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..
Modeling Inter- and Intra-Part Deformations for Object Structure Parsing
Authors: Ling Cai, Rongrong Ji, Wei Liu, Gang Hua
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two benchmark datasets (i.e., faces and horses) demonstrate that the proposed model yields superior parsing performance over state-of-the-art models. |
| Researcher Affiliation | Collaboration | 1 Xiamen University, China 2 IBM T. J. Watson Research Center, USA 3 Stevens Institute of Technology, USA |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It describes the algorithms and procedures in paragraph form. |
| Open Source Code | Yes | MATLAB codes can be downloaded from Ling Cai s homepage at: https://sites.google.com/site/lingcai2006sjtu/parsing |
| Open Datasets | Yes | The proposed model1 is evaluated on Kaggle face dataset [KAG, 2013] and the Weizmann horse dataset [Borenstein and Ullman, 2008]. |
| Dataset Splits | Yes | 100 facial images are used to estimate the model parameter D and w. Test is made on 250 images, and the results from our model are visualized in the first row of Fig. 2. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as CPU or GPU models, or memory specifications. |
| Software Dependencies | No | The paper mentions "MATLAB codes" but does not specify any software dependencies with version numbers (e.g., MATLAB version, specific libraries, or frameworks). |
| Experiment Setup | No | The paper does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs, optimizer settings) or other training configuration parameters. |