Exclusivity-Guided Mask Learning for Semi-Supervised Crowd Instance Segmentation and Counting

Semi-supervised crowd analysis is a prominent area of re-search, as unlabeled data are typically abundant and inexpensive toobtain. However, traditional point-based annotations constrain perfor-mance because individual regions are inherently ambiguous, and con-sequently, learning fine-grained structural semantics from sparse anno-tations remains an unresolved challenge. In this paper, we first pro-pose an Exclusion-Constrained Dual-Prompt SAM (EDP-SAM), basedon our Nearest Neighbor Exclusion Circle (NNEC) constraint, to gener-ate mask supervision for current datasets. With the aim of segmentingindividuals in dense scenes, we then propose Exclusivity-Guided MaskLearning (XMask), which enforces spatial separation through a discrim-inative mask objective. Gaussian smoothing and a differentiable centersampling strategy are utilized to improve feature continuity and train-ing stability. Building on XMask, we present a semi-supervised crowdcounting framework that uses instance mask priors as pseudo-labels,which contain richer shape information than traditional point cues. Ex-tensive experiments on the ShanghaiTech A, UCF-QNRF, and JHU++datasets (using 5%, 10%, and 40% labeled data) verify that our end-to-end model achieves state-of-the-art semi-supervised segmentation andcounting performance, effectively bridging the gap between counting andinstance segmentation within a unified framework. Code can be found athttps://github.com/JoyceeH0127/ECCV2026XMask.