Papers › Classification is a Strong Baseline for Deep Metric Learning
Classification is a Strong Baseline for Deep Metric Learning
Andrew Zhai, Hao-Yu Wu
Deep metric learning aims to learn a function mapping image pixels to embedding feature vectors that model the similarity between images. Two major applications of metric learning are content-based image retrieval and face verification. For the retrieval tasks, the majority of current state-of-the-art (SOTA) approaches are triplet-based non-parametric training. For the face verification tasks, however, recent SOTA approaches have adopted classification-based parametric training. In this paper, we look into the effectiveness of classification based approaches on image retrieval datasets. We evaluate on several standard retrieval datasets such as CAR-196, CUB-200-2011, Stanford Online Product, and In-Shop datasets for image retrieval and clustering, and establish that our classification-based approach is competitive across different feature dimensions and base feature networks. We further provide insights into the performance effects of subsampling classes for scalable classification-based training, and the effects of binarization, enabling efficient storage and computation for practical applications.
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Tasks
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Retrieval | CARS196 | NormSoftmax2048 (ResNet-50) | R@1 | 89.3 | #3 of 8 | Archive leaderboard | report |
| Image Retrieval | CUB-200-2011 | NormSoftmax2048 (ResNet-50) | R@1 | 65.3 | #7 of 8 | Archive leaderboard | report |
| Image Retrieval | In-Shop | NormSoftmax2048 (ResNet-50) | R@1 | 89.4 | #5 of 7 | Archive leaderboard | report |
| Image Retrieval | SOP | NormSoftmax2048 (ResNet-50) | R@1 | 79.5 | #9 of 14 | 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.
Methods
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