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..
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 | Venue PDF | 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 EMAIL, EMAIL, EMAIL |
| 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. |