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EgoCross: Benchmarking Multimodal Large Language Models for Cross-Domain Egocentric Video Question Answering
arXiv – CS AI|Yanjun Li, Yuqian Fu, Tianwen Qian, Qi'ao Xu, Silong Dai, Danda Pani Paudel, Luc Van Gool, Xiaoling Wang|
🤖AI Summary
Researchers introduce EgoCross, a new benchmark to evaluate multimodal AI models on egocentric video understanding across diverse domains like surgery, extreme sports, and industrial settings. The study reveals that current AI models, including specialized egocentric models, struggle with cross-domain generalization beyond common daily activities.
Key Takeaways
- →EgoCross benchmark tests AI models on 1,000 QA pairs across 798 video clips from surgery, industry, extreme sports, and animal perspective domains.
- →Existing multimodal large language models show poor performance when generalizing beyond common daily activities like cooking and cleaning.
- →The benchmark includes four key evaluation tasks: prediction, recognition, localization, and counting in egocentric video scenarios.
- →Both general-purpose and egocentric-specialized AI models demonstrated significant limitations in cross-domain video understanding.
- →Researchers conducted pilot studies using fine-tuning and reinforcement learning to explore potential model improvements.
#multimodal-ai#video-understanding#benchmark#egocentric-ai#computer-vision#domain-adaptation#mllm#cross-domain
Read Original →via arXiv – CS AI
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