Structure Inference Machines: Recurrent Neural Networks for Analyzing Relations in Group Activity Recognition

@article{Deng2016StructureIM,
  title={Structure Inference Machines: Recurrent Neural Networks for Analyzing Relations in Group Activity Recognition},
  author={Zhiwei Deng and Arash Vahdat and Hexiang Hu and Greg Mori},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={4772-4781}
}
Rich semantic relations are important in a variety of visual recognition problems. As a concrete example, group activity recognition involves the interactions and relative spatial relations of a set of people in a scene. State of the art recognition methods center on deep learning approaches for training highly effective, complex classifiers for interpreting images. However, bridging the relatively low-level concepts output by these methods to interpret higher-level compositional scenes remains… CONTINUE READING
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