时空对比学习驱动的弱监督图像语义分割网络

Key Points

• The goal is to enhance weakly supervised semantic segmentation accuracy by utilizing spatio-temporal information.
• Developed a spatio-temporal contrastive learning network using Vision Transformer (ViT).
• Introduced spatial feature contrastive learning through token mechanisms and comparison strategies.
• Implemented temporal context contrastive learning with a memory bank and dynamic update strategies for guidance.
• Applied experiments on PASCAL VOC and MS COCO datasets to evaluate performance.
• Achieved mean Intersection over Union (IoU) of 72.7% on PASCAL VOC and 43.6% on MS COCO.
• Demonstrated improved accuracy over existing methods through enhanced semantic feature extraction.