EgoThink: Evaluating First-Person Perspective Thinking Capability of Vision-Language Models

Sijie Cheng, Zhicheng Guo, Jingwen Wu, Kechen Fang, Peng Li, Huaping Liu, Yang Liu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 14291-14302

Vision-language models (VLMs) have recently shown promising results in traditional downstream tasks. Evaluation studies have emerged to assess their abilities with the majority focusing on the third-person perspective and only a few addressing specific tasks from the first-person perspective. However the capability of VLMs to "think" from a first-person perspective a crucial attribute for advancing autonomous agents and robotics remains largely unexplored. To bridge this research gap we introduce EgoThink a novel visual question-answering benchmark that encompasses six core capabilities with twelve detailed dimensions. The benchmark is constructed using selected clips from egocentric videos with manually annotated question-answer pairs containing first-person information. To comprehensively assess VLMs we evaluate twenty-one popular VLMs on EgoThink. Moreover given the open-ended format of the answers we use GPT-4 as the automatic judge to compute single-answer grading. Experimental results indicate that although GPT-4V leads in numerous dimensions all evaluated VLMs still possess considerable potential for improvement in first-person perspective tasks. Meanwhile enlarging the number of trainable parameters has the most significant impact on model performance on EgoThink. In conclusion EgoThink serves as a valuable addition to existing evaluation benchmarks for VLMs providing an indispensable resource for future research in the realm of embodied artificial intelligence and robotics.