Visual Boundary Knowledge Translation for Foreground Segmentation
Authors: Zunlei Feng, Lechao Cheng, Xinchao Wang, Xiang Wang, Ya Jie Liu, Xiangtong Du, Mingli Song1334-1342
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Exhaustive experiments demonstrate that, with only tens of labeled samples as guidance, Trans-Net achieves close results on par with fully supervised methods. |
| Researcher Affiliation | Collaboration | 1Zhejiang University 2Zhejiang Lab 3Stevens Institute of Technology 4Jiangsu University of Science and Technology {zunleifeng, xiang.wang, brooksong}@zju.edu.cn, {chenglc,liuyj}@zhejianglab.com, xinchao.wang@stevens.edu |
| Pseudocode | Yes | Algorithm 1 The Training Algorithm for Trans-Net |
| Open Source Code | No | The paper lists external datasets and references them, but does not contain an explicit statement about releasing its own source code or provide a link to a repository for the described methodology. |
| Open Datasets | Yes | The datasets we adopted contain single category datasets: Birds (Catherine et al. 2011), Flowers (Nilsback and Zisserman 2007) and Human Matting1, and mixed category datasets: THUR15K (Cheng et al. 2014), MSRA10K and MSRA-B (Cheng et al. 2011; Hou et al. 2017), CSSD (Yan et al. 2013), ECSSD (Shi et al. 2016), DUT-OMRON (Ruan, Tong, and Lu 2011), PASCALContext (Mottaghi et al. 2014), HKU-IS (Li and Yu 2016), SOD (Movahedi and Elder 2010), SIP1K (Fan et al. 2019). |
| Dataset Splits | No | The paper lists total sample counts for datasets (e.g., 'The Birds, Flowers, and Human Matting contain (11, 788), (8, 189), (34, 427) samples, respectively. The THUR15K contains 5 categories and 15000 samples.'), but does not explicitly provide specific percentages, sample counts, or predefined splits for training, validation, and test sets. |
| Hardware Specification | No | The paper specifies network architectures and parameter settings but does not explicitly describe the hardware (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions software components such as 'Deeplab V3+ (backbone: resnet50)' and 'Adam hyperparameters', but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | Parameter setting. The parameters are set as follows: ξ = 1, τ = 1, η = 1, λ = 10, ncritic = 5, the batch size K = 64, Adam hyperparameters for two discriminators α = 0.0001, β1 = 0, β2 = 0.9. The learning rate for the segmentation network and two discriminators are all taken to be 1e 4. The disk strel of radius r is randomly sampled integer between 11 and 55. |