Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
Authors: Lin Chen, Zhixiang Wei, Xin Jin, Huaian Chen, Miao Zheng, Kai Chen, Yi Jin
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on adaptive segmentation tasks with different settings demonstrate that our DDB significantly outperforms state-of-the-art methods. |
| Researcher Affiliation | Academia | Lin Chen1,2 Zhixiang Wei1 Xin Jin3 Huaian Chen1 Miao Zheng2 Kai Chen2 Yi Jin1 1 University of Science and Technology of China 2 Shanghai AI Laboratory 3 Eastern Institute for Advanced Study |
| Pseudocode | Yes | These two steps are conducted iteratively, and the ending of each round will serve as the beginning for the next round (see detailed algorithm in supplemental material). |
| Open Source Code | Yes | Code is available at https: //github.com/xiaoachen98/DDB.git. |
| Open Datasets | Yes | We use four publicly available semantic segmentation benchmarks for validation, including two synthetic scenes and two real-world scenes. In detail, GTA5 [48] is a synthetic dataset of 24,966 labeled images obtained from a video game. Synscapes [56] is also a synthetic dataset of 25,000 images created by photo-realistic rendering techniques, and its style is closer to real-world driving scenes than GTA5. Cityscapes [9] is a real-world urban dataset collected from European cities, with 2,975 images for training and 500 images for validation. Mapillary Vista [42] is a large-scale dataset collected by various imaging devices worldwide and includes 18,000 images for training and 2,000 images for validation. |
| Dataset Splits | Yes | Cityscapes [9] is a real-world urban dataset collected from European cities, with 2,975 images for training and 500 images for validation. Mapillary Vista [42] is a large-scale dataset collected by various imaging devices worldwide and includes 18,000 images for training and 2,000 images for validation. |
| Hardware Specification | Yes | We use the mmsegmentation [8] codebase and train models on RTX 3090Ti GPUs. |
| Software Dependencies | No | The paper mentions 'mmsegmentation', 'Deep Lab-v2', 'Res Net101', and 'Adam W' but does not specify their version numbers. |
| Experiment Setup | Yes | We set the learning rate as 6e-5 for the backbone and 6e-4 for the decoder head, use a weight decay of 0.01 and a linear learning rate warmup followed by 1.5k iterations linear decay. All experiments are trained on a batch of 512x512 random cropped images for 40k iterations. We set the batch size to 2 for analysis and experiments in Tab. 1 and Tab. 6, and set batch size to 4 for other results. Following [50], we use the same augmentation parameters and set τ = 0.968. For Cut Mix [62], the ratio of the selected region for cross-domain pasting is experimentally set to 0.3. For Class Mix [43], half of categories in the source domain are selected for cross-domain pasting. |