VideoSAVi: Self-Aligned Video Language Models without Human Supervision

• URL: http://arxiv.org/abs/2412.00624v1
• Date: Sun, 01 Dec 2024 00:33:05 GMT
• Title: VideoSAVi: Self-Aligned Video Language Models without Human Supervision
• Authors: Yogesh Kulkarni, Pooyan Fazli,
• Abstract summary: VideoSAVi is a novel self-training pipeline for vision-language models (VLMs) It generates its own training data without extensive manual annotation. VideoSAVi shows significant improvements across multiple benchmarks.
• Score: 0.6854849895338531
• License:
• Abstract: Recent advances in vision-language models (VLMs) have significantly enhanced video understanding tasks. Instruction tuning (i.e., fine-tuning models on datasets of instructions paired with desired outputs) has been key to improving model performance. However, creating diverse instruction-tuning datasets is challenging due to high annotation costs and the complexity of capturing temporal information in videos. Existing approaches often rely on large language models to generate instruction-output pairs, which can limit diversity and lead to responses that lack grounding in the video content. To address this, we propose VideoSAVi (Self-Aligned Video Language Model), a novel self-training pipeline that enables VLMs to generate their own training data without extensive manual annotation. The process involves three stages: (1) generating diverse video-specific questions, (2) producing multiple candidate answers, and (3) evaluating these responses for alignment with the video content. This self-generated data is then used for direct preference optimization (DPO), allowing the model to refine its own high-quality outputs and improve alignment with video content. Our experiments demonstrate that even smaller models (0.5B and 7B parameters) can effectively use this self-training approach, outperforming previous methods and achieving results comparable to those trained on proprietary preference data. VideoSAVi shows significant improvements across multiple benchmarks: up to 28% on multi-choice QA, 8% on zero-shot open-ended QA, and 12% on temporal reasoning benchmarks. These results demonstrate the effectiveness of our self-training approach in enhancing video understanding while reducing dependence on proprietary models.

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