Papers › Disentangled Non-Local Neural Networks
Disentangled Non-Local Neural Networks
Minghao Yin, Zhuliang Yao, Yue Cao, Xiu Li, Zheng Zhang, Stephen Lin, Han Hu
The non-local block is a popular module for strengthening the context modeling ability of a regular convolutional neural network. This paper first studies the non-local block in depth, where we find that its attention computation can be split into two terms, a whitened pairwise term accounting for the relationship between two pixels and a unary term representing the saliency of every pixel. We also observe that the two terms trained alone tend to model different visual clues, e.g. the whitened pairwise term learns within-region relationships while the unary term learns salient boundaries. However, the two terms are tightly coupled in the non-local block, which hinders the learning of each. Based on these findings, we present the disentangled non-local block, where the two terms are decoupled to facilitate learning for both terms. We demonstrate the effectiveness of the decoupled design on various tasks, such as semantic segmentation on Cityscapes, ADE20K and PASCAL Context, object detection on COCO, and action recognition on Kinetics.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semantic Segmentation | ADE20K | DNL | Validation mIoU | 45.97 | #183 of 235 | Archive leaderboard | report |
| Semantic Segmentation | ADE20K val | DNL | mIoU | 45.97 | #75 of 95 | Archive leaderboard | report |
| Semantic Segmentation | Cityscapes test | DNL (coarse) | Mean IoU (class) | 83% | #22 of 105 | Archive leaderboard | report |
| Semantic Segmentation | DADA-seg | DNL (ResNet-101) | mIoU | 19.7 | #22 of 28 | Archive leaderboard | report |
| Semantic Segmentation | DensePASS | DNL (ResNet-101) | mIoU | 32.1% | #22 of 36 | Archive leaderboard | report |
| Semantic Segmentation | PASCAL Context | DNL | mIoU | 55.3 | #30 of 66 | 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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