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..
Keypoints as Dynamic Centroids for Unified Human Pose and Segmentation
Authors: Niaz Ahmad, Jawad Khan, Kang G. Shin, Youngmoon Lee, Guanghui Wang
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluations focus on crowded and occluded cases using the Crowd Pose, OCHuman, and COCO benchmarks, demonstrating KDC s effectiveness and generalizability in challenging scenarios in terms of both accuracy and runtime performance. |
| Researcher Affiliation | Academia | Niaz Ahmad1 , Jawad Khan2 , Kang G. Shin3 , Youngmoon Lee4 and Guanghui Wang1 1Department of Computer Science, Toronto Metropolitan University, Canada 2 School of Computing, Gachon University, Republic of Korea, 3Department of Electrical Engineering and Computer Science, University of Michigan, USA 4 Department of Robotics, Hanyang University, Republic of Korea EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes its technical approach in text and mathematical formulas across sections 3.1, 3.2, and 3.3. There are no explicitly labeled pseudocode or algorithm blocks presented in the paper. |
| Open Source Code | Yes | Our implementation is available at: https://sites.google.com/view/ niazahmad/projects/kdc. |
| Open Datasets | Yes | We evaluate KDC on COCO [Lin et al., 2014], Crowd Pose [Li et al., 2019], and OCHuman [Zhang et al., 2019] benchmarks. |
| Dataset Splits | Yes | The model is trained end-to-end using the COCO keypoint and segmentation training set, and ablations are conducted on the COCO val set. |
| Hardware Specification | Yes | Models are tested on a single Titan RTX. |
| Software Dependencies | No | The paper mentions "Adam optimizer is employed" but does not specify any software libraries or frameworks (e.g., PyTorch, TensorFlow) with version numbers. |
| Experiment Setup | Yes | Hyperparameters for training are: learning rate = 0.1 e 4, image size = 401 401, batch size = 4, training epochs = 400, and Adam optimizer is employed. Various transformations are applied during model training, such as scale, flip, and rotate operations. Unless otherwise specified, a disk DR s radius is set to be R = 32. |