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..
PoseHD: Boosting Human Detectors Using Human Pose Information
Authors: Zhijian Liu, Bowen Pan, Yuliang Xiu, Cewu Lu
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results on multiple pedestrian benchmark datasets validate that our proposed Pose HD framework can generally improve the overall performance of recent state-of-the-art human detectors (by 2-4% in both m AP and MR metrics). |
| Researcher Affiliation | Academia | Shanghai Jiao Tong University, Shanghai, China 200240 |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code for its methodology. |
| Open Datasets | Yes | The performance of Pose HD is evaluated across three pedestrian benchmark datasets: INRIA (Dalal and Triggs 2005), ETH (Ess, Leibe, and Van Gool 2007) and PASCAL VOC 2007 (Everingham et al. 2010). |
| Dataset Splits | No | The paper mentions training and testing on datasets but does not provide specific training/validation/test dataset splits (e.g., percentages or counts). |
| Hardware Specification | Yes | We use Tensor Flow (Abadi et al. 2016) and Nvidia Titan X GPU to train the classification network. |
| Software Dependencies | No | The paper mentions software like Tensor Flow, Adam, and Lambda MART, but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | We initialize the weights randomly from scratch, and the optimization is carried out using Adam (Kingma and Ba 2015) with β1 = 0.9 and β2 = 0.999. We use a fixed learning rate of γ = 10 3 and mini-batch size of 128. |