Connectivity-Driven Pseudo-Labeling Makes Stronger Cross-Domain Segmenters
Authors: Dong Zhao, Qi Zang, Shuang Wang, Nicu Sebe, Zhun Zhong
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that Se Co can be flexibly applied to various cross-domain semantic segmentation tasks, i.e. domain generalization and domain adaptation, even including source-free, and black-box domain adaptation, significantly improving the performance of existing state-of-the-art methods. |
| Researcher Affiliation | Academia | Dong Zhao1 , Qi Zang1 , Shuang Wang1 , Nicu Sebe2, Zhun Zhong3,4 1 School of Artificial Intelligence, Xidian University, Shaanxi, China 2 Department of Information Engineering and Computer Science, University of Trento, Italy 3 School of Computer Science and Information Engineering, Hefei University of Technology, China 4 School of Computer Science, University of Nottingham, NG8 1BB Nottingham, UK |
| Pseudocode | Yes | Algorithm 1 Aggregation of Pseudo-Labels with SAM |
| Open Source Code | Yes | The code is available at https://github.com/DZhao Xd/Se Co. |
| Open Datasets | Yes | Datasets. We employ two real datasets (Cityscapes [12] and BDD-100k [86]) alongside two synthetic datasets (GTA5 [63] and SYNTHIA [65]). The details of these datasets are introduced in Section B. |
| Dataset Splits | Yes | The Cityscapes dataset comprises 2,975 training images and 500 validation images, all with a resolution of 2048 1024. |
| Hardware Specification | No | The paper mentions using 'SAM [36] with Vision Transformer-H (Vi T-H) [14]' but does not specify the hardware (e.g., GPU models, CPU, RAM) used for running the experiments or training the models. |
| Software Dependencies | No | The paper mentions using Deep Lab V2, Seg Former, and SAM, but does not specify software versions for libraries, frameworks, or operating systems (e.g., PyTorch 1.x, CUDA 11.x). |
| Experiment Setup | Yes | In Algorithm 1, the enlargement factor for the bounding box area is set to 1.5. The connectivity classifier is trained only for 5000 iterations in an early learning way for all tasks. The noise threshold (τns) and correction threshold (τcr) are configured at 0.60 and 0.95, respectively. |