4 papers
Correspondence as Video: Test-Time Adaption on SAM2 for Reference Segmentation in the Wild
Haoran Wang, Zekun Li, Jian Zhang +2
Large vision models like the Segment Anything Model (SAM) exhibit significant limitations when applied to downstream tasks in the wild. Consequently, reference segmentation, which…
Balancing Multi-Target Semi-Supervised Medical Image Segmentation with Collaborative Generalist and Specialists
You Wang, Zekun Li, Lei Qi +3
Despite the promising performance achieved by current semi-supervised models in segmenting individual medical targets, many of these models suffer a notable decrease in performance…
Taste More, Taste Better: Diverse Data and Strong Model Boost Semi-Supervised Crowd Counting
Maochen Yang, Zekun Li, Jian Zhang +2
Semi-supervised crowd counting is crucial for addressing the high annotation costs of densely populated scenes. Although several methods based on pseudo-labeling have been proposed…
Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation
Qinghe Ma, Jian Zhang, Zekun Li +3
Large pretrained visual foundation models exhibit impressive general capabilities. However, the extensive prior knowledge inherent in these models can sometimes be a double-edged s…