Localization with Sampling-Argmax
Authors: Jiefeng Li, Tong Chen, Ruiqi Shi, Yujing Lou, Yong-Lu Li, Cewu Lu
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the beneļ¬ts 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 {ljf_likit,chentong1023, gzfoxie, louyujing, yonglu_li, lucewu}@sjtu.edu.cn |
| 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. |