Summary of Gca-sun: a Gated Context-aware Swin-unet For Exemplar-free Counting, by Yuzhe Wu et al.
GCA-SUN: A Gated Context-Aware Swin-UNet for Exemplar-Free Counting
by Yuzhe Wu, Yipeng Xu, Tianyu Xu, Jialu Zhang, Jianfeng Ren, Xudong Jiang
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces Exemplar-Free Counting, a method for counting objects of interest in images without requiring extensive annotations. The proposed approach, Gated Context-Aware Swin-UNet (GCA-SUN), directly maps input images to density maps of countable objects. This is achieved by designing a Gated Context-Aware Modulation module that suppresses irrelevant objects or background through a gate mechanism and exploits the attentive support of objects of interest using a self-similarity matrix. The gate strategy is also incorporated into the bottleneck network and decoder to highlight features most relevant to objects of interest. By exploiting attentive support among countable objects and eliminating irrelevant features, GCA-SUN focuses on counting objects without relying on predefined categories or exemplars. Experimental results on FSC-147 and CARPK datasets demonstrate that GCA-SUN outperforms state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us count things in pictures without needing to label every single object first. They created a new way called Gated Context-Aware Swin-UNet (GCA-SUN) that can take a picture and create a map of where the things we want to count are. This is important because it makes it easier for computers to help us count things in pictures, which has many uses like counting animals or objects. |
Keywords
» Artificial intelligence » Decoder » Unet
