Articulated Pose Estimation Using Hierarchical Exemplar-Based Models
Authors: Jiongxin Liu, Yinxiao Li, Peter Allen, Peter Belhumeur
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our method extensively on multiple benchmarks, and conduct diagnostic experiments to show the effect of different components in our method. |
| Researcher Affiliation | Academia | Jiongxin Liu, Yinxiao Li, Peter Allen, Peter Belhumeur Columbia University in the City of New York {liujx09, yli, allen, belhumeur}@cs.columbia.edu |
| Pseudocode | Yes | Algorithm 1: Inference Procedure for Pose Estimation |
| 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 | LSP dataset (Johnson and Everingham 2010) includes 1, 000 images for training and 1, 000 images for testing... CUB-200-2011 bird dataset, which contains 5, 994 images for training and 5, 794 images for testing. |
| Dataset Splits | Yes | To avoid over-fitting, the training is conducted on a held-out validation set that was not used to train the DCNNs. |
| Hardware Specification | No | The paper does not explicitly describe the hardware (e.g., specific GPU or CPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions using "Caffe (Jia et al. 2014)" but does not provide specific version numbers for Caffe or any other software libraries or dependencies. |
| Experiment Setup | Yes | We augment the training data by left-right flipping, and rotating through 360 ... In this experiment, we build a hierarchy of four levels for human body... As the bird body is relatively more rigid than the human body, the degrees of bird s internal nodes can be larger, resulting in fewer levels... we design a three-level hierarchy for birds... To gain an understanding of the effect of the components of our inference algorithm, we evaluate our full method (which will be referred to as Ours-full ), and variants of our method (which will be referred to as Ours-partial , and Ours-no-HIER ). |