VideoPASTA: 7K Preference Pairs That Matter for Video-LLM Alignment.

Area of Science:

• Artificial Intelligence
• Computer Vision
• Natural Language Processing

Background:

• Video-language models (Video-LLMs) demonstrate proficiency in video comprehension but exhibit weaknesses in spatial reasoning, temporal sequencing, and cross-frame consistency.
• Existing models often require extensive pretraining or architectural changes to improve performance.

Purpose of the Study:

• To introduce VideoPASTA (Preference Alignment with Spatio-Temporal-Cross Frame Adversaries), a novel framework designed to enhance Video-LLMs.
• To improve the ability of Video-LLMs to understand complex spatial and temporal dynamics within videos.

Main Methods:

• VideoPASTA employs targeted preference optimization, training Video-LLMs to differentiate correct video representations from adversarial examples that violate spatial, temporal, or cross-frame relationships.
• The framework utilizes Direct Preference Optimization with a limited dataset of 7,020 preference pairs and 32-frame sampling.

Main Results:

• VideoPASTA significantly enhances the performance of various state-of-the-art Video-LLMs across multiple benchmarks, including LongVideoBench (+3.8%), VideoMME (+4.1%), and MVBench (+4.0%).
• The approach is model-agnostic and achieves substantial improvements without requiring human annotation or captioning.
• Models trained with VideoPASTA demonstrate improved capture of fine-grained spatial details and long-range temporal dynamics.

Conclusions:

• Targeted preference alignment, as implemented in VideoPASTA, is an effective strategy for addressing core challenges in video-language understanding.
• VideoPASTA offers a scalable, plug-and-play solution that seamlessly integrates with existing Video-LLMs, enhancing their capabilities without altering their fundamental architecture.