Summary of Deep Semantic-visual Alignment For Zero-shot Remote Sensing Image Scene Classification, by Wenjia Xu et al.
Deep Semantic-Visual Alignment for Zero-Shot Remote Sensing Image Scene Classification
by Wenjia Xu, Jiuniu Wang, Zhiwei Wei, Mugen Peng, Yirong Wu
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| 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 proposes a novel approach to zero-shot learning (ZSL) for remote sensing (RS) image classification. The traditional ZSL methods rely on manual labeling of attributes or word embeddings, which is time-consuming and unrealistic given the dynamic growth of the target database. The authors address this issue by collecting visually detectable attributes automatically, predicting them for each class based on semantic-visual similarity between attributes and images. They also introduce a Deep Semantic-Visual Alignment (DSVA) model that utilizes self-attention mechanisms to integrate background context information and focus on informative image regions. This approach outperforms state-of-the-art models by a large margin on a challenging RS scene classification benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn new things without seeing them before! Right now, it’s hard to teach machines to recognize objects in pictures taken from space or airplanes. The authors figured out a way to make this process easier and more accurate. They do this by automatically labeling the important parts of each picture, like what’s in the background, instead of relying on humans to do it. This new approach is super good at recognizing things it hasn’t seen before! |
Keywords
» Artificial intelligence » Alignment » Classification » Image classification » Self attention » Zero shot
