Summary of Beyond Few-shot Object Detection: a Detailed Survey, by Vishal Chudasama et al.
Beyond Few-shot Object Detection: A Detailed Survey
by Vishal Chudasama, Hiran Sarkar, Pankaj Wasnik, Vineeth N Balasubramanian, Jayateja Kalla
First submitted to arxiv on: 26 Aug 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 survey paper comprehensively reviews few-shot object detection (FSOD) research, focusing on different FSOD settings such as standard FSOD, generalized FSOD, incremental FSOD, open-set FSOD, and domain adaptive FSOD. These approaches allow models to quickly adapt to new object categories with only a few annotated samples, reducing the reliance on extensive labeled datasets. The paper compares state-of-the-art methods across different FSOD settings, analyzing them in detail based on their evaluation protocols, and provides insights into their applications, challenges, and potential future directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The survey paper looks at how computers can learn to find objects in pictures or videos quickly, even with just a few examples. This is important because it helps reduce the need for big labeled training datasets, which can be time-consuming and expensive to make. The paper reviews different ways that computers do this, such as by using a few examples to teach a model to detect new types of objects. It compares the best methods in each category and talks about how they are used and what challenges they face. |
Keywords
» Artificial intelligence » Few shot » Object detection
