Distillation of Human-Object Interaction Contexts for Action Recognition

• URL: http://arxiv.org/abs/2112.09448v1
• Date: Fri, 17 Dec 2021 11:39:44 GMT
• Title: Distillation of Human-Object Interaction Contexts for Action Recognition
• Authors: Muna Almushyti and Frederick W. Li
• Abstract summary: We learn human-object relationships by exploiting the interaction of their local and global contexts. We propose the Global-Local Interaction Distillation Network (GLIDN), learning human and object interactions through space and time. GLIDN encodes humans and objects into graph nodes and learns local and global relations via graph attention network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modeling spatial-temporal relations is imperative for recognizing human actions, especially when a human is interacting with objects, while multiple objects appear around the human differently over time. Most existing action recognition models focus on learning overall visual cues of a scene but disregard informative fine-grained features, which can be captured by learning human-object relationships and interactions. In this paper, we learn human-object relationships by exploiting the interaction of their local and global contexts. We hence propose the Global-Local Interaction Distillation Network (GLIDN), learning human and object interactions through space and time via knowledge distillation for fine-grained scene understanding. GLIDN encodes humans and objects into graph nodes and learns local and global relations via graph attention network. The local context graphs learn the relation between humans and objects at a frame level by capturing their co-occurrence at a specific time step. The global relation graph is constructed based on the video-level of human and object interactions, identifying their long-term relations throughout a video sequence. More importantly, we investigate how knowledge from these graphs can be distilled to their counterparts for improving human-object interaction (HOI) recognition. We evaluate our model by conducting comprehensive experiments on two datasets including Charades and CAD-120 datasets. We have achieved better results than the baselines and counterpart approaches.

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