Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |