{"paper":{"title":"The First EgoCross Challenge at EgoVis 2026: Cross-Domain Egocentric Video Question Answering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Alessio Tonioni, Danda Paudel, Federico Tombari, Jinjie Zhang, Kunyu Peng, Leyi Wu, Licheng Jiao, Lingling Li, Liqiang Nie, Luc Van Gool, Takuya Murakawa, Tianwen Qian, Toru Tamaki, Weili Guan, Wenbo Wang, Wenping Ma, Xiaoling Wang, Xu Zheng, Yanjun Li, Yanwei Fu, Yifan Zhao, Yinchuan Li, Yingcong Chen, Yi Wen, Yongqin Xian, Yu Li, Yupeng Hu, Yuqian Fu, Zhenglin Du, Zhengyang Li, Zhiheng Fu, Zhiwei Chen, Zixu Li","submitted_at":"2026-08-05T08:51:20Z","abstract_excerpt":"EgoCross is a cross-domain egocentric video question answering benchmark designed to evaluate whether multimodal large language models can generalize beyond common daily-life scenarios. The first EgoCross Challenge was hosted at the Third EgoVis Workshop at CVPR 2026 and evaluated models on first-person videos from four target domains: surgery, industrial assembly, extreme sports, and animal perspectives. Each test example consists of an egocentric video clip, a question, and four candidate answers, from which the model must select the correct option. This technical report introduces the chall"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.04589","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2608.04589/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}