Target Adaptive Context Aggregation for Video Scene Graph Generation

• URL: http://arxiv.org/abs/2108.08121v1
• Date: Wed, 18 Aug 2021 12:46:28 GMT
• Title: Target Adaptive Context Aggregation for Video Scene Graph Generation
• Authors: Yao Teng, Limin Wang, Zhifeng Li, Gangshan Wu
• Abstract summary: This paper deals with a challenging task of video scene graph generation (VidSGG) We present a new em detect-to-track paradigm for this task by decoupling the context modeling for relation prediction from the complicated low-level entity tracking.
• Score: 36.669700084337045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper deals with a challenging task of video scene graph generation (VidSGG), which could serve as a structured video representation for high-level understanding tasks. We present a new {\em detect-to-track} paradigm for this task by decoupling the context modeling for relation prediction from the complicated low-level entity tracking. Specifically, we design an efficient method for frame-level VidSGG, termed as {\em Target Adaptive Context Aggregation Network} (TRACE), with a focus on capturing spatio-temporal context information for relation recognition. Our TRACE framework streamlines the VidSGG pipeline with a modular design, and presents two unique blocks of Hierarchical Relation Tree (HRTree) construction and Target-adaptive Context Aggregation. More specific, our HRTree first provides an adpative structure for organizing possible relation candidates efficiently, and guides context aggregation module to effectively capture spatio-temporal structure information. Then, we obtain a contextualized feature representation for each relation candidate and build a classification head to recognize its relation category. Finally, we provide a simple temporal association strategy to track TRACE detected results to yield the video-level VidSGG. We perform experiments on two VidSGG benchmarks: ImageNet-VidVRD and Action Genome, and the results demonstrate that our TRACE achieves the state-of-the-art performance. The code and models are made available at \url{https://github.com/MCG-NJU/TRACE}.

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