APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention
📰 ArXiv cs.AI
Accelerate long-video understanding with APB-V, a sequence-parallelism-aware approximate attention method, to improve efficiency and performance
Action Steps
- Implement APB-V to accelerate long-video inference in LMMs
- Apply sequence-parallelism-aware approximate attention to reduce computation in the prefill stage
- Configure APB-V to balance acceleration and performance degradation
- Test APB-V on various long-video datasets to evaluate its effectiveness
- Compare APB-V with existing methods to assess its advantages and limitations
Who Needs to Know This
Machine learning engineers and researchers working on large multimodal models (LMMs) for video understanding can benefit from this method to improve inference efficiency and handle longer, more complex videos
Key Insight
💡 APB-V overcomes the efficiency bottleneck of long-video inference by leveraging sequence-parallelism-aware approximate attention
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🚀 Accelerate long-video understanding with APB-V! 🤖
Key Takeaways
Accelerate long-video understanding with APB-V, a sequence-parallelism-aware approximate attention method, to improve efficiency and performance
Full Article
Title: APB-V: Accelerating Long-Video Understanding via Sequence-Parallelism-aware Approximate Attention
Abstract:
arXiv:2601.21444v2 Announce Type: replace-cross Abstract: The efficiency of long-video inference remains a critical bottleneck, mainly due to the dense computation in the prefill stage of Large Multimodal Models (LMMs). Existing methods either compress visual embeddings or apply sparse attention on a single GPU, yielding limited acceleration or degraded performance and restricting LMMs from handling longer, more complex videos. To overcome these issues, we propose APB-V, a sequence-parallel fram
Abstract:
arXiv:2601.21444v2 Announce Type: replace-cross Abstract: The efficiency of long-video inference remains a critical bottleneck, mainly due to the dense computation in the prefill stage of Large Multimodal Models (LMMs). Existing methods either compress visual embeddings or apply sparse attention on a single GPU, yielding limited acceleration or degraded performance and restricting LMMs from handling longer, more complex videos. To overcome these issues, we propose APB-V, a sequence-parallel fram
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