[1906.05571] Learning Spatio-Temporal Representation with Local and Global Diffusioncontact arXivarXiv Twitter

Convolutional Neural Networks (CNN) have been regarded as a powerful class of models for visual recognition problems. Nevertheless, the convolutional filters in these networks are local operations while ignoring the large-range dependency. Such drawback becomes even worse particularly for video recognition, since video is an information-intensive media with complex temporal variations. In this paper, we present a novel framework to boost the spatio-temporal representation learning by Local and Global Diffusion (LGD). Specifically, we construct a novel neural network architecture that learns the local and global representations in parallel. The architecture is composed of LGD blocks, where each block updates local and global features by modeling the diffusions between these two representations. Diffusions effectively interact two aspects of information, i.e., localized and holistic, for more powerful way of representation learning. Furthermore, a kernelized classifier is introduced to c

1 mentions: @HirokatuKataoka
Date: 2020/02/11 14:21

Referring Tweets

@HirokatuKataoka Learning Spatio-Temporal Representation with Local and Global Diffusion, CVPR 2019. / 動画認識にて、局所(Local)大域(Global)的特徴の両者を統合、伝播させるユニット(LGD block)を提案。時空間ResNet内に組み込れ400/600カテゴリ識別にて最高精度。 t.co/QWsgLcLYia

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