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..
Bootstrap AutoEncoders With Contrastive Paradigm for Self-supervised Gaze Estimation
Authors: Yaoming Wang, Jin Li, Wenrui Dai, Bowen Shi, Xiaopeng Zhang, Chenglin Li, Hongkai Xiong
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that the proposed approaches outperform state-of-the-art unsupervised gaze approaches on extensive datasets (including wild scenes) under both within-dataset and cross-dataset protocols. |
| Researcher Affiliation | Collaboration | 1School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China 2Huawei Inc, Shenzhen, China. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability for the described methodology. |
| Open Datasets | Yes | Datasets. We perform our self-supervised experiments on 4 gaze datasets: Columbia Gaze (Smith et al., 2013), MPIIFace Gaze (Zhang et al., 2017b), Gaze360 (Kellnhofer et al., 2019) and ETH-Xgaze (Zhang et al., 2020). |
| Dataset Splits | Yes | Following the convention, a 5-fold evaluation protocol is adopted for Columbia. MPIIFace Gaze (MPII)...evaluated with a leave-one-out evaluation protocol. |
| Hardware Specification | Yes | We implement the codes with the Pytorch (Paszke et al., 2019) framework and use 4 Nvidia-V100 GPUs for training. |
| Software Dependencies | No | We implement the codes with the Pytorch (Paszke et al., 2019) framework". While PyTorch is mentioned with a citation, a specific version number is not provided, nor are versions for other key software components. |
| Experiment Setup | Yes | An Adam W optimizer and a cosine decay learning rate schedule are used with the initial learning rate settled as 4 10 4 for Res Net and 1.5 10 4 for Vi T-tiny. A 0.05 weight-decay is also employed and we warm up the training process with 10 epochs and then train the model for 190 epochs (The total epochs are 200). |