows the predictions of MiB and MiB+AWT across time on Cityscapes
14-1 setting. MiB is biased towards the new classes and forgets the class
(_bus_) learned in step 2 and classifies the _bus_ as _train_ from step 3
onward. MiB+AWT still classifies the _bus_ correctly till step 5.

Figure 7 shows the predictions for both MiB and MiB+AWT models trained in
100-5 setting on test images of ADE20K. We show that MiB+AWT improves
predictions of classes like _fan_ (row 1), _wardrobe_ (row 2) and _chair,
chandelier_ (row 3) compared to MiB.

(a) Image

(b) MiB [7]

(c) MiB+AWT

(d) GT

Figure 7: Visualization of predictions using MiB and MiB+AWT in 100-5 setting
on test images of ADE20K.

##  5 Conclusion and Limitations

In this paper, we addressed the issue of semantic background shift during the
initialization of the new foreground classifiers at each step of CISS. We
discussed the problems with the existing initialization method, and then
proposed an attribution-based approach to identify weights that are of
interest for the new foreground classes and transfer these weights. This
selective initialization takes into account the gradual transition of classes
from background to foreground across time. Experimental results on multiple
datasets showed that our approach reduces the forgetting of old classes and
further improves the plasticity. Our weight transfer approach generalizes well
with both _things_ and _stuff_ classes and outperforms the existing CISS
methods. This work lays the foundation for attribution-aware weight
initialization that could be more generally used for incremental learning
problems involving multi-class classification

Although our method works well with most incremental settings, the strategy of
selecting multiple set of channels for multiple new classes would involve a
significant increase in computational complexity as a function of the number
of new classes at every step, especially for 10, 50 or more class increments
at a step. We believe future work can be done to address this limitation. We
hope that our attribution-based channel selection approach would be adapted
beyond semantic segmentation to other computer vision applications.

Acknowledgement. This work was partially funded by the Federal Ministry of
Education and Research Germany under the project DECODE (01IW21001) and
partially by the Spanish Government funded project
PID2019-104174GB-I00/AEI/10.13039/501100011033.

Supplementary Materials

## Introduction

In this supplementary material to our main paper Attribution-aware Weight
Transfer: A Warm-Start Initialization for Class-Incremental Semantic
Segmentation, we discuss the details of the gradient-based attribution method,
Integrated Gradients [46] used in our Attribution-aware Weight Transfer (AWT)
initialization. We further share more details of our implementation for better
reproducibility, and perform additional ablative experiments to analyze the
impact of the proposed warm-start initialization. Finally, we present the
qualitative results of AWT with MiB [7] and SSUL [8] on Pascal-VOC 2012.

##  A Attribution Method

Integrated Gradients: Consider a deep neural network as a function
$F:\mathbb{R}^{n}\rightarrow[0,1]$. Let $x\in\mathbb{R}^{n}$ be the input
image and $x^{\prime}\in\mathbb{R}^{n}$ be a baseline black image. Integrated
Gradients (IG) [46] computes and accumulates the gradients at all points along
the straight line path (in $\mathbb{R}^{n}$) from the baseline to the input.

Let $\tfrac{\partial F(x)}{\partial x_{i}}$ be the gradient of $F(x)$ along
the $i^{th}$ dimension. Then the integrated gradient along the $i^{th}$
dimension for an input $x$ and baseline $x^{\prime}$ is defined as follows:

$\small
IG_{i}(x)::=(x_{i}-x^{\prime}_{i})\times\int_{\alpha=0}^{1}\tfrac{\partial
F(x^{\prime}+\alpha\times(x-x^{\prime}))}{\partial x_{i}}~{}d\alpha$ (6)

Note that the attributions add up to the difference between $F(x)$ and
$F(x^{\prime})$.

Layer Integrated Gradients: Layer Integrated Gradients [29] is designed for
computing attributions corresponding to inputs or outputs of a specific layer
of the network. For a given layer, the size of the attribution maps is the
same as the layer’s input or output dimensions, based on whether we attribute
to the inputs or outputs of that layer. In our method, we compute the
attributions for the inputs to the final classifier layer. We obtain the
attributions corresponding to a given target class (_background_ class in our
method).

