Knowledge Consultation for Semi-Supervised Semantic Segmentation
This work addresses the need for efficient segmentation with less annotation, but it appears incremental as it builds on existing co-training methods.
The paper tackles the problem of semi-supervised semantic segmentation by proposing Knowledge Consultation (SegKC) to enhance performance, achieving mIoU scores of up to 89.8% on Pascal VOC benchmarks.
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.