Weakly Supervised RBM for Semantic Segmentation
Authors: Yong Li, Jing Liu, Yuhang Wang, Hanqing Lu, Songde Ma
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two real-world datasets demonstrate the good performance of our approach compared with some state-of-the-art methods. |
| Researcher Affiliation | Academia | Yong Li, Jing Liu, Yuhang Wang, Hanqing Lu, Songde Ma National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1 CDWS |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We evaluate our algorithm on two real world datasets, PASCAL VOC 2007 dataset (PASCAL for short) [Everingham et al., 2010] and Label Me dataset [Russell et al., 2008; Liu et al., 2009a]. |
| Dataset Splits | Yes | We conduct experiments on the segmentation set with the train-val split including 422 training-validation images and 210 test images. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions methods like SIFT and Grabcut but does not list specific software dependencies with version numbers (e.g., Python, PyTorch, or specific libraries). |
| Experiment Setup | Yes | where α and β are the tradeoff parameters of the proposed two terms. ... learning rate ε, maximum epoch number Ech |