Summary of Adapnet: Adaptive Noise-based Network For Low-quality Image Retrieval, by Sihe Zhang et al.
AdapNet: Adaptive Noise-Based Network for Low-Quality Image Retrieval
by Sihe Zhang, Qingdong He, Jinlong Peng, Yuxi Li, Zhengkai Jiang, Jiafu Wu, Mingmin Chi, Yabiao Wang, Chengjie Wang
First submitted to arxiv on: 28 May 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 Medium Difficulty summary: Image retrieval aims to identify visually similar images using a query image. Traditional methods combine global and local features, but may not account for noise in the query images, which can negatively impact performance. To address this, we propose an Adaptive Noise-Based Network (AdapNet) that learns robust abstract representations by compensating for low-quality factors and adjusting its focus on noisy samples during training. We evaluate AdapNet’s performance using two datasets with low-quality queries built from standard Revisited Oxford and Paris datasets. Experimental results show that AdapNet surpasses state-of-the-art methods on Noise Revisited Oxford and Paris benchmarks while maintaining competitive performance on high-quality datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about finding similar pictures when you have a noisy or low-quality picture to start with. Traditional ways of doing this might not work well if the starting picture is messy or has noise in it. The researchers created a new method, called AdapNet, that can learn to ignore the noise and find good matches even with low-quality starting pictures. They tested their method on some special datasets they made by adding noise to clean pictures. Their results show that AdapNet works better than other methods when dealing with noisy or low-quality pictures. |
