CMA-VC: Large Vision-Language Model for Cross-Modal Alignment in Intention-Oriented Video Captioning

摘要

Traditional video captioning methods often produce generic descriptions that fail to align with specific user intentions, limiting their applicability in scenarios requiring customized information extraction. This paper proposes a novel intention-oriented controllable video captioning approach, which leverages large-scale vision-language models (InternViT and InternLM) and achieves parameter-efficient fine-tuning through Low-Rank Adaptation (LoRA). The proposed framework processes both video content and user-specified intentions via a unified cross-modal pipeline, dynamically aligning visual features with intent semantics to generate focused and contextually accurate captions. Experiments on the IntentVC dataset validate the effectiveness of the proposed method in generating intention-aligned captions, with the following performance metrics: BLEU@4 scores 44.38 on the public test set and 40.21 on the private test set; METEOR scores 63.79 and 60.07 respectively; CIDEr scores 230.33 and 208.15 respectively; ROUGE-L scores 61.75 and 57.14 respectively. Ablation studies confirm the significant effectiveness of joint LoRA adaptation on vision and language modules, as well as the sensitivity of performance to LoRA parameters. This work advances the field by enabling precise control over caption generation, enhancing the practical utility of video understanding systems in applications such as accessibility services and targeted video retrieval.