Papers › Causality Compensated Attention for Contextual Biased Visual Recognition
Causality Compensated Attention for Contextual Biased Visual Recognition
Ruyang Liu, Jingjia Huang, Ge Li, Thomas H. Li
Visual attention does not always capture the essential object representation desired for robust predictions. Attention modules tend to underline not only the target object but also the common co-occurring context that the module thinks helpful in the training. The problem is rooted in the confounding effect of the context leading to incorrect causalities between objects and predictions, which is further exacerbated by visual attention. In this paper, to learn causal object features robust for contextual bias, we propose a novel attention module named Interventional Dual Attention (IDA) for visual recognition. Specifically, IDA adopts two attention layers with multiple sampling intervention, which compensates the attention against the confounder context. Note that our method is model-agnostic and thus can be implemented on various backbones. Extensive experiments show our model obtains significant improvements in classification and detection with lower computation. In particular, we achieve the state-of-the-art results in multi-label classification on MS-COCO and PASCAL-VOC.
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 | IDA-SwinL | mAP | 90.3 | #10 of 34 | Archive leaderboard | report |
| Multi-Label Classification | MS-COCO | IDA-R101 | mAP | 86.3 | #21 of 34 | Archive leaderboard | report |
| Multi-Label Image Classification | MSCOCO | IDA-R101(H) | mAP | 84.8 | #1 of 4 | Archive leaderboard | report |
| Multi-Label Image Classification | MSCOCO | IDA-R101(H) 576 | mAP | 86.3 | #2 of 4 | Archive leaderboard | report |
| Multi-Label Image Classification | MSCOCO | IDA-SwinL(H) 384 | mAP | 90.3 | #3 of 4 | 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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