{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T00:14:37Z","timestamp":1758672877661,"version":"3.44.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Due to the dynamic nature in videos, it is evident that perceiving and reasoning about temporal information are the key focus of Video Question Answering (VideoQA). In recent years, several methods have explored relationship-level temporal modeling with graph-structured video representation. Unfortunately, these methods heavily rely on the question text, thus making it challenging to perceive and reason about video content that is not explicitly mentioned in the question. To address the above challenge, we propose Graph Prompts-based VideoQA (GP-VQA), which adopts a video-based graph structure for enhanced video understanding. The proposed GP-VQA contains two stages, i.e., pre-training and prompt tuning. In pre-training, we define the pretext task that requires GP-VQA to reason about the randomly masked nodes or edges in the video graph, thus prompting GP-VQA to learn the reasoning ability with video-guided information. In prompt-tuning, we organize the textual question into question graph and implement message passing from video graph to question graph, therefore inheriting the video-based reasoning ability from video graph completion to VideoQA. Extensive experiments on various datasets have demonstrated the promising performance of GP-VQA.<\/jats:p>","DOI":"10.24963\/ijcai.2025\/166","type":"proceedings-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T08:10:40Z","timestamp":1758269440000},"page":"1485-1493","source":"Crossref","is-referenced-by-count":0,"title":["Graph Prompts: Adapting Video Graph for Video Question Answering"],"prefix":"10.24963","author":[{"given":"Yiming","family":"Li","sequence":"first","affiliation":[{"name":"Nanjing University of Posts and Telecommunications"},{"name":"State Key Laboratory of Tibetan Intelligence"}]},{"given":"Xiaoshan","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese academy of science"},{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences"},{"name":"Pengcheng Laboratory"}]},{"given":"Bing-Kun","family":"Bao","sequence":"additional","affiliation":[{"name":"Nanjing University of Posts and Telecommunications"},{"name":"Pengcheng Laboratory"}]},{"given":"Changsheng","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese academy of science"},{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences"},{"name":"Pengcheng Laboratory"}]}],"member":"10584","event":{"number":"34","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"acronym":"IJCAI-2025","name":"Thirty-Fourth International Joint Conference on Artificial Intelligence {IJCAI-25}","start":{"date-parts":[[2025,8,16]]},"theme":"Artificial Intelligence","location":"Montreal, Canada","end":{"date-parts":[[2025,8,22]]}},"container-title":["Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T11:33:11Z","timestamp":1758627191000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2025\/166"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2025\/166","relation":{},"subject":[],"published":{"date-parts":[[2025,9]]}}}