Modeling Inter- and Intra-Part Deformations for Object Structure Parsing
Authors: Ling Cai, Rongrong Ji, Wei Liu, Gang Hua
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | 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. |