ViLAMP - A visual language model jointly developed by Ant Financial and Renmin University
ViLAMP (VIdeo-LAnguage Model with Mixed Precision) is a visual language model jointly developed by Ant Group and Renmin University of China, specifically designed for efficiently processing long video content. Based on a mixed precision strategy, it...
What is ViLAMP?
ViLAMP (VIdeo-LAnguage Model with Mixed Precision) is a visual language model jointly developed by Ant Group and Renmin University of China, specifically designed for efficiently processing long video content. Based on a mixed precision strategy, it maintains high-precision analysis of keyframes in videos, significantly reducing computational costs and improving processing efficiency. ViLAMP has performed exceptionally well in multiple video understanding benchmarks, demonstrating significant advantages in long video understanding tasks. ViLAMP can process up to 10,000 frames (approximately 3 hours) of video on a single A100 GPU while maintaining stable understanding accuracy, providing a new solution for long video analysis.
ViLAMP's main functions
- Long video comprehensionSupports processing videos up to several hours long.
- Key information extractionIt accurately extracts key information from videos while compressing redundant information.
- High-efficiency computingProcessing up to 10,000 frames (approximately 3 hours) of video on a single A100 GPU significantly reduces memory and computing costs while improving processing efficiency.
- MultitaskingIt supports various video understanding tasks, such as video content question answering, action recognition, and scene understanding.
ViLAMP's technical principles
- Differential keyframe selectionThe algorithm uses a greedy approach to select keyframes that are highly relevant to the user query and exhibit temporal diversity. This ensures that the selected keyframes capture important information while avoiding redundancy.
- Differential feature mergingNon-key frames are compressed, and multiple patches for each non-key frame are merged into a single token. Based on differential weighted pooling, patches relevant to the user query and unique are given higher weights, while patches that overlap with key frames are given lower weights. This significantly reduces computational load while preserving key information.
ViLAMP project address
- GitHub repository:https://github.com/steven-ccq/ViLAMP
- arXiv technical paper:https://arxiv.org/pdf/2504.02438
ViLAMP application scenarios
- Online EducationQuickly extract key content from educational videos and generate summaries or answers to student questions.
- Video surveillanceIt analyzes surveillance video in real time, detects abnormal events, and issues timely alarms.
- Live Stream Analysis: Process live stream content in real time, extract highlights, or answer audience questions.
- Film and television productionIt helps editors and directors select materials, extract key scenes, and improve production efficiency.
- Intelligent Customer ServiceAutomatically answer user questions about video content to improve user experience.