TraveLER: A Modular Multi-LMM Agent Framework for Video Question-Answering

Chuyi Shang, Amos You, Sanjay Subramanian, Trevor Darrell, Roei Herzig


Abstract
Recently, image-based Large Multimodal Models (LMMs) have made significant progress in video question-answering (VideoQA) using a frame-wise approach by leveraging large-scale pretraining in a zero-shot manner. Nevertheless, these models need to be capable of finding relevant information, extracting it, and answering the question simultaneously. Currently, existing methods perform all of these steps in a single pass without being able to adapt if insufficient or incorrect information is collected. To overcome this, we introduce a modular multi-LMM agent framework based on several agents with different roles, instructed by a Planner agent that updates its instructions using shared feedback from the other agents. Specifically, we propose TraveLER, a method that can create a plan to "**Trave**rse” through the video, ask questions about individual frames to "**L**ocate” and store key information, and then "**E**valuate” if there is enough information to answer the question. Finally, if there is not enough information, our method is able to "**R**eplan” based on its collected knowledge. Through extensive experiments, we find that the proposed TraveLER approach improves performance on several VideoQA benchmarks without the need to fine-tune on specific datasets. Our code is available at https://github.com/traveler-framework/TraveLER.
Anthology ID:
2024.emnlp-main.544
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9740–9766
Language:
URL:
https://aclanthology.org/2024.emnlp-main.544
DOI:
10.18653/v1/2024.emnlp-main.544
Bibkey:
Cite (ACL):
Chuyi Shang, Amos You, Sanjay Subramanian, Trevor Darrell, and Roei Herzig. 2024. TraveLER: A Modular Multi-LMM Agent Framework for Video Question-Answering. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 9740–9766, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
TraveLER: A Modular Multi-LMM Agent Framework for Video Question-Answering (Shang et al., EMNLP 2024)
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PDF:
https://aclanthology.org/2024.emnlp-main.544.pdf