Papers › Learning Super-Features for Image Retrieval
Learning Super-Features for Image Retrieval
Philippe Weinzaepfel, Thomas Lucas, Diane Larlus, Yannis Kalantidis
Methods that combine local and global features have recently shown excellent performance on multiple challenging deep image retrieval benchmarks, but their use of local features raises at least two issues. First, these local features simply boil down to the localized map activations of a neural network, and hence can be extremely redundant. Second, they are typically trained with a global loss that only acts on top of an aggregation of local features; by contrast, testing is based on local feature matching, which creates a discrepancy between training and testing. In this paper, we propose a novel architecture for deep image retrieval, based solely on mid-level features that we call Super-features. These Super-features are constructed by an iterative attention module and constitute an ordered set in which each element focuses on a localized and discriminant image pattern. For training, they require only image labels. A contrastive loss operates directly at the level of Super-features and focuses on those that match across images. A second complementary loss encourages diversity. Experiments on common landmark retrieval benchmarks validate that Super-features substantially outperform state-of-the-art methods when using the same number of features, and only require a significantly smaller memory footprint to match their performance. Code and models are available at: https://github.com/naver/FIRe.
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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) | FIRe | mAP | 61.2 | #6 of 23 | Archive leaderboard | report |
| Image Retrieval | ROxford (Medium) | FIRe | mAP | 81.8 | #4 of 23 | Archive leaderboard | report |
| Image Retrieval | RParis (Hard) | FIRe | mAP | 70.0 | #7 of 23 | Archive leaderboard | report |
| Image Retrieval | RParis (Medium) | FIRe | mAP | 85.3 | #5 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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