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arXiv · 2412.15509

PolySmart @ TRECVid 2024 Video Captioning (VTT)

Jiaxin Wu·Wengyu Zhang·Xiao-Yong Wei·Qing Li
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Abstract

In this paper, we present our methods and results for the Video-To-Text (VTT) task at TRECVid 2024, exploring the capabilities of Vision-Language Models (VLMs) like LLaVA and LLaVA-NeXT-Video in generating natural language descriptions for video content. We investigate the impact of fine-tuning VLMs on VTT datasets to enhance description accuracy, contextual relevance, and linguistic consistency. Our analysis reveals that fine-tuning substantially improves the model's ability to produce more detailed and domain-aligned text, bridging the gap between generic VLM tasks and the specialized needs of VTT. Experimental results demonstrate that our fine-tuned model outperforms baseline VLMs across various evaluation metrics, underscoring the importance of domain-specific tuning for complex VTT tasks.

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Jiaxin Wu, Wengyu Zhang, Xiao-Yong Wei, Qing Li. 2024-12-20. PolySmart @ TRECVid 2024 Video Captioning (VTT). https://arxiv.org/abs/2412.15509

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