##  B Reproducibility

Datasets: We evaluate our models on Pascal-VOC 2012 [21], ADE20K [57] and
Cityscapes [14]. VOC contains 10,582 images for training and 1,449 images for
testing. ADE20K contains 20,210 and 2,000 images for training and testing
respectively. Cityscapes contains 2,975 training images and 500 testing
images.

Implementation details: We use Deeplab-v3 [11] with ResNet-101 [22] backbone
pretrained on ImageNet [16] having output stride of 16. In-place activated
batch normalization [5] is used to reduce memory requirements. Similar to [7,
18, 54], the data augmentation from [11] has been applied along with random
cropping of $512\times 512$ pixels for training and a center crop of the same
size for testing. A random horizontal flip is performed on the training set
only.

We re-implement SSUL by training for 60 epochs on ADE20K dataset. We follow
the same training settings for SSUL as proposed in [8] for VOC and ADE20K. For
Cityscapes, we trained SSUL with a learning rate of 0.01 and a batch size of
24. We train the other models of FT, PLOP, RCIL for Cityscapes with SGD and a
learning rate of $2\times 10^{-2}$ for the first step only and $10^{-3}$ for
the incremental steps.

Class order: For all the quantitative experiments, we order the classes by
increasing class id, _i.e_. the default order of the respective datasets.

For the ablation experiment using random orders on VOC 15-1, we sampled the
following 10 class sequences:
[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20]
[12, 9, 20, 7, 15, 8, 14, 16, 5, 19, 4, 1, 13, 2, 11, 17, 3, 6, 18, 10]
[13, 19, 15, 17, 9, 8, 5, 20, 4, 3, 10, 11, 18, 16, 7, 12, 14, 6, 1, 2]
[15, 3, 2, 12, 14, 18, 20, 16, 11, 1, 19, 8, 10, 7, 17, 6, 5, 13, 9, 4]
[7, 13, 5, 11, 9, 2, 15, 12, 14, 3, 20, 1, 16, 4, 18, 8, 6, 10, 19, 17]
[7, 5, 9, 1, 15, 18, 14, 3, 20, 10, 4, 19, 11, 17, 16, 12, 8, 6, 2, 13]
[12, 9, 19, 6, 4, 10, 5, 18, 14, 15, 16, 3, 8, 7, 11, 13, 2, 20, 17, 1]
[13, 10, 15, 8, 7, 19, 4, 3, 16, 12, 14, 11, 5, 20, 6, 2, 18, 9, 17, 1]
[1, 14, 9, 5, 2, 15, 8, 20, 6, 16, 18, 7, 11, 10, 19, 3, 4, 17, 12, 13]
[16, 13, 1, 11, 12, 18, 6, 14, 5, 3, 7, 9, 20, 19, 15, 4, 2, 10, 8, 17]

##  C Additional Ablation Experiments

Additional experiments are performed to analyze the effect of the
initialization and the number of training iterations per step. We show in
Table 7 that training the model with random initialization for a higher number
of iterations ($\times 2$, $\times 4$) cannot reach the performance of AWT
initialization or even the one proposed by [7]. Instead, training for more
iterations causes higher forgetting of old classes.

Furthermore, we vary the threshold $k$ to select the most significant 10%,
25%, 50% and 75% of the channels for weight transfer. Based on the results of
this experiment shown in Table 8, our final AWT uses a ratio of 25% for all
our experiments in the main paper.

Table 7: Ablation study for significance of weight transfer on Pascal-VOC
2012.

|  | VOC (15-1)
---|---|---
New Classifier Init | Iterations | 0-15 | 16-20 | all
Random |  $\times$ 1 | 45.7 | 5.3 | 36.1
Random |  $\times$ 2 | 39.7 | 6.6 | 31.8
Random |  $\times$ 4 | 29.9 | 7.5 | 24.6
Weight transfer - MiB [7] |  $\times$ 1 | 48.1 | 15.8 | 40.4
Weight transfer - AWT (Ours) |  $\times$ 1 | 59.1 | 17.2 | 49.1

Table 8: Ablation study for selection of threshold using MiB+AWT on Pascal-VOC 2012. | VOC (15-1)
---|---
Threshold for channel selection | 0-15 | 16-20 | all
Top 10% | 51.0 | 11.0 | 41.5
Top 25% | 59.1 | 17.2 | 49.1
Top 50% | 58.3 | 17.6 | 48.6
Top 75% | 56.8 | 14.9 | 46.8

To discuss the role of AWT on reducing the effect of background shift, we
analyze the performance of the newly added classes after every step of
training for VOC 15-1 and ADE20K 100-10 settings in Figure 7. We observe that
MiB+AWT better learns the new set of classes which transitions from the
previous background to current foreground. This indicates reduced effect of
the background shift with AWT across multiple steps.

