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..
Learning a Generalized Gaze Estimator from Gaze-Consistent Feature
Authors: Mingjie Xu, Haofei Wang, Feng Lu
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our proposed method achieves state-of-the-art performance on gaze domain generalization task. Furthermore, our proposed method also improves domain adaption performance on gaze estimation. |
| Researcher Affiliation | Academia | 1State Key Laboratory of VR Technology and Systems, School of CSE, Beihang University 2Peng Cheng Laboratory, Shenzhen, China EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 describes the training procedure of our proposed method. |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code for the methodology or a link to a code repository. |
| Open Datasets | Yes | In this paper, we use 4 commonly-used gaze datasets, i.e., ETH-XGaze (DE) (Zhang et al. 2020), Gaze360 (DG) (Kellnhofer et al. 2019), MPIIGaze (DM) (Zhang et al. 2017a) and Eye Diap (DD) (Funes Mora, Monay, and Odobez 2014). |
| Dataset Splits | Yes | ETH-XGaze contains 80 subjects, and we use data from 75 subjects for training (713646 images) and the rest 5 subjects data for validation. |
| Hardware Specification | No | The paper states: "We use a single NVIDIA GPU to run the experiments." This is not specific enough to identify the exact GPU model or other hardware components required for a 'Yes' answer. |
| Software Dependencies | No | The paper mentions using "Res Net-18 (He et al. 2016) as backbone" and "Adam optimizer", and implicitly "Py Torch". However, it does not provide specific version numbers for these software components, which is required for a 'Yes' answer. |
| Experiment Setup | Yes | All the images are resized to 224x224 and normalized to [0, 1]. The batch size is 128, and we train the model for 10 epochs for ETH-XGaze and 100 epochs for Gaze360. We use Adam optimizer with a learning rate of 10^-4, and set β1 = 0.9, β2 = 0.95. |