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..
Referring Human Pose and Mask Estimation In the Wild
Authors: Bo Miao, Mingtao Feng, Zijie Wu, Mohammed Bennamoun, Yongsheng Gao, Ajmal Mian
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that Uni PHD produces quality results based on user-friendly prompts and achieves top-tier performance on Ref Human val and MS COCO val2017. |
| Researcher Affiliation | Academia | 1University of Western Australia 2Xidian University 3Hunan University 4Griffith University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks, or clearly labeled algorithm sections. |
| Open Source Code | Yes | https://github.com/bo-miao/Ref Human |
| Open Datasets | Yes | We substantially extend COCO [40] to construct the Ref Human dataset. It contains pose and mask annotations for humans along with text and positional prompts to facilitate the new task of R-HPM. |
| Dataset Splits | Yes | To construct Ref Human train set, we annotate prompts for all humans in MS COCO train2017 set with at least three surrounding people, a minimum of eight visible keypoints, and an area ratio of at least 2%. For the Ref Human val set, we annotate humans in MS COCO val2017 set, excluding those with non-visible keypoints or an area ratio below 1%, as instances below this threshold are often not visually clear and difficult to describe accurately. |
| Hardware Specification | Yes | FPS is measured on RTX 3090 with a batch size of 24. |
| Software Dependencies | No | The paper mentions software components like RoBERTa and Swin Transformer but does not specify exact version numbers for general software dependencies or libraries (e.g., Python, PyTorch/TensorFlow versions). |
| Experiment Setup | Yes | We use the Adam W [48] optimizer with a weight decay of 1 10 4 and train our models on 24GB RTX 3090 GPUs with batch size 16 for 20 epochs. The initial learning rates are set to 1 10 5 for the visual encoder and 1 10 4 for other components, with a rate decay at the 18th epoch by a factor of 10. |