Summary of Mrovseg: Breaking the Resolution Curse Of Vision-language Models in Open-vocabulary Image Segmentation, by Yuanbing Zhu et al.
MROVSeg: Breaking the Resolution Curse of Vision-Language Models in Open-Vocabulary Image Segmentation
by Yuanbing Zhu, Bingke Zhu, Yingying Chen, Yunfang Niu, Ming Tang, Jinqiao Wang
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposes a novel approach to open-vocabulary image segmentation using pre-trained vision-language models (VLMs) like CLIP. The current methods are limited by operating only on downscaled images, which can lead to loss of fine details. To address this issue, the authors introduce MROVSeg, a multi-resolution training framework that uses a single pretrained CLIP backbone and sliding windows to slice high-resolution inputs into uniform patches. This allows for the preservation of spatial geometry and local-global correspondences across patches. The paper also introduces a Multi-grained Masked Attention scheme to aggregate semantics from multi-resolution features to object queries. Experimental results demonstrate the superiority of MROVSeg on open-vocabulary image segmentation benchmarks, setting new standards for this task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand images by using special models called vision-language models (VLMs). Right now, these models can only work with small versions of images, which means they lose important details. The authors created a new way to make these models work with full-sized images, preserving the tiny details that are important for good image segmentation. They also developed a special attention mechanism to help the model focus on the right parts of the image. This new approach performs better than previous methods and sets a new standard for image segmentation. |
Keywords
» Artificial intelligence » Attention » Image segmentation » Semantics
