Papers › M3TR: Multi-modal Multi-label Recognition with Transformer
M3TR: Multi-modal Multi-label Recognition with Transformer
Jiawei Zhao, Yifan Zhao, Jia Li
Multi-label image recognition aims to recognize multiple objects simultaneously in one image. Recent ideas to solve this problem have focused on learning dependencies of label co-occurrences to enhance the high-level semantic representations. However, these methods usually neglect the important relations of intrinsic visual structures and face difficulties in understanding contextual relationships. To build the global scope of visual context as well as interactions between visual modality and linguistic modality, we propose the Multi-Modal Multi-label recognition TRansformers (M3TR) with the ternary relationship learning for inter-and intra-modalities. For the intra-modal relationship, we make insightful conjunction of CNNs and Transformers, which embeds visual structures into the high-level features by learning the semantic cross-attention. For constructing the interactions between the visual and linguistic modalities, we propose a linguistic cross-attention to embed the class-wise linguistic information into the visual structure learning, and finally present a linguistic guided enhancement module to enhance the representation of high-level semantics. Experimental evidence reveals that with the collaborative learning of ternary relationship, our proposed M3TR achieves new state-of-the-art on two public multi-label recognition benchmarks.
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 | M3TR(ImageNet-21K-P pretraining, resolution 448) | mAP | 87.5 | #18 of 34 | Archive leaderboard | report |
| Multi-Label Classification | PASCAL VOC 2007 | M3TR(448×448) | mAP | 96.5 | #5 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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