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Weakly Supervised Visual Semantic Parsing
2020
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Scene Graph Generation (SGG) aims to extract entities, predicates and their semantic structure from images, enabling deep understanding of visual content, with many applications such as visual reasoning and image retrieval. Nevertheless, existing SGG methods require millions of manually annotated bounding boxes for training, and are computationally inefficient, as they exhaustively process all pairs of object proposals to detect predicates. In this paper, we address those two limitations by
doi:10.1109/cvpr42600.2020.00379
dblp:conf/cvpr/ZareianKC20
fatcat:h3v5phfq3ze4refvwhfntufbri