Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Semantic Segmentation Using Multiple Graphs with Block-Diagonal Constraints
Authors: Ke Zhang, Wei Zhang, Sheng Zeng, Xiangyang Xue
AAAI 2014 | Venue PDF | LLM Run Details | Input Tokens: 15,033 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,391 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 | Experimental results on two real-world image datasets demonstrate the effectiveness of our method. We conduct the experiments on two real-world image datasets MSRC (Shotton et al. 2006) and VOC2007 (Everingham et al. 2007). |
| Researcher Affiliation | Academia | Ke Zhang, Wei Zhang, Sheng Zeng, Xiangyang Xue Shanghai Engineering Research Center for Video Technology and System School of Computer Science, Fudan University, China EMAIL |
| Pseudocode | No | The paper describes the optimization steps using equations and textual explanations, but it does not provide a formal pseudocode or algorithm block labeled as such. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for the source code of the described methodology. |
| Open Datasets | Yes | We conduct the experiments on two real-world image datasets MSRC (Shotton et al. 2006) and VOC2007 (Everingham et al. 2007). |
| Dataset Splits | Yes | The training, validation and test subsets are 45%, 10%, and 45% of the whole image dataset, respectively. (for MSRC-21) and we conduct experiments on the segmentation set with the train-val split including 422 training-validation images and 210 test images (for VOC2007). |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., CPU, GPU models, memory amounts) used for running the experiments. |
| Software Dependencies | No | The paper mentions using MOSEK as a solver ('MOSEK: http://www.mosek.com') but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Parameters α, β, γ are set by 10-fold cross-validation on the training set of each dataset for different segmentations. To get results from different quantization of images, 9 sets of parameters of the mean-shift kernels were randomly chosen as (5;5); (5;7); (5;9); (8;7); (8;9.5); (8;11); (12;10); (12; 15); (12;18). |