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In this paper, we investigate if we could make the self-training -a simple but popular framework -work better for semi-supervised segmentation. Since the core issue in semi-supervised setting lies in effective and efficient utilization of unlabeled data, we notice that increasing the diversity and hardness of unlabeled data is crucial to performance improvement. Being aware of this fact, we propose to adopt the most plain self-training scheme coupled with appropriate strong data augmentations on unlabeled data (namely ST) for this task, which surprisingly outperforms previous methods under various settings without any bells and whistles. Moreover, to alleviate the negative impact of the wrongly pseudo labeled images, we further propose an advanced self-training framework (namely ST++), that performs selective re-training via selecting and prioritizing the more reliable unlabeled images. As a result, the proposed ST++ boosts the performance of semi-supervised model significantly and surpasses existing methods by a large margin on the Pascal VOC 2012 and Cityscapes benchmark. Overall, we hope this straightforward and simple framework will serve as a strong baseline or competitor for future works.Code is available at https://github.com/LiheYoung/ST-PlusPlus.

Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. Existing methods suffer the problem of feature undermining, i.e., potential novel classes are treated as background during training phase. Our method aims to alleviate this problem and enhance the feature embedding on latent novel classes. In our work, we propose a novel joint-training framework. Based on conventional episodic training on support-query pairs, we introduce an additional mining branch that exploits latent novel classes via transferable sub-clusters, and a new rectification technique on both background and foreground categories to enforce more stable prototypes. Over and above that, our transferable sub-cluster has the ability to leverage extra unlabeled data for further feature enhancement. Extensive experiments on two FSS benchmarks demonstrate that our method outperforms previous state-ofthe-art by a large margin of 3.7% mIOU on PASCAL-5 i and 7.0% mIOU on COCO-20 i at the cost of 74% fewer parameters and 2.5x faster inference speed.

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To produce reliable kidney and kidney tumor semantic segmentation, we proposed a two-stage method to automatically segment kidney and tumor. Specifically, in the first stage, to crop input into a small region, we train a small network to locate kidney and tumor with down-sampled image. In second stage, we train three types of networks to segment kidney, tumor, kidney and tumor respectively. Then we combine these networks together with ensemble method to produce reliable kidney and tumor segmentation. Our method can achieve an overall approximate score of 85.1% in DSC in Kits19 Challenge, with 96.9% for kidney and 73.3% for kidney tumor.

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