Summary of Lmseg: Unleashing the Power Of Large-scale Models For Open-vocabulary Semantic Segmentation, by Huadong Tang et al.
LMSeg: Unleashing the Power of Large-Scale Models for Open-Vocabulary Semantic Segmentation
by Huadong Tang, Youpeng Zhao, Yan Huang, Min Xu, Jun Wang, Qiang Wu
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 proposes an open-vocabulary-based approach for semantic segmentation in images, leveraging multiple large-scale models to enhance the alignment between fine-grained visual features and enriched linguistic features. The method employs large language models (LLMs) to generate enriched language prompts with diverse visual attributes, including color, shape/size, and texture/material. Additionally, a learnable weighted fusion strategy combines the SAM model with the CLIP visual encoder for enhanced visual feature extraction. The proposed approach, LMSeg, achieves state-of-the-art performance across all major open-vocabulary segmentation benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand images by combining two types of AI models: language models and computer vision models. It uses a special technique to make the language models generate more detailed descriptions of what’s in an image. This helps the computer vision model understand the image even better. The result is a new way for computers to do semantic segmentation, which is like labeling different parts of an image. This approach performs better than existing methods on several benchmark tests. |
Keywords
» Artificial intelligence » Alignment » Encoder » Feature extraction » Sam » Semantic segmentation
