Summary of Prcl: Probabilistic Representation Contrastive Learning For Semi-supervised Semantic Segmentation, by Haoyu Xie et al.
PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation
by Haoyu Xie, Changqi Wang, Jian Zhao, Yang Liu, Jun Dan, Chong Fu, Baigui Sun
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 paper presents a breakthrough in Semi-Supervised Semantic Segmentation (S4) through contrastive learning, but notes that current methods rely too heavily on model-generated guidance for unlabeled images, leading to noise and training process disturbances. To address this issue, the authors propose a Probabilistic Representation Contrastive Learning (PRCL) framework that enhances the robustness of unsupervised training. The framework models pixel-wise representations as probabilistic distributions and tunes ambiguous representation contributions to tolerate inaccurate guidance in contrastive learning. Additionally, it introduces Global Distribution Prototypes (GDP) to capture intra-class variance and Virtual Negatives (VNs) for more effective contrastive learning. The PRCL framework is evaluated on two public benchmarks, demonstrating its superiority. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper makes a big breakthrough in understanding how computers can learn from incomplete training data. Right now, computer models are really good at recognizing things like objects or faces when they’re labeled, but they struggle when there aren’t any labels. To fix this problem, the researchers came up with a new way to train the models using something called contrastive learning. This method helps the model learn from both labeled and unlabeled data by comparing and contrasting different representations of images. The researchers also introduced some new ideas, like probabilistic distributions and prototypes, which help the model become more robust to noisy or ambiguous data. They tested their approach on two big datasets and showed that it performs better than previous methods. |
Keywords
* Artificial intelligence * Semantic segmentation * Semi supervised * Unsupervised
