AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation
Authors: Boyu Han, Qianqian Xu, Zhiyong Yang, Shilong Bao, Peisong Wen, Yangbangyan Jiang, Qingming Huang
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. |
| Researcher Affiliation | Academia | Boyu Han1,2 Qianqian Xu1,3 Zhiyong Yang2 Shilong Bao2 Peisong Wen1,2 Yangbangyan Jiang2 Qingming Huang2,1,4 1 Key Lab. of Intelligent Information Processing, Institute of Computing Technology, CAS 2 School of Computer Science and Tech., University of Chinese Academy of Sciences 3 Peng Cheng Laboratory 4 Key Laboratory of Big Data Mining and Knowledge Management, CAS |
| Pseudocode | Yes | Algorithm 1: AUCSeg Algorithm (Short Version) |
| Open Source Code | Yes | The code is available at https://github.com/boyuh/AUCSeg. |
| Open Datasets | Yes | The experiment includes three benchmark datasets: Cityscapes [19], ADE20K [109], and COCO-Stuff 164K [9]. |
| Dataset Splits | Yes | The training, validation, and testing set numbers are 2975, 500, and 1525, respectively. (Cityscapes) ...with 20210, 2000, and 3352 images used for training, validation, and testing, respectively. (ADE20K) |
| Hardware Specification | Yes | We perform all experiments using mmsegmentation [18] on an NVIDIA 3090 GPU. |
| Software Dependencies | No | The paper mentions software like mmsegmentation and AdamW optimizer, but does not provide specific version numbers for these software dependencies (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | We use Adam with Weight Decay (Adam W) [59] optimizer with an initial learning rate of 6e-5 and a weight decay of 0.01. We adopt the poly learning rate policy, where the initial learning rate is multiplied by 1 − iter/max_iter. Moreover, a linear warmup strategy is employed at the beginning of training, allowing the learning rate to increase from 1e-6 to the initial learning rate within 1500 iterations. The batch size is set to 2 for the Cityscapes dataset and 4 for all the other datasets. The total number of iterations is 160000 on Cityscapes and ADE20K and 80000 on COCO-Stuff 164K. |