(a) VOC 15-1 setting

(b) ADE20K 100-10 setting

Figure 7: Analysis of the learning of new classes at every step

##  D Additional Qualitative Evaluation

Figure 9 shows the comparison of predictions using MiB, MiB+AWT, SSUL, and
SSUL+AWT on some test samples of Pascal-VOC 2012 using models trained in the
10-1 setting. Over both the methods, AWT improves the predictions for multiple
classes like _TV_ , _car_ , _aeroplane_ , _bird_ , _chair_ , _table_ , _horse_
, _person_ , _dog_ , and many more.

(a) Image

(b) MiB [7]

(c) MiB+AWT

(d) SSUL [8]

(e) SSUL+AWT

(f) GT

Figure 9: Visualization of predictions using MiB, MiB+AWT, SSUL and SSUL+AWT
in 10-1 setting on test images of Pascal-VOC 2012.

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# Towards Embedding Dynamic Personas in Interactive Robots: Masquerading
Animated Social Kinematics (MASK)

Jeongeun Park1, Taemoon Jeong1, Hyeonseong Kim1, Taehyun Byun1, Seungyoon
Shin1,
Keunjun Choi2, Jaewoon Kwon3, Taeyoon Lee3, Matthew Pan4, and Sungjoon Choi1
1Jeongeun Park, Taemoon Jeong, Hyeonseong Kim, Taehyun Byun, Seungyoon Shin,
and Sungjoon Choi are with Department of Artificial Intelligence, Korea
University, Seoul, Republic of Korea {baro0906, taemoon-jeong, hyeonseong-kim,
taehyun-byun, 2022021568<EMAIL_ADDRESS>Choi is with the
Rainbow Robotics, Daejeon, Republic of Korea<EMAIL_ADDRESS>. This work was conducted while at NAVER LABS.3Jaewoon Kwon and Taeyoon Lee
are with the NAVER LABS, Seongnam, Republic of Korea {jaewoon.kwon, ty-
<EMAIL_ADDRESS>Matthew Pan is with the Ingenuity Labs Research Institute
and Department of Electrical and Computer Engineering, Queens University,
Kingston, Canada<EMAIL_ADDRESS>Project page: https://mask-
robot.github.io.

###### Abstract

This paper presents the design and development of an innovative interactive
robotic system to enhance audience engagement using character-like personas.
Built upon the foundations of persona-driven dialog agents, this work extends
the agent’s application to the physical realm, employing robots to provide a
more immersive and interactive experience. The proposed system, named the
Masquerading Animated Social Kinematics (MASK), leverages an anthropomorphic
robot which interacts with guests using non-verbal interactions, including
facial expressions and gestures. A behavior generation system based upon a
finite-state machine structure effectively conditions robotic behavior to
convey distinct personas The MASK framework integrates a perception engine, a
behavior selection engine, and a comprehensive action library to enable real-
time, dynamic interactions with minimal human intervention in behavior design.
Throughout the user subject studies, we examined whether the users could
recognize the intended character in film-character-based persona conditions.
We conclude by discussing the role of personas in interactive agents and the
factors to consider for creating an engaging user experience.

##  I Introduction

Interactivity in robots [1, 2, 3] has the ability to establish meaningful
connections with humans, thereby greatly improving the user experience by
creating immersive relationships. Integrating character-like attributes into
these robots makes interactions more human-like and relatable, enhancing
emotional connections and creating memorable experiences. Interactive robots
embedded with personalized personas can offer companionship for combating
loneliness or deliver standout performances in the entertainment sector.
Leveraging this potential, we focus on building an interactive robotic system
that takes a step towards adapting flexible personas that can take on the
likeness of personalities or film characters.

