Segment objects using graph neural networks with second-order similarity with PVG
Segment objects using graph neural networks with second-order similarity with PVG
PVG: Progressive Vision Graph for Vision Recognition
arXiv paper abstract https://arxiv.org/abs/2308.00574
arXiv PDF paper https://arxiv.org/pdf/2308.00574.pdf
Convolution-based and Transformer-based vision backbone networks process images into the grid or sequence structures, respectively, which are inflexible for capturing irregular objects.
Though Vision GNN (ViG) adopts graph-level features ... has ... issues, such as inaccurate neighbor node selection, expensive node information aggregation calculation, and over-smoothing in the deep layers ... propose a Progressive Vision Graph (PVG) architecture for vision recognition task.
... PVG contains three main components: 1) Progressively Separated Graph Construction (PSGC) to introduce second-order similarity by gradually increasing the channel of the global graph branch and decreasing the channel of local branch as the layer deepens;
2) Neighbor nodes information aggregation and update module by using Max pooling and mathematical Expectation (MaxE) to aggregate rich neighbor information;
3) Graph error Linear Unit (GraphLU) to enhance low-value information in a relaxed form to reduce the compression of image detail information for alleviating the over-smoothing.
... demonstrate the superiority of PVG over state-of-the-art methods ...
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