Summary of Detect An Object at Once Without Fine-tuning, by Junyu Hao et al.
Detect an Object At Once without Fine-tuning
by Junyu Hao, Jianheng Liu, Yongjia Zhao, Zuofan Chen, Qi Sun, Jinlong Chen, Jianguo Wei, Minghao Yang
First submitted to arxiv on: 4 Nov 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 method introduces a novel technical realization of recognizing unseen objects in different scenes by generating a Similarity Density Map (SDM) and using a Region Alignment Network (RAN). The SDM is created by convolving scene images with given object image patches, highlighting possible locations. The RAN regresses location and area differences between ground truths and predicted areas indicated by the highlight areas. By pre-learning from labeled datasets, the method can detect previously unknown objects without fine-tuning, outperforming state-of-the-art methods on MS COCO and PASCAL VOC datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to recognize objects in different scenes using computer vision. It’s like how humans can recognize an object they’ve seen before even if it’s in a different place. The method uses two steps: first, it creates a map of where the object might be in the scene, and then it uses that map to find the exact location of the object. This is useful because it allows computers to detect objects without needing lots of training data. |
Keywords
» Artificial intelligence » Alignment » Fine tuning