We hypothesize that by embedding a character template into robotic behavior,
interactive agents can convincingly embody distinct personas for users to
engage with. In particular, we aim to achieve this through a simple finite-
state machine framework, which determines robotic actions from an observed
user’s behavior, a current robot’s behavior, and a persona. We focus on non-
verbal communication, where the social cues come from gestures, gazes, or
facial expressions. For instance, if a robot demonstrated actions representing
fear when the user is greeting it, the users would be led to associate a “shy”
or “introverted” character with the robot. Furthermore, our system aims to
easily adopt diverse personas, similar to a robot putting on and switching
masks, requiring minimal human effort to design such robots.

To realize this vision of immersive persona-driven robots, we introduce the
Masquerading Animated Social Kinetics (MASK) framework, crafted to facilitate
persona-based non-verbal interactions designed to engage audiences. The system
comprises the integration of three key components [1]: a perception engine
that extracts meaningful features from users’ 3D body pose, a behavior
selection engine that selects appropriate robotic behavior within the context,
and an action library housing a collection of robot motions and facial
expressions. We introduce a persona-infuser module to automate the process of
generating behavior transitions with non-verbal cues and a given persona
leveraging large language models (LLMs) [4], which is utilized in behavior
selection engine. Notably, our framework enables minimal human intervention in
behavior selection design, enabling the swift creation of interactive robotic
agents imbued with distinct personas through textual input.

##  II Related Work

The influence of an agent’s persona on a user’s experience has been actively
explored in various domains, including robotics applications [5] and dialog
agents [6, 7, 8]. Several papers [5, 2, 3, 9] have focused on building
personality-driven agents to provide the user’s immersive experience. Van et
al. [2] focused on developing robot personalities expressed through nonverbal
cues with distinct personality traits like extroversion and introversion.
Mohammadi et al. [3] investigated whether a robot with a socially engaged
personality is more accepted than one with a competitive personality through a
case study involving a dice game. However, previous work on robotic personas
was restricted to the personality with a manual design from the experts. In
this work, we aim to adapt various types of personas with minimum human effort
by utilizing large language models (LLMs), including personality and fictional
characters, to enhance flexibility.

Prior studies [6, 7, 8] have explored the ability of language models to adapt
specific personas or personalities. Safdari et al. [7] discussed the impact of
synthetic personality in large language models (LLMs) on conversational
agents, introducing a method for assessing and validating personality tests on
LLMs and for influencing the personality in LLM-generated text. Lee et al. [8]
demonstrated distinct LLM-based behaviors that enable more authentic and
context-aware human-robot interactions by integrating non-verbal cues.
However, the approach has limitations in the latency of requesting API calls,
and the explored persona was dialog-centric rather than the character or style
of the robot.

##  III Problem Formulation

The platform used in this work is the Ambidex [10], a tendon-driven mechanism
consisting of a head, waist, and two arms. A screen which forms the head of
the robot displays the robot’s facial expressions. The robot utilizes a 1 kHz
low-level control system that computes the required torque for controlling the
specified joint positions of the robot. A Zed2i
camera111https://www.stereolabs.com placed at the top of the robot detects the
user’s body pose. The robot is encased within a transparent acrylic barrier to
ensure interaction safety. In our setup, visitors can interact with the robot
non-verbally through body language. For this work, the robot was placed in the
cafe in NAVER 1784222https://www.navercorp.com/en/naver/1784, a space open to
the public.

Figure 1: The proposed system architecture. The system is composed of a
perception engine, a behavior selection engine, and an action library, where
the action library and the behavior databases are pre-built components.

##  IV Proposed Method

This section describes technical details for automating the design process in
a persona-based interactive system. The system’s architecture, as depicted in
Figure 1, contains three main modules: a perception engine, an action library,
and a behavior selection engine. At runtime, the perception engine estimates
the 3D body poses of users seen in the environment via the Zed2i camera and
refines human pose information to discrete observations and is used to obtain
a “curiosity score”. This data is used by the behavior selection engine, which
contains the state machine defining the robot’s persona-infused behavior. In
the behavior selection engine, the robot selects the subsequent motion and the
corresponding facial expression based on the observation and the current
state. The engine’s behavior database maps observations and the robot’s
persona to specific state-action transitions. This behavior database is pre-
built and populated by a persona-infuser, which leverages large language
models [4] to construct these transitions autonomously, alleviating the need
for manual design. Finally, with the selected behavior, the robot physically
displays the motion and the facial expressions that are stored within the
action library.

