Debate-Enhanced Pseudo Labeling and Frequency-Aware Progressive Debiasing for Weakly-Supervised Camouflaged Object Detection with Scribble Annotations
📰 ArXiv cs.AI
Improve weakly-supervised camouflaged object detection using debate-enhanced pseudo labeling and frequency-aware progressive debiasing with scribble annotations
Action Steps
- Apply debate-enhanced pseudo labeling to generate high-quality pseudo masks
- Implement frequency-aware progressive debiasing to reduce bias in the model
- Use scribble annotations as sparse supervision for weakly-supervised camouflaged object detection
- Evaluate the performance of the model using metrics such as precision, recall, and F1-score
- Fine-tune the model by adjusting hyperparameters and experimenting with different architectures
Who Needs to Know This
Computer vision engineers and researchers working on object detection tasks can benefit from this technique to improve the accuracy of their models, especially in scenarios with limited supervision.
Key Insight
💡 Debate-enhanced pseudo labeling and frequency-aware progressive debiasing can significantly improve the accuracy of weakly-supervised camouflaged object detection models
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Boost weakly-supervised camouflaged object detection with debate-enhanced pseudo labeling & frequency-aware progressive debiasing! #CV #AI
Key Takeaways
Improve weakly-supervised camouflaged object detection using debate-enhanced pseudo labeling and frequency-aware progressive debiasing with scribble annotations
Full Article
Title: Debate-Enhanced Pseudo Labeling and Frequency-Aware Progressive Debiasing for Weakly-Supervised Camouflaged Object Detection with Scribble Annotations
Abstract:
arXiv:2512.20260v5 Announce Type: replace-cross Abstract: Weakly-Supervised Camouflaged Object Detection (WSCOD) aims to locate and segment objects that are visually concealed within their surrounding scenes, relying solely on sparse supervision such as scribble annotations. Despite recent progress, existing WSCOD methods still lag far behind fully supervised ones due to two major limitations: (1) the pseudo masks generated by general-purpose segmentation models (e.g., SAM) and filtered via rule
Abstract:
arXiv:2512.20260v5 Announce Type: replace-cross Abstract: Weakly-Supervised Camouflaged Object Detection (WSCOD) aims to locate and segment objects that are visually concealed within their surrounding scenes, relying solely on sparse supervision such as scribble annotations. Despite recent progress, existing WSCOD methods still lag far behind fully supervised ones due to two major limitations: (1) the pseudo masks generated by general-purpose segmentation models (e.g., SAM) and filtered via rule
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