OpenVidVRD: Open-Vocabulary Video Visual Relation Detection via Prompt-Driven Semantic Space Alignment

• URL: http://arxiv.org/abs/2503.09416v1
• Date: Wed, 12 Mar 2025 14:13:17 GMT
• Title: OpenVidVRD: Open-Vocabulary Video Visual Relation Detection via Prompt-Driven Semantic Space Alignment
• Authors: Qi Liu, Weiying Xue, Yuxiao Wang, Zhenao Wei,
• Abstract summary: Visual language models (VLMs) help explore open-vocabulary visual relation detection, yet often overlook the connections between various visual regions and their relations.<n>We propose a novel open-vocabulary VidVRD framework, termed OpenVidVRD, which improves VidVRD tasks through prompt learning.
• Score: 5.215417164787923
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The video visual relation detection (VidVRD) task is to identify objects and their relationships in videos, which is challenging due to the dynamic content, high annotation costs, and long-tailed distribution of relations. Visual language models (VLMs) help explore open-vocabulary visual relation detection tasks, yet often overlook the connections between various visual regions and their relations. Moreover, using VLMs to directly identify visual relations in videos poses significant challenges because of the large disparity between images and videos. Therefore, we propose a novel open-vocabulary VidVRD framework, termed OpenVidVRD, which transfers VLMs' rich knowledge and powerful capabilities to improve VidVRD tasks through prompt learning. Specificall y, We use VLM to extract text representations from automatically generated region captions based on the video's regions. Next, we develop a spatiotemporal refiner module to derive object-level relationship representations in the video by integrating cross-modal spatiotemporal complementary information. Furthermore, a prompt-driven strategy to align semantic spaces is employed to harness the semantic understanding of VLMs, enhancing the overall generalization ability of OpenVidVRD. Extensive experiments conducted on the VidVRD and VidOR public datasets show that the proposed model outperforms existing methods.

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