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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Image-level to Pixel-wise Labeling: From Theory to Practice
Authors: Tiezhu Sun, Wei Zhang, Zhijie Wang, Lin Ma, Zequn Jie
IJCAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 14,450 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,819 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results on benchmark dataset demonstrate the effectiveness of the proposed method, where good image-level labels can significantly improve the pixel-wise segmentation accuracy. |
| Researcher Affiliation | Collaboration | 1School of Control Science and Engineering, Shandong University 2Tencent AI Lab, Shenzhen, China |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | In this section, the evaluation is conducted on the benchmark segmentation dataset PASCAL VOC 2012 [Everingham et al., 2010], which consists of 21 classes of objects (including background). Similar to [Zhao et al., 2016], we use the augmented data of PASCAL VOC 2012 with annotation of [Hariharan et al., 2011] resulting 11,295, 736, 1456 samples for training, validation and testing, respectively. |
| Dataset Splits | Yes | resulting 11,295, 736, 1456 samples for training, validation and testing, respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions optimizers (SGD and Adam) and learning rates but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | In the training stage, SGD and Adam were employed as optimizers to train the segmentation and generative networks with the same learning rate of 10-10, respectively. The iteration number 100,000 is set for all experiments. |