Methods › Computer Vision › Image Retrieval Models › DELG
DELG
Introduced by Bingyi Cao et al. in Unifying Deep Local and Global Features for Image Search
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
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, which are coupled to 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, 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.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Deep Learning Based Image Retrieval in the JPEG Compressed Domain 8 Jul 2021 · 0 repositories · arXiv:2107.03648
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Unifying Deep Local and Global Features for Image Search 14 Jan 2020 · 5 repositories · arXiv:2001.05027
Tasks archive 2025-07-28
5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
| Task | Papers |
|---|---|
| Image Retrieval | 2 |
| Retrieval | 2 |
| Content-Based Image Retrieval | 1 |
| Deep Learning | 1 |
| Dimensionality Reduction | 1 |
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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