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..
Weakly Supervised RBM for Semantic Segmentation
Authors: Yong Li, Jing Liu, Yuhang Wang, Hanqing Lu, Songde Ma
IJCAI 2015 | Venue PDF | 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 |