Space Engage: Collaborative Space Supervision for Contrastive-based
  Semi-Supervised Semantic Segmentation
Changqi Wang Haoyu Xie Yuhui Yuan Chong Fu Xiangyu Yue
Semi-Supervised Semantic Segmentation (S4) aims to train a segmentation model
with limited labeled images and a substantial volume of unlabeled images. To
improve the robustness of representations, powerful methods introduce a
pixel-wise contrastive learning approach in latent space (i.e., representation
space) that aggregates the representations to their prototypes in a fully
supervised manner. However, previous contrastive-based S4 methods merely rely
on the supervision from the model's output (logits) in logit space during
unlabeled training. In contrast, we utilize the outputs in both logit space and
representation space to obtain supervision in a collaborative way. The
supervision from two spaces plays two roles: 1) reduces the risk of
over-fitting to incorrect semantic information in logits with the help of
representations; 2) enhances the knowledge exchange between the two spaces.
Furthermore, unlike previous approaches, we use the similarity between
representations and prototypes as a new indicator to tilt training those
under-performing representations and achieve a more efficient contrastive
learning process. Results on two public benchmarks demonstrate the competitive
performance of our method compared with state-of-the-art methods.