Papers › Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel...
Combining Metric Learning and Attention Heads For Accurate and Efficient Multilabel Image Classification
Kirill Prokofiev, Vladislav Sovrasov
Multi-label image classification allows predicting a set of labels from a given image. Unlike multiclass classification, where only one label per image is assigned, such a setup is applicable for a broader range of applications. In this work we revisit two popular approaches to multilabel classification: transformer-based heads and labels relations information graph processing branches. Although transformer-based heads are considered to achieve better results than graph-based branches, we argue that with the proper training strategy, graph-based methods can demonstrate just a small accuracy drop, while spending less computational resources on inference. In our training strategy, instead of Asymmetric Loss (ASL), which is the de-facto standard for multilabel classification, we introduce its metric learning modification. In each binary classification sub-problem it operates with L₂ normalized feature vectors coming from a backbone and enforces angles between the normalized representations of positive and negative samples to be as large as possible. This results in providing a better discrimination ability, than binary cross entropy loss does on unnormalized features. With the proposed loss and training strategy, we obtain SOTA results among single modality methods on widespread multilabel classification benchmarks such as MS-COCO, PASCAL-VOC, NUS-Wide and Visual Genome 500. Source code of our method is available as a part of the OpenVINO Training Extensions https://github.com/openvinotoolkit/deep-object-reid/tree/multilabel
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multi-Label Classification | MS-COCO | MLD-TResNet-L-AAM[640x640] | mAP | 91.30 | #6 of 34 | Archive leaderboard | report |
| Multi-Label Classification | NUS-WIDE | MLD-TResNet-L-AAM[448x448] | MAP | 68.30 | #2 of 9 | Archive leaderboard | report |
| Multi-Label Classification | PASCAL VOC 2007 | MLD-TResNetL-AAM (resolution 448, pretrain from OpenImages V6) | mAP | 96.70 | #4 of 17 | 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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