VC R-CNN
Visual Commonsense Region-based Convolutional Neural Network
**VC R-CNN** is an unsupervised feature representation learning method, which uses Region-based Convolutional Neural Network ([R-CNN](https://paperswithcode.com/method/r-cnn)) as the visual backbone, and the causal intervention as the training objective. Given a set of detected object regions in an image (e.g., using [Faster R-CNN](https://paperswithcode.com/method/faster-r-cnn)), like any other unsupervised feature learning methods (e.g., word2vec), the proxy training objective of VC R-CNN is to predict the contextual objects of a region. However, they are fundamentally different: the prediction of VC R-CNN is by using causal intervention: P(Y|do(X)), while others are by using the conventional likelihood: P(Y|X). This is also the core reason why VC R-CNN can learn "sense-making" knowledge like chair can be sat -- while not just "common" co-occurrences such as the chair is likely to exist if table is observed.
Visual Commonsense R-CNN
https://paperswithcode.com/paper/visual-commonsense-r-cnn
Visual Commonsense R-CNN