Image-level to Pixel-wise Labeling: From Theory to Practice
Authors: Tiezhu Sun, Wei Zhang, Zhijie Wang, Lin Ma, Zequn Jie
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |