VidChain: Chain-of-Tasks with Metric-based Direct Preference Optimization for Dense Video Captioning

• URL: http://arxiv.org/abs/2501.06761v1
• Date: Sun, 12 Jan 2025 10:08:26 GMT
• Title: VidChain: Chain-of-Tasks with Metric-based Direct Preference Optimization for Dense Video Captioning
• Authors: Ji Soo Lee, Jongha Kim, Jeehye Na, Jinyoung Park, Hyunwoo J. Kim,
• Abstract summary: Video is complicated task describing all events within a video while also temporally localizing them. Previous VideoLLMs attempt to solve the task in a single step, failing to utilize their reasoning capability. We propose VidChain of Chain-of-Tasks and Dense-based Direct Preference (-DPO) M-DPO aligns a VideoLLM with evaluation metrics, providing fine-grained supervision to each task that is well-aligned with metrics.
• Score: 17.820597831536322
• License:
• Abstract: Despite the advancements of Video Large Language Models (VideoLLMs) in various tasks, they struggle with fine-grained temporal understanding, such as Dense Video Captioning (DVC). DVC is a complicated task of describing all events within a video while also temporally localizing them, which integrates multiple fine-grained tasks, including video segmentation, video captioning, and temporal video grounding. Previous VideoLLMs attempt to solve DVC in a single step, failing to utilize their reasoning capability. Moreover, previous training objectives for VideoLLMs do not fully reflect the evaluation metrics, therefore not providing supervision directly aligned to target tasks. To address such a problem, we propose a novel framework named VidChain comprised of Chain-of-Tasks (CoTasks) and Metric-based Direct Preference Optimization (M-DPO). CoTasks decompose a complex task into a sequence of sub-tasks, allowing VideoLLMs to leverage their reasoning capabilities more effectively. M-DPO aligns a VideoLLM with evaluation metrics, providing fine-grained supervision to each task that is well-aligned with metrics. Applied to two different VideoLLMs, VidChain consistently improves their fine-grained video understanding, thereby outperforming previous VideoLLMs on two different DVC benchmarks and also on the temporal video grounding task. Code is available at \url{https://github.com/mlvlab/VidChain}.

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