Papers › Compact Global Descriptor for Neural Networks
Compact Global Descriptor for Neural Networks
Xiangyu He, Ke Cheng, Qiang Chen, Qinghao Hu, Peisong Wang, Jian Cheng
Long-range dependencies modeling, widely used in capturing spatiotemporal correlation, has shown to be effective in CNN dominated computer vision tasks. Yet neither stacks of convolutional operations to enlarge receptive fields nor recent nonlocal modules is computationally efficient. In this paper, we present a generic family of lightweight global descriptors for modeling the interactions between positions across different dimensions (e.g., channels, frames). This descriptor enables subsequent convolutions to access the informative global features with negligible computational complexity and parameters. Benchmark experiments show that the proposed method can complete state-of-the-art long-range mechanisms with a significant reduction in extra computing cost. Code available at https://github.com/HolmesShuan/Compact-Global-Descriptor.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | MobileNet-224 (CGD) | GFLOPs | 1.198 | #995 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MobileNet-224 (CGD) | Number of params | 4.26M | #995 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | MobileNet-224 (CGD) | Top 1 Accuracy | 72.56% | #995 of 1060 | Archive leaderboard | report |
| Object Detection | COCO test-dev | Faster R-CNN + FPN + CGD | box mAP | 37.9 | #215 of 225 | 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
Introduced by this paper: Compact Global Descriptor
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