SynPO: Synergizing Descriptiveness and Preference Optimization for Video Detailed Captioning.

Abstract

Fine-grained video captioning aims to generate detailed, temporally coherent descriptions of video content. However, existing methods struggle to capture subtle video dynamics and rich detailed information. In this paper, we leverage preference learning to enhance the performance of vision-language models (VLM) in fine-grained video captioning, while mitigating several limitations inherent to Direct Preference Optimization (DPO). First, we propose a pipeline for constructing preference pairs that leverages the intrinsic properties of VLMs along with partial assistance from large language models, achieving an balance between cost and data quality. Then, we propose Synergistic Preference Optimization (SynPO), a novel optimization method offering significant advantages over DPO and its variants. SynPO prevents negative pReferences from dominating the training, explicitly preserves the model's language capability to avoid deviation of the optimization objective, thus obtains high-quality captions and improves training efficiency by eliminating the need for the reference model. We extensively evaluate our proposed data construction pipeline across three models: AuroraCap, LLaVA1.6-7B-Video and InterVL2-8B. Results demonstrate that our method improve performance in fine-grained video captioning significantly and consistenly. Source code is available at https://github.com/longmalongma/SynPO.