###  IV-A Non-Verbal Cues

MASK employs non-verbal communication for human-robot interaction, with users
interacting with the robot through body poses ($p$). As such, we examine non-
verbal cues generated by non-verbal cues (presented in Table I) to drive the
interaction. Human nonverbal cues (observations $o(p)$) are defined as
elements that form the observation space ($\mathcal{O}$), encompassing all
possible combinations of each observation element. Robots’ reactions utilize a
discrete set of generated motions and facial expressions, which is the state
space ($\mathcal{S}$), also presented in Table I.

Human | Observation (72) | # Raised Hands $\in\\{0,1,2\\}$,
---|---|---
Distance $\in$ {close,far}
Gaze $\in$ {gaze, no gaze}
hand velocity $\in$ {waving, not waving}
approaching $\in$ {approach, static, leave}
Robot | Motion (13) | 1\. wave hand big 2. wave hand small
3\. look around 4. attract to come closer
5\. small bow 6. cry 7. push away
8\. hide away 9. read book 10. standstill
10\. yawn 11. teasing 13. cross arms
Facial Expression (12) | 1\. neutral 2. smile 3. cry
4\. angry 5. scared 6. excited
7\. reading book 8. confused 9. yes
10\. tongue out 11. yawn 12. nod head
TABLE I: Non Verbal Cues. The cues for human observation include four factors
with 72 possible combinations, while robot cues contain 13 motions and 12
facial expressions with 156 possible combinations.

###  IV-B Automated Persona Infuser via LLMs

Figure 2: Illustration of the proposed Persona Infuser, which generates the
behavior database via LLM.

In the quest to embed robots with distinct personas, we introduce a persona
infuser that constructs a behavior database. This database, $\mathcal{D}$,
acts as a blueprint for persona-driven behavior, encoding all possible
combinations of states, observations, and transitions by large language models
[4] (LLMs). The database is pre-built prior to runtime to reduce potential
latency from language model inference, ensuring smooth and responsive robot
actions. We utilize gpt-4-0613 model333https://platform.openai.com/docs/models
for our MASK system and our experiments. The input of the LLM [4] becomes the
defined state space $\mathcal{S}$, observation space $\mathcal{O}$, and the
persona $x_{p}$. Conditioned with the mentioned inputs, the LLM estimates the
state transitions
$\mathcal{D}=\\{T(s^{\prime}|s,o)\\}^{s\in\mathcal{S},o\in\mathcal{O}}$. The
behavior database is formulated as a dictionary. We design a hierarchical
structure for generating behavior transitions as illustrated in Figure 2.

#### Motion Selection

We first utilize LLMs to predict the set of motions relevant to a given
persona. This procedure helps the system filter out the states unrelated to
the persona. The input of the LLM, denoted as $p_{\theta}$, includes the
robot’s motion cues $\mathcal{M}$, the target persona $x_{p}$, and the motion
selection prompt $x_{\text{motion}}$. In the motion selection prompt, we
enable the LLM to select between $0.25M$ and $0.5M$ different motions, where
$M=13$ is the number of motion cues. The predicted set of motions $\mathbf{m}$
is defined as $\mathbf{m}\sim
p_{\theta}(\mathbf{m}|\mathcal{M},x_{p},x_{\text{motion}})$.

#### Expression Integration

In this step, we establish state $s$ that pairs each selected motion ($m$)
with a corresponding facial expression ($f$). LLM estimates the matching
facial expression conditioned on motion, iterating the estimation through the
selected motions $\mathbf{m}$. The state $s$ is defined as a pair of faces and
the motion, and is as follows: $s=(f,m)\quad\text{where}\quad f\sim
p_{\theta}(f|m,\mathcal{F},x_{p},x_{\text{states}})$, $m\in\mathbf{m}$,
$x_{\text{states}}$ is a prompt for expression integration, and $\mathcal{F}$
is a pre-defined set of facial expression cues. The set of states is denoted
as $\mathbf{s}=\\{(f,m)\\}^{m\in\mathbf{m}}$. In addition, we utilize LLMs to
define an initial state $s_{0}\sim
p_{\theta}(s_{0}|\mathbf{s},x_{p},x_{\text{init}})$, where $x_{\text{init}}$
is a prompt for defining initial state. The textual inputs of the states are
designed as the names of the motion to simplify the notations to LLM.

