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ACM Multimedia 2025Demos and Videos

CReLeRI: Explainable, Concept-centric, Representation, Learning, Reasoning, and Interaction Video Analysis System

Michael Francis Perez, Yichi Yang, Yuheng Zha, Enze Ma, Danish Nisar Ahmed Tamboli, Haodi Ma, Reza Shahriari, Vyom Pathak, Dzmitry Kasinets, Rohith Venkatakrishnan, Daisy Zhe Wang, Jaime Ruiz, Eric D. Ragan, Zhiting Hu, Eric P. Xing, Jun-Yan Zhu

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.1145/3746027.3754479 ↗

摘要

Existing video analysis models often lack explainability, perform poorly on long videos, and frequently hallucinate. Commercial solutions are closed-source and costly. We introduce CReLeRI, an open-source system for action detection in untrimmed videos. CReLeRI segments videos using scene and action transitions, detects actions and their arguments and grounds them in 3D space to improve interpretability and reduce hallucinations. The system promotes transparency and trust in AI-driven analysis of complex, real-world videos. A demonstration video is also available.