Summary of Cross-modal Bidirectional Interaction Model For Referring Remote Sensing Image Segmentation, by Zhe Dong et al.
Cross-Modal Bidirectional Interaction Model for Referring Remote Sensing Image Segmentation
by Zhe Dong, Yuzhe Sun, Yanfeng Gu, Tianzhu Liu
First submitted to arxiv on: 11 Oct 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 cross-modal bidirectional interaction model (CroBIM) is a novel framework for referring remote sensing image segmentation (RRSIS). The goal is to generate pixel-level masks of target objects identified by natural language expressions. To address challenges in capturing complex geospatial relationships and varying object scales, CroBIM integrates spatial positional relationships and task-specific knowledge into linguistic features through a context-aware prompt modulation module. The model also incorporates an attention deficit compensation mechanism for feature aggregation and a mutual-interaction decoder for cross-modal feature alignment. Evaluation on the RISBench dataset, as well as two other datasets, demonstrates the superior performance of CroBIM compared to existing state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to use language to help machines understand and label remote sensing images. This is important because it can be hard for computers to figure out what’s in an image when someone describes it using complex geospatial relationships. The researchers created a special model that combines language and visual information to create a mask of the object being described. They tested this model on three big datasets and showed that it worked better than other models. |
Keywords
» Artificial intelligence » Alignment » Attention » Decoder » Image segmentation » Mask » Prompt
