Summary of Precision Matters: Precision-aware Ensemble For Weakly Supervised Semantic Segmentation, by Junsung Park et al.
Precision matters: Precision-aware ensemble for weakly supervised semantic segmentation
by Junsung Park, Hyunjung Shim
First submitted to arxiv on: 28 Jun 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 The proposed Weakly Supervised Semantic Segmentation (WSSS) method employs weak supervision through image-level labels to train segmentation models. Despite recent advancements, introducing high-quality weak labels does not guarantee high performance. Existing studies emphasize prioritizing precision and reducing noise for improved results. The authors introduce ORANDNet, an ensemble approach combining Class Activation Maps (CAMs) from two classifiers to increase pseudo-mask (PM) precision. To mitigate small PM noise, they incorporate curriculum learning, initially training with smaller-sized images and gradually transitioning to original-sized pairs. By combining ResNet-50 and ViT CAMs, the authors significantly improve segmentation performance over single-best and naive ensemble models. They also extend their approach to AMN and MCTformer models, achieving benefits in advanced WSSS models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary WSSS is a way for machines to learn about images without perfect labels. It’s like trying to draw a picture of an animal just from knowing it’s an animal, not what kind. The method has gotten better recently, but even with good weak labels, the results aren’t always great. To fix this, researchers propose ORANDNet, a way to combine information from multiple models. This helps make pseudo-masks (fake labels) more accurate and reduces small mistakes. By training on smaller images first and then bigger ones, they can get better results. The authors show that their method works well with different types of models and datasets. |
Keywords
» Artificial intelligence » Curriculum learning » Mask » Precision » Resnet » Semantic segmentation » Supervised » Vit
