On the Calibration of Human Pose Estimation
Authors: Kerui Gu, Rongyu Chen, Xuanlong Yu, Angela Yao
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate pose estimation tasks on three benchmarks: MSCOCO (Lin et al., 2014), MPII (Andriluka et al., 2014), and MSCOCOWhole Body (Jin et al., 2020). For the downstream tasks, we evaluate the 3D fitting task on 3DPW (Von Marcard et al., 2018). ... Results in Tab. 2 show that our simple yet effective method gives improvements across varying backbones, learning pipelines, and scoring functions. |
| Researcher Affiliation | Academia | 1School of Computing, National University of Singapore 2U2IS, ENSTA Paris, IP Paris. |
| Pseudocode | Yes | Algorithm 1 CCNet Pseudocode, Py Torch-like |
| Open Source Code | No | The project page is at https://comp.nus.edu.sg/ keruigu/calibrate pose/ project.html. While a project page is provided, it is not an explicit statement of source code release for the methodology described, nor is it a direct link to a code repository. |
| Open Datasets | Yes | We evaluate pose estimation tasks on three benchmarks: MSCOCO (Lin et al., 2014), MPII (Andriluka et al., 2014), and MSCOCO-Whole Body (Jin et al., 2020). |
| Dataset Splits | Yes | MSCOCO consists of 250k person instances annotated with 17 keypoints. We evaluate the model with m AP over the standard 10 OKS thresholds. We also evaluate on MPII with the Percentage of Correct Keypoint (PCK) and on MSCOCO-Whole Body, which includes face and hand keypoints. We evaluate pose estimation tasks on three benchmarks: MSCOCO (Lin et al., 2014), MPII (Andriluka et al., 2014), and MSCOCO-Whole Body (Jin et al., 2020). |
| Hardware Specification | No | The paper does not provide specific details on the hardware (e.g., GPU/CPU models, memory) used to conduct the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch-like' pseudocode and the 'Adam (Kingma & Ba, 2014) optimizer' but does not specify version numbers for any software libraries or dependencies. |
| Experiment Setup | Yes | The initial learning rate is 1e 3, multiplied by 0.1 in the 9K-th step, and results are reported for 12K steps. |