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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



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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