Weakly Supervised Learning in Computer Vision

The field of computer vision is witnessing significant advancements in weakly supervised learning, with a focus on developing methods that can learn from limited annotated data. Researchers are exploring innovative approaches to address the challenges of fine-grained visual categorization, object segmentation, and defect detection. A common theme among these approaches is the use of pseudo-labels, soft labels, and contrastive learning to improve model performance. Notably, the integration of region-aware attention, Bayesian uncertainty, and diffusion features is showing promising results. Noteworthy papers include RAUM-Net, which proposes a semi-supervised method for fine-grained visual categorization, and Weakly Supervised Object Segmentation by Background Conditional Divergence, which introduces a method for training a masking network using weak supervision. Region-Aware CAM and Contrastive Learning with Diffusion Features are also making significant contributions to the field of weakly supervised semantic segmentation.

Sources

RAUM-Net: Regional Attention and Uncertainty-aware Mamba Network

Weakly Supervised Object Segmentation by Background Conditional Divergence

Region-Aware CAM: High-Resolution Weakly-Supervised Defect Segmentation via Salient Region Perception

Contrastive Learning with Diffusion Features for Weakly Supervised Medical Image Segmentation

De-Simplifying Pseudo Labels to Enhancing Domain Adaptive Object Detection

Soft Self-labeling and Potts Relaxations for Weakly-Supervised Segmentation

Self-Reinforcing Prototype Evolution with Dual-Knowledge Cooperation for Semi-Supervised Lifelong Person Re-Identification

LMPNet for Weakly-supervised Keypoint Discovery

Weakly-supervised Contrastive Learning with Quantity Prompts for Moving Infrared Small Target Detection

Partial Weakly-Supervised Oriented Object Detection

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