Summary of Cgcod: Class-guided Camouflaged Object Detection, by Chenxi Zhang et al.
CGCOD: Class-Guided Camouflaged Object Detection
by Chenxi Zhang, Qing Zhang, Jiayun Wu, Youwei Pang
First submitted to arxiv on: 25 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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 The Camouflaged Object Detection (COD) task aims to accurately identify objects that blend seamlessly into their surroundings, posing significant challenges due to low contrast with the background, diverse textures, and subtle appearance variations. Existing methods rely on visual features, which are insufficient for handling the variability and intricacy of camouflaged objects, leading to unstable object perception and ambiguous segmentation results. To address these limitations, a novel task is introduced, class-guided camouflaged object detection (CGCOD), which incorporates object-specific class knowledge to enhance detection robustness and accuracy. A new dataset, CamoClass, comprising real-world camouflaged objects with class annotations is also presented. Furthermore, a multi-stage framework, CGNet, is proposed, incorporating a plug-and-play class prompt generator and a simple yet effective class-guided detector. This establishes a new paradigm for COD, bridging the gap between contextual understanding and class-guided detection. Extensive experimental results demonstrate the effectiveness of this flexible framework in improving the performance of proposed and existing detectors by leveraging class-level textual information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Camouflaged objects are hard to detect because they blend into their surroundings. Current methods don’t work well because they only look at visual features, like color and texture. But real-world camouflaged objects can be tricky to spot because they have low contrast with the background, many textures, and small appearance changes. To solve this problem, a new task is introduced that uses class information, like what kind of object it is, to make detection more robust and accurate. A new dataset, CamoClass, has real-world camouflaged objects with class labels. A multi-stage framework, CGNet, is also proposed, which includes a prompt generator and a detector. This new approach bridges the gap between understanding the context and detecting classes. |
Keywords
* Artificial intelligence * Object detection * Prompt
