Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
Authors: Ye Du, Yujun Shen, Haochen Wang, Jingjing Fei, Wei Li, Liwei Wu, Rui Zhao, Zehua Fu, Qingjie LIU
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally demonstrate the effectiveness and superiority of our approach under a wide range of settings. |
| Researcher Affiliation | Collaboration | Ye Du1,2 Yujun Shen3 Haochen Wang4 Jingjing Fei5 Wei Li5 Liwei Wu5 Rui Zhao5,6 Zehua Fu1,2 Qingjie Liu1,2 1 State Key Laboratory of Virtual Reality Technology and Systems, Beihang University 2 Hangzhou Innovation Institute, Beihang University 3 The Chinese University of Hong Kong 4 Institute of Automation, Chinese Academy of Sciences 5 Sense Time Research 6 Qing Yuan Research Institute, Shanghai Jiao Tong University, Shanghai, China |
| Pseudocode | Yes | We provide pseudo-codes to further illustrate how we implement Eq. (4) in Supplementary Material. |
| Open Source Code | Yes | Code is available at https://github.com/usr922/FST. |
| Open Datasets | Yes | In UDA segmentation, we use synthetic labeled images from GTAV [38] and SYNTHIA [39] as the source domain and use real images from Cityscapes [13] as the target domain. In addition, PASCAL VOC 2012 [15] is used for standard semi-supervised evaluation. |
| Dataset Splits | Yes | To simulate a semi-supervised setting, we randomly sample a portion (i.e., 1/4, 1/8, and 1/16) of images together with corresponding segmentation masks from the training set as the labeled data and treat the rest as the unlabeled samples. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts, or cloud instance types) used for running its experiments. |
| Software Dependencies | No | The paper mentions optimizers (AdamW, SGD) and network architectures (Deep Lab V2, Deep Lab V3+, PSPNet, UPer Net) but does not provide specific version numbers for any software dependencies like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | In UDA segmentation, the model is trained with an Adam W [28] optimizer, a learning rate of 6 × 10−5 for the encoder and 6 × 10−4 for the decoder, a weight decay of 0.01, linear learning rate warmup with 1.5k iterations and linear decay afterwards. We train the model on a batch of two 512 × 512 random crops for a total of 40k iterations. The momentum u is set to 0.999. In semi-supervised segmentation, the model is trained with a SGD optimizer, a learning rate of 0.0001 for the encoder and 0.001 for the decoder, a weight decay of 0.0001. We train the model with 16 labeled and 16 unlabeled images per-batch for a total of 40 epochs. |