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 [1].
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. |