Summary of Fastvlm: Efficient Vision Encoding For Vision Language Models, by Pavan Kumar Anasosalu Vasu et al.
FastVLM: Efficient Vision Encoding for Vision Language Models
by Pavan Kumar Anasosalu Vasu, Fartash Faghri, Chun-Liang Li, Cem Koc, Nate True, Albert Antony, Gokul Santhanam, James Gabriel, Peter Grasch, Oncel Tuzel, Hadi Pouransari
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents an innovative approach to enhance the performance of Vision Language Models (VLMs) by scaling the input image resolution. The popular ViT-based visual encoders become inefficient at high resolutions due to increased encoding latency and token counts, hindering text-rich image understanding tasks. To address this issue, the authors propose FastVLM, a model that achieves an optimized trade-off between latency, model size, and accuracy by introducing FastViTHD, a novel hybrid vision encoder designed to output fewer tokens and reduce encoding time for high-resolution images. The paper demonstrates significant improvements in time-to-first-token (TTFT) while maintaining similar performance on VLM benchmarks compared to prior works. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary VLMs are powerful tools that can understand text-rich images. To make them work better, researchers need to find ways to scale the input image resolution without slowing down the computer. The problem is that popular visual encoders like ViTs become too slow and use too many tokens when dealing with high-resolution images. The authors of this paper have come up with a solution called FastVLM, which combines two key ideas: reducing encoding latency and minimizing token count. This allows for faster processing without sacrificing accuracy. |
Keywords
» Artificial intelligence » Encoder » Token » Vit