#### Transition Estimation

Based on the persona-based state sets $\mathbf{s}$, we estimate the state
transitions $T(s^{\prime}|s,o)$ via LLMs. To infer the robot’s next state, we
repeat our evaluation process for each combination of current states
($|\mathbf{s}|$) and observations ($|\mathcal{O}|$), resulting in numerous
iterations. We formulate the deterministic state transition, where LLMs
directly estimate the next state $s^{\prime}$ given current state $s$ and
observation $o$. The input of LLM is a state set $\mathbf{s}$ from the
previous paragraph, the target persona $x_{p}$, current observation $o$,
current state $s$, and the state transition prompt $x_{\text{transition}}$.
The transitions are defined as follows:

$\displaystyle T(s^{\prime}|s,o)=\begin{cases}1&\text{if}\quad
s^{\prime}=s^{\prime}_{\text{LLM}}\\\ 0&\text{otherwise}\end{cases}$ (1)
$\displaystyle s^{\prime}_{\text{LLM}}\sim
p_{\theta}(s^{\prime}|s,o,\mathbf{s},x_{p},x_{\text{transition}})$ (2)

where $s\in\mathbf{s}$ and $o\in\mathcal{O}$. Again, we iterate this process
to cover all observation and state spaces.

###  IV-C Details of MASK

#### Perception Engine

During the real-time deployment phase, the perception engine detects the
user’s body pose to determine the user’s state. The input of the perception
engine is an RGBD image, and the output is the feature vector and a curiosity
score (a score that describes how ‘interesting’ a person is based on observed
kinematic quantities as used in [1]) for each person. The camera stays
stationary during the interaction, and the 3D body pose of the users with
tracking is estimated via ZED SDK 4.0444https://www.stereolabs.com/docs/body-
tracking/. For each individual, we transform body skeleton data into five
observation categories: the number of hands raised above nose level, eye gaze,
distance, hand movement speed, and approaching velocity. These observations
are summarized for each person as $o(p)$ in Table I. The curiosity score
determines which user to interact with in multi-person scenarios, indicating
how interested each user is to the robot. Following previous work [1], the
curiosity score was calculated based on the observation.

#### Behavior Selection Engine

Decisions for state change unfolds at two key points: when changes occur in
the observation or when a person of interest changes. The person of interest,
which denotes a person that the robot is to interact with, is defined as a
person with a maximum curiosity in the scene. Then, the next state
$s^{\prime}$ is predicted by the behavior database
$T(s^{\prime}|s,o(p_{in}))$, with the current state $s$ and the observation
from the person of interest $o(p_{in})$. Finally, to face the user while
interacting with them, we also obtain the person’s heading direction
$\theta_{p_{in}}$ with respect to the robot frame. The information about the
next state and the heading direction is then transferred to the action library
to display the robotic action.

#### Action Library

The system is built upon a pre-built action library, where each state maps to
specific joint trajectories and facial expressions. The action library module
expresses the motion and facial expressions chosen from the behavior selection
engine. Facial expressions are directly displayed on the screen as emojis. The
robot’s motions are obtained from a robust motion retargeting pipeline that is
described in [11] where human-we recorded the demonstration of the desired
motion suitable for human-robot interaction scenarios.

##  V Experiments

We conducted a human-subjects study to investigate the capabilities of the
proposed method in representing the persona. Our main question is whether our
system enabled users to distinguish robot’s personas while considering the
effects of persona-based interactions on user experience and engagement. We
hypothesize that people can successfully infer the intended character with
character-based persona agents. We define $67\%$ accuracy as successful in
classifying the correct persona, which is four times higher than a random
guess $16.7\%$.

