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
- 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)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.emnlp-main.544.pdf