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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Context-Guided Adaptive Network for Efficient Human Pose Estimation
Authors: Lei Zhao, Jun Wen, Pengfei Wang, Nenggan Zheng3492-3499
AAAI 2021 | Venue PDF | LLM Run Details | Input Tokens: 18,136 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,072 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimenting on the COCO dataset, our method achieves 68.1 AP at 25.4 fps, and outperforms Mask R-CNN by 8.9 AP at a similar speed. The competitive performance on the HPE and person instance segmentation tasks over the state-of-the-art models show the promise of the proposed method. |
| Researcher Affiliation | Academia | 1 Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China 2 College of Computer Science and Techology, Zhejiang University, Hangzhou, China 3 Collaborative Innovation Center for Artificial Intelligence by MOE and Zhejiang Provincial Government (ZJU) 4 Zhejiang Lab, Hangzhou, China |
| Pseudocode | Yes | Algorithm 1 Dichotomy Extended Area (one box, upper boundary). |
| Open Source Code | Yes | The source code will be made available at https://github.com/zlcnup/CGANet. |
| Open Datasets | Yes | In this section, we evaluate our approach on the COCO dataset (Lin et al. 2014), which contains over 200, 000 images and 250, 000 person instances labeled with 17 keypoints. |
| Dataset Splits | Yes | It is divided into train2017/val2017/test-dev2017 sets with 57k, 5k and 20k images respectively. |
| Hardware Specification | Yes | We report the inference time (speed) of models using one batch size on the same environment equipped with a single NVIDIA GTX 2080Ti GPU |
| Software Dependencies | Yes | CUDA V10.0 and Py Torch 1.4 |
| Experiment Setup | Yes | We use data augmentation with random scale between 0.6 1.5, random rotation between 45 +45 , random translation between 40 +40 and random flip to crop an input image patch. The aligned feature sizes are 1 16, 1 32 and 1 64 of the training input size, respectively. We use the SGD optimizer for 95 epochs, with an initial learning rate of 1e-2 (dropped to 1e-3 and 1e-4 at the 70th and 85th epochs, respectively). |