PPLLaVA: Varied Video Sequence Understanding With Prompt Guidance

Understand

In the past year, video-based large language models (Video LLMs) have achieved impressive progress, particularly in their ability to process long videos through extremely extended context lengths.

• However, this comes at the cost of significantly increased computational overhead due to the massive number of visual tokens, making efficiency a major bottleneck.
• In this paper, we identify the root of this inefficiency as the high redundancy in video content.
• To address this, we propose a novel pooling strategy that enables aggressive token compression while retaining instruction-relevant visual semantics.