Summary of Vision-aware Text Features in Referring Image Segmentation: From Object Understanding to Context Understanding, by Hai Nguyen-truong et al.
Vision-Aware Text Features in Referring Image Segmentation: From Object Understanding to Context Understanding
by Hai Nguyen-Truong, E-Ro Nguyen, Tuan-Anh Vu, Minh-Triet Tran, Binh-Son Hua, Sai-Kit Yeung
First submitted to arxiv on: 12 Apr 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 The proposed framework, Vision-Aware Text Features (VAT), is a novel approach to referring image segmentation that leverages human-like cognitive processes. VAT emphasizes object and context comprehension by integrating CLIP Prior, Contextual Multimodal Decoder, and Meaning Consistency Constraint. The method achieves significant performance improvements on RefCOCO, RefCOCO+, and G-Ref benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a new way to segment images based on text descriptions. It’s like how we humans understand what someone is talking about when they point to something in a picture. The model uses special features to focus on the main object and then combines that with the image and text to get the correct result. |
Keywords
» Artificial intelligence » Decoder » Image segmentation
