Summary of Progressive Multi-task Anti-noise Learning and Distilling Frameworks For Fine-grained Vehicle Recognition, by Dichao Liu
Progressive Multi-task Anti-Noise Learning and Distilling Frameworks for Fine-grained Vehicle Recognition
by Dichao Liu
First submitted to arxiv on: 25 Jan 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 research proposes a progressive multi-task anti-noise learning (PMAL) framework and a progressive multi-task distilling (PMD) framework to address the intra-class variation problem in fine-grained vehicle recognition (FGVR) due to image noise. The PMAL framework treats image denoising as an additional task in image recognition, progressively forcing the model to learn noise invariance. The PMD framework transfers the knowledge of the PMAL-trained model into the original backbone network, producing a model with similar recognition accuracy without additional overheads. By combining both frameworks, the paper achieves state-of-the-art recognition accuracy on several FGVR datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps make cars easier to recognize in pictures and videos. Right now, it’s hard because of things like noise in the image that can confuse computers. The researchers created two new ways for computers to learn how to ignore this noise. One way is to teach the computer to get rid of the noise while recognizing the car. The other way is to take what the first method learned and share it with the original computer program. This makes the computer better at recognizing cars without using extra processing power. |
Keywords
» Artificial intelligence » Image denoising » Multi task
