Neural Message Passing for Visual Relationship Detection

• URL: http://arxiv.org/abs/2208.04165v1
• Date: Mon, 8 Aug 2022 14:06:23 GMT
• Title: Neural Message Passing for Visual Relationship Detection
• Abstract summary: We propose a neural message passing (NMP) algorithm for visual relationship detection. We explicitly model objects and interactions by an interaction graph and then propose a message-passing-style algorithm to propagate the contextual information. Experimental results on two benchmark datasets demonstrate the superiority of our proposed method.
• Score: 45.595505481091074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual relationship detection aims to detect the interactions between objects in an image; however, this task suffers from combinatorial explosion due to the variety of objects and interactions. Since the interactions associated with the same object are dependent, we explore the dependency of interactions to reduce the search space. We explicitly model objects and interactions by an interaction graph and then propose a message-passing-style algorithm to propagate the contextual information. We thus call the proposed method neural message passing (NMP). We further integrate language priors and spatial cues to rule out unrealistic interactions and capture spatial interactions. Experimental results on two benchmark datasets demonstrate the superiority of our proposed method. Our code is available at https://github.com/PhyllisH/NMP.

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