DELG
DELG
**DELG** is a convolutional neural network for image retrieval that combines generalized mean pooling for global features and attentive selection for local features. The entire network can be learned end-to-end by carefully balancing the gradient flow between two heads – requiring only image-level labels. This allows for efficient inference by extracting an image’s global feature, detected keypoints and local descriptors within a single model.

The model is enabled by leveraging hierarchical image representations that arise in [CNNs](https://paperswithcode.com/methods/category/convolutional-neural-networks), which are coupled to [generalized mean pooling](https://paperswithcode.com/method/generalized-mean-pooling) and attentive local feature detection. Secondly, a convolutional autoencoder module is adopted that can successfully learn low-dimensional local descriptors. This can be readily integrated into the unified model, and avoids the need of post-processing learning steps, such as [PCA](https://paperswithcode.com/method/pca), that are commonly used. Finally, a procedure is used that enables end-to-end training of the proposed model using only image-level supervision. This requires carefully controlling the gradient flow between the global and local network heads during backpropagation, to avoid disrupting the desired representations.
Unifying Deep Local and Global Features for Image Search
https://paperswithcode.com/paper/unifying-deep-local-and-global-features-for
Unifying Deep Local and Global Features for Image Search