The First EgoCross Challenge at EgoVis 2026: Cross-Domain Egocentric Video Question Answering

• URL: http://arxiv.org/abs/2608.04589v1
• Date: Wed, 05 Aug 2026 08:51:20 GMT
• Title: The First EgoCross Challenge at EgoVis 2026: Cross-Domain Egocentric Video Question Answering
• Abstract summary: EgoCross is a cross-domain egocentric video question answering benchmark.<n>It is designed to evaluate whether multimodal large language models can generalize beyond common daily-life scenarios.<n>The first EgoCross Challenge was hosted at the Third EgoVis Workshop at CVPR 2026.
• Score: 196.78334982965507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 challenge task, benchmark resources, and two official Codabench tracks. The Source-Limited Track restricts participants to the official baseline model and a small support set, whereas the Open-Source Track permits broader choices of models and training data under rules that prohibit the manual construction of target-domain training data. In total, the challenge received more than 1,500 submissions from over 130 participants, with 19 teams participating in the Open-Source Track and 38 teams in the Source-Limited Track. We further present the official leaderboard results and summarize the winning solutions from both tracks. We hope that this report will serve as a useful technical reference for advancing cross-domain egocentric video understanding. All resources, including the challenge data, baseline implementation, and code released by the winning teams, are made publicly available.

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