Summary of A Light-weight Framework For Open-set Object Detection with Decoupled Feature Alignment in Joint Space, by Yonghao He et al.
A Light-Weight Framework for Open-Set Object Detection with Decoupled Feature Alignment in Joint Space
by Yonghao He, Hu Su, Haiyong Yu, Cong Yang, Wei Sui, Cong Wang, Song Liu
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 Decoupled Open-set Object Detection (DOSOD) framework is a lightweight and efficient solution for real-time open-set object detection in robotic systems. DOSOD builds upon the YOLO-World pipeline by integrating a vision-language model with a detector, using a Multilayer Perceptron (MLP) adaptor to transform text embeddings into a joint space. This approach avoids complex feature interactions, improving computational efficiency. Compared to the baseline YOLO-World, DOSOD achieves comparable accuracy while significantly enhancing real-time performance. The slight DOSOD-S model demonstrates a Fixed AP of 26.7%, outperforming YOLO-World-v1-S and YOLO-World-v2-S on the LVIS minival dataset, with an FPS increase of 57.1% and 29.6%, respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DOSOD is a new way to find objects in pictures. It’s faster than other methods because it doesn’t need to do as much complicated thinking. This makes it perfect for robots that need to detect things quickly, like in a factory or warehouse. The DOSOD team compared their method to others and found that it was just as good at finding objects, but way faster. |
Keywords
» Artificial intelligence » Language model » Object detection » Yolo
