Enhancing Large Multimodal Models for Long Video Sequences
Addressing the Challenge
The challenge of effectively processing and understanding long videos in large multimodal models (LMMs) arises from the high volume of visual tokens generated by vision encoders. This creates a bottleneck in handling long video sequences, necessitating innovative solutions.
Practical Solutions
An innovative approach called Long Context Transfer has been introduced to extend the context length of language model backbones, enabling them to process a significantly larger number of visual tokens. The proposed model, Long Video Assistant (LongVA), demonstrates superior performance in processing long videos by aligning the context-extended language model with visual inputs and leveraging the UniRes encoding scheme.
Value and Performance
LongVA’s performance on the Video-MME dataset sets a new benchmark by processing up to 2000 frames or over 200,000 visual tokens. It also shows superior performance in locating and retrieving visual information over long contexts, demonstrating state-of-the-art performance among 7B-scale models.
Research Validation and Feasibility
Detailed experiments validate the effectiveness of LongVA, showcasing its ability to process and understand long videos and maintain high GPU occupancy. The long context training was completed efficiently in just two days using eight A100 GPUs, highlighting the feasibility of this approach within academic budgets.
Utilizing AI for Your Business
Stay competitive and redefine your way of work by leveraging LongVA and the Impact of Long Context Transfer in Visual Processing. Identify automation opportunities, define KPIs, select an AI solution, and implement gradually to evolve your company with AI. For AI KPI management advice and continuous insights into leveraging AI, connect with us at hello@itinai.com and follow us on Telegram and Twitter.
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