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About this paper
Gangming Zhao, Weifeng Ge, Yizhou Yu
GraphFPN integrates graph neural networks with feature pyramid networks by constructing image-specific superpixel hierarchies, enabling simultaneous and direct feature interactions across all scales for enhanced object detection. This approach achieved 43.7% AP on MS-COCO 2017 test-dev, showing particular gains for small objects.
Key insights
- Adapting the FPN's topological structure to intrinsic image hierarchies via superpixels and GNNs allows for more flexible and powerful multi-scale feature representation than fixed grid-based methods.
- Graph-based interactions facilitate simultaneous and direct communication between features from all scales, overcoming the limitations of sequential, adjacent-scale-only interactions in conventional FPNs.
- Local channel attention mechanisms, generalized for GNNs, enhance the discriminative power of deep features within graph structures while maintaining computational efficiency and spatial adaptivity.
Summary by alphaXiv.
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