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..
UVAGaze: Unsupervised 1-to-2 Views Adaptation for Gaze Estimation
Authors: Ruicong Liu, Feng Lu
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results show that a single-view estimator, when adapted for dual views, can achieve much higher accuracy, especially in cross-dataset settings, with a substantial improvement of 47.0%. |
| Researcher Affiliation | Academia | Ruicong Liu, Feng Lu* State Key Laboratory of VR Technology and Systems, School of CSE, Beihang University, Beijing, China EMAIL |
| Pseudocode | Yes | Algorithm 1: Unsupervised 1-to-2 views adaptation. Input: Dual-camera input D, pre-training dataset Dpre and G pre-trained on Dpre Output: G( |Θ) |
| Open Source Code | Yes | Project page: https://github.com/ Mickey LLG/UVAGaze. |
| Open Datasets | Yes | We pre-train using two datasets: ETH-XGaze (Zhang et al. 2020) and Gaze360 (Kellnhofer et al. 2019). We have named the fine-tuned dataset as ETH-MV (Multi-View), which is the foundation for all our experiments. The ETH-MV is available at the project page. |
| Dataset Splits | No | The paper mentions pre-training datasets and dual-camera input for adaptation, and describes splitting cameras into pairs, but does not specify explicit train/validation/test splits (e.g., percentages or counts) for the data used in the adaptation process. |
| Hardware Specification | Yes | We use Py Torch on an NVIDIA 3090 GPU. |
| Software Dependencies | No | We use Py Torch on an NVIDIA 3090 GPU. The paper mentions PyTorch but does not specify its version or any other software dependencies with version numbers. |
| Experiment Setup | Yes | Pre-training employs the Adam optimizer at a learning rate of 10 4, while UVA uses 10 5. empirically, we set λ1 = 50 and λ2 = 10. |