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..
Articulated Pose Estimation Using Hierarchical Exemplar-Based Models
Authors: Jiongxin Liu, Yinxiao Li, Peter Allen, Peter Belhumeur
AAAI 2016 | Venue PDF | 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 EMAIL |
| 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 ). |