Neural-Logic Human-Object Interaction Detection

• URL: http://arxiv.org/abs/2311.09817v1
• Date: Thu, 16 Nov 2023 11:47:53 GMT
• Title: Neural-Logic Human-Object Interaction Detection
• Authors: Liulei Li, Jianan Wei, Wenguan Wang, Yi Yang
• Abstract summary: We present L OGIC HOI, a new HOI detector that leverages neural-logic reasoning and Transformer to infer feasible interactions between entities. Specifically, we modify the self-attention mechanism in vanilla Transformer, enabling it to reason over the human, action, object> triplet and constitute novel interactions. We formulate these two properties in first-order logic and ground them into continuous space to constrain the learning process of our approach, leading to improved performance and zero-shot generalization capabilities.
• Score: 67.4993347702353
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The interaction decoder utilized in prevalent Transformer-based HOI detectors typically accepts pre-composed human-object pairs as inputs. Though achieving remarkable performance, such paradigm lacks feasibility and cannot explore novel combinations over entities during decoding. We present L OGIC HOI, a new HOI detector that leverages neural-logic reasoning and Transformer to infer feasible interactions between entities. Specifically, we modify the self-attention mechanism in vanilla Transformer, enabling it to reason over the <human, action, object> triplet and constitute novel interactions. Meanwhile, such reasoning process is guided by two crucial properties for understanding HOI: affordances (the potential actions an object can facilitate) and proxemics (the spatial relations between humans and objects). We formulate these two properties in first-order logic and ground them into continuous space to constrain the learning process of our approach, leading to improved performance and zero-shot generalization capabilities. We evaluate L OGIC HOI on V-COCO and HICO-DET under both normal and zero-shot setups, achieving significant improvements over existing methods.

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