@article{Pang_Li_Zhang_Peng_Tang_Zha_Li_Lu_2022, title={Unsupervised Representation for Semantic Segmentation by Implicit Cycle-Attention Contrastive Learning}, volume={36}, url={https://ojs.aaai.org/index.php/AAAI/article/view/20100}, DOI={10.1609/aaai.v36i2.20100}, abstractNote={We study the unsupervised representation learning for the semantic segmentation task. Different from previous works that aim at providing unsupervised pre-trained backbones for segmentation models which need further supervised fine-tune, here, we focus on providing representation that is only trained by unsupervised methods. This means models need to directly generate pixel-level, linearly separable semantic results. We first explore and present two factors that have significant effects on segmentation under the contrastive learning framework: 1) the difficulty and diversity of the positive contrastive pairs, 2) the balance of global and local features. With the intention of optimizing these factors, we propose the cycle-attention contrastive learning (CACL). CACL makes use of semantic continuity of video frames, adopting unsupervised cycle-consistent attention mechanism to implicitly conduct contrastive learning with difficult, global-local-balanced positive pixel pairs. Compared with baseline model MoCo-v2 and other unsupervised methods, CACL demonstrates consistently superior performance on PASCAL VOC (+4.5 mIoU) and Cityscapes (+4.5 mIoU) datasets.}, number={2}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Pang, Bo and Li, Yizhuo and Zhang, Yifan and Peng, Gao and Tang, Jiajun and Zha, Kaiwen and Li, Jiefeng and Lu, Cewu}, year={2022}, month={Jun.}, pages={2044-2052} }