6-5 基于图的视觉分类模型的可解释性.pdf

1、1Explainability of Graph-based Image ClassificationJindong Gu University of MunichContent:1.Motivation2.Graph Neural Network3.Graph Capsule Network4.A Graph-based View of Vision Transformer 5.Conclusion21.Motivation3He,Kaiming,Xiangyu Zhang,Shaoqing Ren,and Jian Sun.Deep residual learning for image

2、recognition.InProceedings of the IEEE conference on computer vision and pattern recognition,pp.770-778.2016.Image Classification with Convolutional Neural Networks(ResNet)1.Motivation4He,Kaiming,Xiangyu Zhang,Shaoqing Ren,and Jian Sun.Deep residual learning for image recognition.InProceedings of the

3、 IEEE conference on computer vision and pattern recognition,pp.770-778.2016.Image Classification with Convolutional Neural Networks(ResNet)Convolutional Neural Networks:Advantages:powerful to capture correlations and abstract conceptions out of image pixels.Disadvantage:difficult to capture pairwise

4、 relation,global context and attribute feature.1.Motivation5He,Kaiming,Xiangyu Zhang,Shaoqing Ren,and Jian Sun.Deep residual learning for image recognition.InProceedings of the IEEE conference on computer vision and pattern recognition,pp.770-778.2016.Image Classification with Convolutional Neural N

5、etworks(ResNet)Convolutional Neural Networks:Advantages:powerful to capture correlations and abstract conceptions out of image pixels.Disadvantage:difficult to capture pairwise relation,global context and attribute feature.2.Graph Neural Networks6Kipf,T.N.and Welling,M.,2016.Semi-supervised classifi

6、cation with graph convolutional networks.arXiv preprint arXiv:1609.02907.2.Graph Neural Networks7Monti,Federico,et al.Geometric deep learning on graphs and manifolds using mixture model cnns.Proceedings of the IEEE conference on computer vision and pattern recognition.2017.Dwivedi,Vijay Prakash,et a