AutoEval-Video: An Automatic Benchmark for Assessing Large Vision Language Models in Open-Ended Video Question Answering - researchr publication related

researchr

You are not signed in
Sign in
Sign up

Xiuyuan Chen, Yuan Lin, Yuchen Zhang, Weiran Huang 0001. AutoEval-Video: An Automatic Benchmark for Assessing Large Vision Language Models in Open-Ended Video Question Answering. In Ales Leonardis, Elisa Ricci 0001, Stefan Roth 0001, Olga Russakovsky, Torsten Sattler, Gül Varol, editors, Computer Vision - ECCV 2024 - 18th European Conference, Milan, Italy, September 29-October 4, 2024, Proceedings, Part LXVIII. Volume 15126 of Lecture Notes in Computer Science, pages 179-195, Springer, 2024. [doi]

The following publications are possibly variants of this publication:

Assessing Modality Bias in Video Question Answering Benchmarks with Multimodal Large Language ModelsJean Park, Kuk Jin Jang, Basam Alasaly, Sriharsha Mopidevi, Andrew Zolensky, Eric Eaton, Insup Lee 0001, Kevin Johnson. AAAI 2025: 19821-19829 [doi]

M-QALM: A Benchmark to Assess Clinical Reading Comprehension and Knowledge Recall in Large Language Models via Question AnsweringAnand Subramanian 0004, Viktor Schlegel, Abhinav Ramesh Kashyap, Thanh Tung Nguyen, Vijay Prakash Dwivedi, Stefan Winkler 0001. acl 2014: 4002-4042 [doi]

VQAGuider: Guiding Multimodal Large Language Models to Answer Complex Video QuestionsYuyan Chen, Jiyuan Jia, Jiaxin Lu, Siyue Li, Yu Guan, Ming Yang 0007, Qingpei Guo. acl 2025: 7821-7834 [doi]

Empowering Large Language Model for Continual Video Question Answering with Collaborative PromptingChen Cai, Zheng Wang, Jianjun Gao, Wenyang Liu, Ye Lu, Runzhong Zhang, Kim-Hui Yap. emnlp 2024: 3921-3932 [doi]

runs on WebDSL