Summary of Spider: a Unified Framework For Context-dependent Concept Segmentation, by Xiaoqi Zhao et al.
Spider: A Unified Framework for Context-dependent Concept Segmentation
by Xiaoqi Zhao, Youwei Pang, Wei Ji, Baicheng Sheng, Jiaming Zuo, Lihe Zhang, Huchuan Lu
First submitted to arxiv on: 2 May 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 The proposed unified model, Spider, is designed to understand and distinguish diverse context-dependent concepts with a single set of parameters. Unlike existing methods that require separate models for different domains, Spider can be trained once and then fine-tuned for new tasks with minimal degradation in performance. This approach enables continuous learning and addresses the limitations of current specialized models in cross-domain generalization. The model outperforms state-of-the-art methods in 8 context-dependent segmentation tasks, including natural scenes and medical lesions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Spider is a new AI model that can understand different things depending on the background or situation. This is important because many real-world problems require understanding how objects relate to their surroundings. Existing models are specialized for specific areas, like nature or medicine, but Spider can learn once and then apply its learning to other areas with minimal loss of performance. The model performs better than others in 8 tasks that involve recognizing objects in different contexts. |
Keywords
» Artificial intelligence » Domain generalization
