ResAdapt: Adaptive Resolution for Efficient Multimodal Reasoning
📰 ArXiv cs.AI
ResAdapt is an adaptive resolution framework for efficient multimodal reasoning in large language models
Action Steps
- Identify the bottleneck in multimodal large language models as the volume of pixels the encoder receives
- Develop an input-side adaptation framework like ResAdapt to learn hierarchical representations
- Apply ResAdapt to adaptively adjust the resolution of input data for efficient multimodal reasoning
- Evaluate the performance of ResAdapt in terms of visual understanding and computational efficiency
Who Needs to Know This
AI engineers and researchers working on multimodal large language models can benefit from ResAdapt to improve visual understanding while reducing computational costs. This can be particularly useful for teams developing applications that require joint processing of visual and textual data
Key Insight
💡 Adaptive resolution can help reduce the computational costs of multimodal large language models while improving visual understanding
Share This
🤖 ResAdapt: adaptive resolution for efficient multimodal reasoning in large language models 📸💻
Key Takeaways
ResAdapt is an adaptive resolution framework for efficient multimodal reasoning in large language models
Full Article
Title: ResAdapt: Adaptive Resolution for Efficient Multimodal Reasoning
Abstract:
arXiv:2603.28610v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) achieve stronger visual understanding by scaling input fidelity, yet the resulting visual token growth makes jointly sustaining high spatial resolution and long temporal context prohibitive. We argue that the bottleneck lies not in how post-encoding representations are compressed but in the volume of pixels the encoder receives, and address it with ResAdapt, an Input-side adaptation framework that learns h
Abstract:
arXiv:2603.28610v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) achieve stronger visual understanding by scaling input fidelity, yet the resulting visual token growth makes jointly sustaining high spatial resolution and long temporal context prohibitive. We argue that the bottleneck lies not in how post-encoding representations are compressed but in the volume of pixels the encoder receives, and address it with ResAdapt, an Input-side adaptation framework that learns h
DeepCamp AI