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• W3204052007 abstract "Human-object interaction detection that aims at detecting <human, verb, object> triplets is critical for the holistic human-centric scene understanding. Existing approaches ignore the modeling of correlations among hierarchical human parts and objects. In this work, we introduce a Hierarchical Reasoning Network (HRNet) to capture relations among human parts at multiple scales (including the holistic human, human region, and human keypoint levels) and objects via a unified graph. In particular, HRNet first constructs one multi-level human parts graph, each level of which consists of human parts at one specific scale, objects, and the unions of human part-object pairs as nodes, and their mutual visual and spatial layout relations as intra-level reasoning. To also capture the relations across scales, we further introduce inter-level reasoning between the nodes of two consecutive levels based on the prior of human body structure. The representations of graph nodes are propagated along intra-level and inter-level reasoning in turn during reasoning. Extensive experiments demonstrate our HRNet obtains new state-of-the-art results on three challenging HICO-DET, V-COCO and HOI-A benchmarks, validating the compelling effectiveness of the proposed method." @default.
• W3204052007 created "2021-10-11" @default.
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• W3204052007 date "2021-01-01" @default.
• W3204052007 modified "2023-10-05" @default.
• W3204052007 title "Hierarchical Reasoning Network for Human-Object Interaction Detection" @default.
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• W3204052007 doi "https://doi.org/10.1109/tip.2021.3093784" @default.
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