Junmyeong Lee, Chan Hur, ChangSu Choi, Sukmin Cho, Fitsum Gaim, Eui Jun Hwang, Hoyun Song, KyungTae Lim. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Video question answering is a non-trivial task that demands joint understanding of visual contents and linguistic questions as well as temporal reasoning across video frames. Recent agent-based approaches address this by conducting multi-step reasoning with large language models (LLMs) across frame-level captions generated by vision-language models, but encounter limited temporal coherence across frames. A possible direction based on video language models (VideoLMs) directly captures temporal dynamics via video-level descriptions, but often lacks fine-grained visual cues due to a restricted number of input frames and a large dependency on input prompts. To tackle these challenges, we propose RefineVQA, a training-free framework that can easily be plugged into existing VideoLMs with iterative, LLM-guided description refinements. Specifically, the VideoLM produces an initial description, followed by LLM feedback determining whether the description suffices for the question and guiding further visual extraction, which in turn enhances the description quality while preserving temporal context. Plugged into state-of-the-art VideoLMs, ReFineVQA yields consistent gains across diverse benchmarks–NExT-QA, EgoSchema, VideoMME, ActivityNet, and StreamingBench–even with a small external LLM of 3.8B parameters.

In recent text-video retrieval, the use of additional captions from vision-language models has shown promising effects on the performance. However, existing models using additional captions often have struggled to capture the rich semantics, including temporal changes, inherent in the video. In addition, incorrect information caused by generative models can lead to inaccurate retrieval. To address these issues, we propose a new framework, Narrating the Video (NarVid), which strategically leverages the comprehensive information available from frame-level captions, the narration. The proposed NarVid exploits narration in multiple ways: 1) feature enhancement through cross-modal interactions between narration and video, 2) query-aware adaptive filtering to suppress irrelevant or incorrect information, 3) dual-modal matching score by adding query-video similarity and query-narration similarity, and 4) hard-negative loss to learn discriminative features from multiple perspectives using the two similarities from different views. Experimental results demonstrate that NarVid achieves state-of-the-art performance on various benchmark datasets.