Summary of Efficient and Concise Explanations For Object Detection with Gaussian-class Activation Mapping Explainer, by Quoc Khanh Nguyen et al.
Efficient and Concise Explanations for Object Detection with Gaussian-Class Activation Mapping Explainer
by Quoc Khanh Nguyen, Truong Thanh Hung Nguyen, Vo Thanh Khang Nguyen, Van Binh Truong, Tuong Phan, Hung Cao
First submitted to arxiv on: 20 Apr 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 The proposed Gaussian Class Activation Mapping Explainer (G-CAME) aims to provide quick and plausible explanations for object detection models. It efficiently generates concise saliency maps by utilizing activation maps from selected layers and applying a Gaussian kernel. This approach significantly reduces explanation time to 0.5 seconds without compromising quality, outperforming region-based methods. The evaluation of G-CAME on the MS-COCO 2017 dataset with Faster-RCNN and YOLOX demonstrates its ability to offer highly plausible and faithful explanations, particularly in reducing bias on tiny object detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary G-CAME is a new way to explain why an object detection model chose a certain object. It’s fast, accurate, and helps reduce errors when detecting small objects. This method uses special maps from different parts of the model and applies a mathematical technique called Gaussian kernel. The result is a clear picture showing what features in the image are most important for detecting an object. |
Keywords
» Artificial intelligence » Faster rcnn » Object detection
