Summary of Cross-domain Few-shot Object Detection Via Enhanced Open-set Object Detector, by Yuqian Fu et al.
Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
by Yuqian Fu, Yu Wang, Yixuan Pan, Lian Huai, Xingyu Qiu, Zeyu Shangguan, Tong Liu, Yanwei Fu, Luc Van Gool, Xingqun Jiang
First submitted to arxiv on: 5 Feb 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 In this paper, researchers investigate the challenges of developing accurate object detectors in novel domains with minimal labeled examples. They focus on cross-domain few-shot object detection (CD-FSOD) and explore whether transformer-based open-set detectors can generalize to this task. The authors employ various measures to understand domain gaps and establish a new benchmark for evaluating object detection methods. Most current approaches fail to generalize across domains, leading to performance declines associated with proposed measures such as style, inter-class variance, and indefinable boundaries. To address these issues, the researchers propose novel modules, including learnable instance features, instance reweighting, and domain prompters. These techniques collectively contribute to the development of the Cross-Domain Vision Transformer (CD-ViTO), which improves upon the base DE-ViT model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how to create object detectors that work well in new places with only a few examples. The authors want to know if special kinds of computer models can be used for this task. They look at different ways to measure the difference between old and new places, and they make a new test to see how well different methods do. Most current methods don’t work well across different places, so the researchers try to fix this problem by adding new parts to their model. This helps create a better object detector that can be used in new places. |
Keywords
* Artificial intelligence * Few shot * Object detection * Transformer * Vision transformer * Vit
