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..
Continuous Heatmap Regression for Pose Estimation via Implicit Neural Representation
Authors: Shengxiang Hu, Huaijiang Sun, Dong Wei, Xiaoning Sun, Jin Wang
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on three pose estimation benchmarks: COCO [25], MPII [1], and Crowd Pose [21]. The results show that Ner PE significantly enhances existing heatmap-based methods and obtains superior performance on low-resolution input images. |
| Researcher Affiliation | Academia | 1Nanjing University of Science and Technology, Nanjing, China 2Nantong University, Nantong, China |
| Pseudocode | No | The paper does not contain explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/hushengxiang/Ner PE. |
| Open Datasets | Yes | We conduct extensive experiments on three pose estimation benchmarks: COCO [25], MPII [1], and Crowd Pose [21]. |
| Dataset Splits | Yes | Evaluation on COCO. To evaluate the value of continuous heatmap representation for human pose estimation (HPE), we perform Ner PE with three backbones [16, 42, 24] at three input resolutions on the COCO validation set, as shown in Table 1. |
| Hardware Specification | No | The paper mentions "All our experiments are conducted on an open-source machine learning, Py Torch [35]" and reports GFLOPS (Figure 3), but it does not specify concrete hardware details such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper states "All our experiments are conducted on an open-source machine learning, Py Torch [35]" but does not specify the version number of PyTorch or any other software dependencies. |
| Experiment Setup | Yes | In the main experimental results, the training settings of Ner PE is consistent with the comparison methods [48, 42, 24] based on discrete heatmap regression. We use the Adam optimizer [18] for training, in which the learning rate is initialized to 1e 3 and decreased to 1e 4 and 1e 5. The data augmentation used includes random rotation, random scale, image flipping, and half body cropping. |