Corpus ID: 235458006

Learning to Predict Visual Attributes in the Wild

@article{Pham2021LearningTP,
  title={Learning to Predict Visual Attributes in the Wild},
  author={Khoi Pham and Kushal Kafle and Zhe Lin and Zhi Ding and Scott D. Cohen and Quan Tran and Abhinav Shrivastava},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.09707}
}
Visual attributes constitute a large portion of information contained in a scene. Objects can be described using a wide variety of attributes which portray their visual appearance (color, texture), geometry (shape, size, posture), and other intrinsic properties (state, action). Existing work is mostly limited to study of attribute prediction in specific domains. In this paper, we introduce a large-scale in-thewild visual attribute prediction dataset consisting of over 927K attribute annotations… Expand

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