Summary of Vlsm-adapter: Finetuning Vision-language Segmentation Efficiently with Lightweight Blocks, by Manish Dhakal et al.
VLSM-Adapter: Finetuning Vision-Language Segmentation Efficiently with Lightweight Blocks
by Manish Dhakal, Rabin Adhikari, Safal Thapaliya, Bishesh Khanal
First submitted to arxiv on: 10 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 The paper introduces a novel adapter, VLSM-Adapter, which can fine-tune pretrained vision-language segmentation models using transformer encoders. By keeping the original model frozen and only training adapters during fine-tuning, this approach significantly reduces computing resources required. The authors demonstrate that with just 3 million trainable parameters, VLSM-Adapter outperforms state-of-the-art methods in widely used CLIP-based segmentation models, comparable to upper bounds achieved through end-to-end fine-tuning. This innovation has potential applications in medical imaging, where medical professionals can leverage VLSMs for tasks like delineating target structures of interest. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to improve image segmentation using text prompts. It’s called the VLSM-Adapter and helps reduce the need for lots of computer power. The authors tested it on some popular image segmentation models and found that it works just as well as more complex methods, but is much faster and uses fewer resources. |
Keywords
» Artificial intelligence » Fine tuning » Image segmentation » Transformer
