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 [1].
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. |