Papers › Learning Token-based Representation for Image Retrieval
Learning Token-based Representation for Image Retrieval
Hui Wu, Min Wang, Wengang Zhou, Yang Hu, Houqiang Li
In image retrieval, deep local features learned in a data-driven manner have been demonstrated effective to improve retrieval performance. To realize efficient retrieval on large image database, some approaches quantize deep local features with a large codebook and match images with aggregated match kernel. However, the complexity of these approaches is non-trivial with large memory footprint, which limits their capability to jointly perform feature learning and aggregation. To generate compact global representations while maintaining regional matching capability, we propose a unified framework to jointly learn local feature representation and aggregation. In our framework, we first extract deep local features using CNNs. Then, we design a tokenizer module to aggregate them into a few visual tokens, each corresponding to a specific visual pattern. This helps to remove background noise, and capture more discriminative regions in the image. Next, a refinement block is introduced to enhance the visual tokens with self-attention and cross-attention. Finally, different visual tokens are concatenated to generate a compact global representation. The whole framework is trained end-to-end with image-level labels. Extensive experiments are conducted to evaluate our approach, which outperforms the state-of-the-art methods on the Revisited Oxford and Paris datasets.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Retrieval | ROxford (Hard) | Token | mAP | 66.57 | #4 of 23 | Archive leaderboard | report |
| Image Retrieval | ROxford (Medium) | Token | mAP | 82.28 | #3 of 23 | Archive leaderboard | report |
| Image Retrieval | RParis (Hard) | Token | mAP | 78.56 | #4 of 23 | Archive leaderboard | report |
| Image Retrieval | RParis (Medium) | Token | mAP | 89.34 | #3 of 23 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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