Knowledge Consultation for Semi-Supervised Semantic Segmentation
Journal:
arXiv
Published Date:
Mar 12, 2025
Abstract
Semi-Supervised Semantic Segmentation reduces reliance on extensive
annotations by using unlabeled data and state-of-the-art models to improve
overall performance. Despite the success of deep co-training methods, their
underlying mechanisms remain underexplored. This work revisits Cross Pseudo
Supervision with dual heterogeneous backbones and introduces Knowledge
Consultation (SegKC) to further enhance segmentation performance. The proposed
SegKC achieves significant improvements on Pascal and Cityscapes benchmarks,
with mIoU scores of 87.1%, 89.2%, and 89.8% on Pascal VOC with the 1/4, 1/2,
and full split partition, respectively, while maintaining a compact model
architecture.
