Open Compound Domain Adaptation with Object Style Compensation for Semantic Segmentation
Authors: Tingliang Feng, Hao Shi, Xueyang Liu, Wei Feng, Liang Wan, Yanlin Zhou, Di Lin
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct an intensive evaluation of the object style compensation. On the public datasets (e.g., C-Driving [7], ACDC [12], Cityscapes [13], KITTI [14], and Wild Dash [15]) that allows OCDA to assist semantic segmentation, the object style compensation surpasses state-of-the-art methods, demonstrating its effectiveness. |
| Researcher Affiliation | Academia | Tingliang Feng1,2 Hao Shi1,3 Xueyang Liu1 Wei Feng1,2 Liang Wan1 Yanlin Zhou4 Di Lin1 1College of Intelligence and Computing, Tianjin University 2Laboratory of Computation and Analytics of Complex Management Systems (CACMS), Tianjin University 3Department of Automation, Tsinghua University 4Dunhuang Academy |
| Pseudocode | No | The paper describes the method using diagrams and text, but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing code or a link to a code repository. |
| Open Datasets | Yes | We use GTA5 [57], SYNTHIA [58], CDriving [7], ACDC [12], Cityscapes [13], KITTI [14], and Wild Dash [15] datasets to evaluate our method. |
| Dataset Splits | No | All images with annotations in source domain and a portion of images without annotations in target domain are used for network training. We evaluate the segmentation performances on the rest images in target domain and all images in open domain. |
| Hardware Specification | Yes | The network is trained on a single RTX 3090 GPU. |
| Software Dependencies | No | We employ the Py Torch toolkit to implement our segmentation network with Object-Level Discrepancy Memory (OLDM). |
| Experiment Setup | Yes | To optimize the network, we utilize the SGD solver for 250,000 iterations. The initial learning rate is set to 0.00025, which undergoes a linear decay throughout the training process. Each mini-batch consists of 4 images, comprising 2 source images and 2 target images. We apply random cropping, flipping, color jittering, and Gaussian blurring techniques to the training images, using a crop size of 640 × 360. |