PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance

Ruyang Liu, Chen Li, Haoran Tang, Yixiao Ge, Ying Shan, Haibo Lu, Jiankun Yang

Keywords: Video LLM, Prompt-guided Pooling, PPLLaVA

TL;DR: visual token pooling with instruction guidance, 1/8 throughput, better video&image performance.

Abstract: The past year has witnessed the significant advancement of video-based large language models. However, the challenge of developing a unified model for both short and long video understanding remains unresolved. Most existing video LLMs cannot handle hour-long videos, while methods custom for long videos tend to be ineffective for shorter videos and images. In this paper, we identify the key issue as the redundant content in videos. To address this, we propose a novel pooling strategy that simultaneously achieves token compression and instruction-aware visual feature aggregation. Our model is termed Prompt-guided Pooling LLaVA, or PPLLaVA for short. Specifically, PPLLaVA consists of three core components: the CLIP-based visual-prompt alignment that extracts visual information relevant to the user's instructions, the prompt-guided pooling that compresses the visual sequence to arbitrary scales using convolution-style pooling, and the clip context extension designed for lengthy prompt common in visual dialogue. Moreover, our codebase also integrates the most advanced video Direct Preference Optimization (DPO) and visual interleave training. Extensive experiments have validated the performance of our model. With superior throughput, PPLLaVA achieves better results on image benchmarks as a video LLM, while achieving state-of-the-art performance across various video benchmarks, excelling in tasks ranging from caption generation to multiple-choice questions, and handling video lengths from seconds to hours. The codes are promised to be made public.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 1865