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 | Archive PDF | Plain Text | 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. |