Summary of Knowledge Distillation to Effectively Attain Both Region-of-interest and Global Semantics From An Image Where Multiple Objects Appear, by Seonwhee Jin
Knowledge distillation to effectively attain both region-of-interest and global semantics from an image where multiple objects appear
by Seonwhee Jin
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 paper proposes a novel approach to improving object detection in food images. Current models based on convolutional neural networks (CNN) and transformers have achieved significant improvements, but still struggle with accurately localizing and classifying food categories. To address this challenge, the authors segment the food region-of-interest (ROI) using the segment-anything model (SAM), mask the rest of the image as black pixels, and then fine-tune off-the-shelf models to classify the ROI. They find that Data-efficient image Transformers (DeiTs) perform well but struggle with ambiguous images. To overcome this limitation, they introduce RveRNet, a combined architecture that incorporates both ROI and global context features. The paper also investigates the robustness of architectures against input noise caused by permutation and translocation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to recognize food in pictures. They took a problem that was hard for computers – recognizing many types of foods in images – and made it easier by focusing on just one part of the image (the food). Then, they used special computer models to look at this “food region” and decide what type of food it is. The best model they tried was called DeiT, which uses a combination of ideas from two other approaches. However, even DeiT struggled with pictures where the food looks similar to others. To solve this problem, they came up with a new idea – combining different features from the “food region” and the rest of the image. This new approach worked better than any one model alone. |
Keywords
» Artificial intelligence » Cnn » Mask » Object detection » Sam