###  V-A Experimental Setup

During the survey, the participants were asked to interact freely with the
robot for approximately a minute. Following this interaction, participants
were asked to fill out a survey (delivered in Korean). 555 The study protocol
has been approved by the ethics review board of Korea University under
proposal KUIRB-2024-0069-01. Four different personas, based on famous
fictional movie characters, were loaded onto the robot; these included:
Ebenezer Scrooge from A Christmas Carol [12], a Minion from the Despicable Me
and Minions franchise [13], The Cowardly Lion from The Wizard of OZ [14], and
Spock from the Star Trek franchise [15]. We recruited 108 participants
(gender: M=47, F=63, ages: 20-53) to participate in this between-subjects
study where the personas were randomly assigned to participants with equal
numbers. Participants were asked to interact with the robot and answer three
questions as in the following order: S1: List keywords that describe the
robot’s personality or behavior. S2: Which of the following characters does
the robot’s behavior seem to be most similar to? (multiple choice: The
Cowardly Lion from The Wizard of OZ [14], A Minion from Despicable me [13],
Ebenezer Scrooge from A Christmas Carol [12], Captain America from The
Avengers [16], Sloth from Zootopia [17], Spock from Star Trek [15]) S3: Rate
how well the robot imitates the personality of the character you selected in
the previous question (Continuous Scale $0$ to $100$). We have also provided a
one or two-sentence summary of each character’s personality to help users
understand the character mentioned in the choices for S2.

###  V-B Human Subject Study Results

Figure 3: Classification Confusion Matrix for the character-based persona
representing the characters Cowardly Lion (LO), Minion (MM), Scrooge (SC),
Spock (SS), Sloth (SZ), and Captain America (CA), respectively.

We examine our hypothesis with classification accuracy among characters based
on the results of survey questions S2 and S3. The classification accuracy was
reported as $76.7\%$, higher than the threshold set on mentioned hypothesis 2
($67\%$). The confusion matrix for classification is shown in Figure 3, where
we have adjusted the user’s fitting score obtained from the survey question
S6. The confusion matrix $T_{ij}$ is defined as
$T_{ij}=\frac{\sum_{k}\big{(}s_{f}(k)\cdot\mathds{1}(c_{k}=c_{i})\cdot\mathds{1}(g_{k}=c_{j})\big{)}}{\sum_{k}\big{(}s_{f}(k)\cdot\mathds{1}(g_{k}=c_{j})\big{)}}$,
where $k$ is an index of a participants, $s_{f}(k)$ as a fitting score
observed from S3, $c_{k}$ as a chosen character from S2, and $g_{k}$ as a
persona shown to the participant $k$. We observed that the users could
successfully identify the Cowardly Lion from The Wizard of OZ, Minions, and
Scrooge characters. However, participants appeared to misclassify the
character of Spock, especially between Spock ($0.44$) and Scrooge ($0.39$).
Two participants who interacted with persona Spock mentioned that the robot’s
firm and blunt behavior with a neutral face leads users to feel that the robot
is “grumpy”, making it easily confused with Scrooge. Table II describes the
top-3 mentioned keywords for each character from survey question S4. These
mentioned keywords from S1 serve as an indicator that users accurately
perceive the unique characteristics of persona-driven robots, which also
supports our hypothesis.

Persona | Keywords
---|---
Lion from Wizard of OZ | Shy, Coward, Defensive
Minions | Naughty, Active, Playful
Scrooge from Christmas Carol | Angry, Aggressive, Dislike others
Spock from Startrek | Pococurante, Blunty, Cynical
TABLE II: Top 3 mentioned keywords for each persona from survey question 3.

##  VI Discussions

In our user studies, we demonstrated that participants could accurately
identify and differentiate the robot’s persona across character personas,
involving 108 participants. Key findings include discrepancies between user
expectations of warm, competent robots and their experiences, particularly
with less agreeable or expressive personas, leading to challenges in
interaction context comprehension. Additionally, a significant observation was
the tendency of participants to anthropomorphize the robot’s actions, actively
crafting narratives to understand its behavior, which suggests the importance
of user interpretation in interactive robot design. However, our approach
faced limitations such as a lack of diverse robotic behaviors in longer
interactions, repetitive actions due to a limited number of states, and
challenges in group interactions and character representation. These insights
point towards the need for a broader observation space, a more complex
behavior selection mechanism, and improved design for character portrayal to
enhance user engagement and the overall interaction quality with interactive
robots. We anticipate that this work will inspire future efforts toward
expressive and convincing persona-based robotic agents that provide immersive
experiences to people.

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