UniMatch V2: Pushing the Limit of Semi-Supervised Semantic Segmentation Publication date: 14 Oct 2024 Topic: Semantic Segmentation Paper: https://arxiv.org/pdf/2410.10777v1.pdf GitHub: https://github.com/LiheYoung/UniMatch-V2 Description: In this work, we argue that, it is necessary to switch the baseline of SSS from ResNet-based encoders to more capable ViT-based encoders (e.g., DINOv2) that are pre-trained on massive data. A simple update on the encoder (even using 2x fewer parameters) can bring more significant improvement than careful method designs. Built on this competitive baseline, we present our upgraded and simplified UniMatch V2, inheriting the core spirit of weak-to-strong consistency from V1, but requiring less training cost and providing consistently better results. Additionally, witnessing the gradually saturated performance on Pascal and Cityscapes, we appeal that we should focus on more challenging benchmarks with complex taxonomy, such as ADE20K and COCO datasets.