Summary of Mutually-aware Feature Learning For Few-shot Object Counting, by Yerim Jeon and Subeen Lee and Jihwan Kim and Jae-pil Heo
Mutually-Aware Feature Learning for Few-Shot Object Counting
by Yerim Jeon, Subeen Lee, Jihwan Kim, Jae-Pil Heo
First submitted to arxiv on: 19 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 This paper proposes a novel framework for few-shot object counting called MAFEA (Mutually-Aware FEAture learning). The existing extract-and-match approach has limitations when dealing with multiple class objects coexisting in an image. By encoding query and exemplar features mutually aware of each other, MAFEA encourages interaction throughout the pipeline, resulting in target-aware features that are robust to multi-category scenarios. Additionally, a background token is introduced to associate the target region with exemplars and decouple it from the background. The proposed framework achieves state-of-the-art performance on FSCD-LVIS and FSC-147 benchmarks, significantly reducing the target confusion problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research aims to make it easier to count objects in pictures using just a few examples. Right now, computers have trouble distinguishing between different objects when they’re all in one image. The new approach, called MAFEA, helps by making the computer features (like little patterns) aware of each other from the start. This makes it better at finding the right objects and ignoring the background. The team tested this method on two big datasets and found that it works much better than previous methods. |
Keywords
» Artificial intelligence » Few shot » Token
