Titlebook: Computer Vision – ECCV 2020; 16th European Confer Andrea Vedaldi,Horst Bischof,Jan-Michael Frahm Conference proceedings 2020 Springer Natur

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Tensor Low-Rank Reconstruction for Semantic Segmentation,dic decomposition theory (., a high-rank tensor can be expressed as a combination of rank-1 tensors.), we design a low-rank-to-high-rank context reconstruction framework (., RecoNet). Specifically, we first introduce the tensor generation module (TGM), which generates a number of rank-1 tensors to c

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Spatiotemporal Attacks for Embodied Agents,e temporal dimension, along the spatial dimension, we adversarially perturb the physical properties (., texture and 3D shape) of the contextual objects that appeared in the most important scene views. Extensive experiments on the EQA-v1 dataset for several embodied tasks in both the white-box and bl

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Class-Wise Dynamic Graph Convolution for Semantic Segmentation,ons, pyramid pooling or self-attention mechanism. In order to avoid potential misleading contextual information aggregation in previous works, we propose a class-wise dynamic graph convolution (CDGC) module to adaptively propagate information. The graph reasoning is performed among pixels in the sam

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Character-Preserving Coherent Story Visualization,ocuses on maintaining the continuity of generated images (frames), story visualization emphasizes preserving the global consistency of characters and scenes across different story pictures, which is very challenging since story sentences only provide sparse signals for generating images. Therefore,