SynPO: Synergizing Descriptiveness and Preference Optimization for Video Detailed Captioning (2506.00835v1)

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-LLMs 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 LLMs, achieving an optimal balance between cost and data quality. Second, 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 optimization, explicitly preserves the model's language capability to avoid deviation of the optimization objective, and improves training efficiency by eliminating the need for the reference model. We extensively evaluate SynPO not only on video captioning benchmarks (e.g., VDC, VDD, VATEX) but also across well-established NLP tasks, including general language understanding and preference evaluation, using diverse pretrained models. Results demonstrate that SynPO consistently outperforms DPO variants while achieving 20\% improvement in training efficiency. Code is available at https://github.com/longmalongma/SynPO

Synergizing Descriptiveness and Preference Optimization for Video Detailed Captioning

The paper "SynPO: Synergizing Descriptiveness and Preference Optimization for Video Detailed Captioning" investigates the enhancement of fine-grained video captioning through preference learning, specifically focusing on overcoming the limitations inherent in Direct Preference Optimization (DPO). This paper introduces innovative methodologies for improving vision-LLMs (VLMs), aiming to generate detailed and temporally coherent video captions.

Methodological Approach

The authors propose a two-fold advancement. First, a novel pipeline is designed for constructing preference pairs, leveraging the intrinsic properties of VLMs alongside partial assistance from LLMs. This method efficiently balances between cost and data quality, optimizing the dataset for training models. Second, they introduce Synergistic Preference Optimization (SynPO), enhancing DPO by addressing the dominance of negative preferences during optimization and maintaining language capability, thus preventing deviation in the optimization objective.

Experimental Analysis

Extensive evaluations are conducted using benchmarks such as VDC, VDD, and VATEX, alongside general NLP tasks including language understanding and preference evaluation. SynPO demonstrates superior performance compared to standard DPO and its variants, showing a 20% improvement in training efficiency. This advancement results from eliminating the reference model, thereby streamlining the training process without compromising on data quality.

Key Findings

Quantitative results illustrate the capabilities of SynPO in improving video captioning and general language processing tasks. SynPO systematically outperformed DPO variants, indicating the effectiveness of synergizing descriptiveness with preference optimization. The introduction of preference pairs generated via the advanced pipeline also contributes significantly to model performance.

Implications and Speculation on Future Developments

Practically, SynPO enhances the efficiency and output quality of video captioning systems, critical for applications in automated video analysis and documentation. Theoretically, this paper provides insights into blending preference learning with model optimization, opening avenues for further exploration into hybrid optimization strategies in AI.

Looking forward, this research suggests potential developments in fine-tuning methodologies that could extend to other multimodal tasks. As AI systems evolve, integrating synergistic learning into broader aspects of machine understanding, including complex temporal dynamics in video content, could greatly enhance application versatility and performance.

In conclusion, the paper presents crucial advancements in video detailed captioning, highlighting the impact of combining preference learning with model optimization. These methodologies promise substantial contributions to the field of AI, particularly in enhancing multimodal interaction capabilities.