Active Boundary Loss for Semantic Segmentation
Authors: Chi Wang, Yunke Zhang, Miaomiao Cui, Peiran Ren, Yin Yang, Xuansong Xie, Xian-Sheng Hua, Hujun Bao, Weiwei Xu2397-2405
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that training with the active boundary loss can effectively improve the boundary F-score and mean Intersection-over-Union on challenging image and video object segmentation datasets. |
| Researcher Affiliation | Collaboration | Chi Wang1,2, Yunke Zhang1,2, Miaomiao Cui2, Peiran Ren2, Yin Yang3, Xuansong Xie2, Xian-Sheng Hua2, Hujun Bao1, Weiwei Xu1* 1 State Key Lab of CAD&CG, Zhejiang University 2Alibaba Inc 3 Clemson University |
| Pseudocode | No | The paper describes procedural steps in text, such as 'Phase I' and 'Phase II' of the Active Boundary Loss, but does not present them in a structured pseudocode or algorithm block format. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | We evaluate our loss mainly on the image segmentation dataset Cityscapes (Cordts et al. 2016) and ADE20K (Zhou et al. 2017). |
| Dataset Splits | Yes | ADE20K: There are 20210/2000/3000 images for the training/validation/testing set in ADE20K, respectively. |
| Hardware Specification | Yes | We implemented the ABL on a GPU server (2 Intel Xeon Gold 6148 CPUs, 512GB memory) with 4 Nvidia Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions software like 'Pytorch' and 'scipy.ndimage.morphology.distance transform edt' but does not specify their version numbers. |
| Experiment Setup | Yes | ADE20K: the parameters are set as follows: initial learning rate = 0.02, weight decay = 0.0001, crop size = 520 520, batch size = 16, and 150k training iterations... Cityscapes: the parameters are set as follows: initial learning rate = 0.01 or 0.04, crop size = 512 1024 (used in OCR) or 769 769 (used in Deeplab V3), weight decay = 0.0005, batch size = 8, and 80K training iterations. |