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..
Coarse-to-fine Animal Pose and Shape Estimation
Authors: Chen Li, Gim Hee Lee
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our approach on the Stanford Extra dataset and achieve state-of-the-art results. Furthermore, we test the generalization capacity of our approach on the Animal Pose and BADJA datasets. |
| Researcher Affiliation | Academia | Chen Li Gim Hee Lee Department of Computer Science National University of Singapore EMAIL |
| Pseudocode | No | The paper describes the proposed method in detail but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at the project website1. 1https://github.com/chaneyddtt/Coarse-to-fine-3D-Animal |
| Open Datasets | Yes | We train our model on the Stanford Extra dataset [2], and test on the Stanford Extra, the Animal Pose [6] and the benchmark animal dataset of joint annotations (BADJA) [4] datasets. |
| Dataset Splits | Yes | Each dog-breed is divided into training and testing split with a ratio of 4 : 1. |
| Hardware Specification | Yes | The whole training process takes around 30 hours on a RTX2080Ti GPU. |
| Software Dependencies | No | Our model is implemented with Pytorch. A specific version number for Pytorch or any other software dependency is not provided. |
| Experiment Setup | Yes | We use the Adam optimizer with a learning rate of 10-4 in the first two stages and of 10-5 in the third stage. |