Summary of Cross-aware Early Fusion with Stage-divided Vision and Language Transformer Encoders For Referring Image Segmentation, by Yubin Cho et al.
Cross-aware Early Fusion with Stage-divided Vision and Language Transformer Encoders for Referring Image Segmentation
by Yubin Cho, Hyunwoo Yu, Suk-ju Kang
First submitted to arxiv on: 14 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 A novel architecture called Cross-aware early fusion with stage-divided Vision and Language Transformer encoders (CrossVLT) is proposed to improve the ability of cross-modal context modeling in referring segmentation. This task involves understanding complex language expressions and determining relevant regions in images with multiple objects. The paper addresses limitations in previous approaches by enabling both language and vision features to refer to each other’s information at each stage, enhancing robustness and improving cross-modal alignment. The proposed approach outperforms state-of-the-art methods on three public benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Referring segmentation is a task that involves understanding complex language expressions and determining relevant regions in images with multiple objects. A new architecture called CrossVLT proposes to improve the ability of cross-modal context modeling by enabling both language and vision features to refer to each other’s information at each stage. This approach helps to enhance robustness and improves cross-modal alignment, leading to better results. |
Keywords
* Artificial intelligence * Alignment * Transformer
