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..
Localization with Sampling-Argmax
Authors: Jiefeng Li, Tong Chen, Ruiqi Shi, Yujing Lou, Yong-Lu Li, Cewu Lu
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the bene๏ฌts of the proposed sampling-argmax with experiments on a variety of localization tasks, including human pose estimation, retina segmentation and object keypoint estimation. |
| Researcher Affiliation | Academia | Jiefeng Li Tong Chen Ruiqi Shi Yujing Lou Yong-Lu Li Cewu Lu Shanghai Jiao Tong University EMAIL |
| Pseudocode | No | The paper does not include a dedicated section or figure explicitly labeled "Pseudocode" or "Algorithm". |
| Open Source Code | Yes | Code is available at https://github.com/Jeff-sjtu/sampling-argmax. |
| Open Datasets | Yes | The experiments are conducted on the large-scale in-the-wild 2D human pose benchmark COCO Keypoint [19]. We further evaluate the proposed sampling-argmax on Human3.6M [10], an indoor benchmark for 3D human pose estimation. |
| Dataset Splits | No | While the paper states "Training details of all tasks are provided in the supplemental material," it does not explicitly provide the specific training/validation/test splits within the main body of the paper. |
| Hardware Specification | Yes | The required training resources (number of GPUs) are elaborated in the supplemental material. |
| Software Dependencies | No | The paper states "Training details of all tasks are provided in the supplemental material," but does not explicitly list specific software dependencies with version numbers in the main text. |
| Experiment Setup | Yes | In our experiment, we tune the loss weight ranging from 0.1 to 10 and the variance ranging from 1 to 5 for each task. |