ELF-UA: Efficient Label-Free User Adaptation in Gaze Estimation
Authors: Yong Wu, Yang Wang, Sanqing Qu, Zhijun Li, Guang Chen
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate the effectiveness of our method on several challenging benchmarks. |
| Researcher Affiliation | Academia | 1Tongji University 2Concordia University |
| Pseudocode | Yes | Algorithm 1 Training for ELF-UA |
| Open Source Code | No | The paper does not provide any explicit statement about releasing the source code or a link to a code repository. |
| Open Datasets | Yes | We re-purpose several existing gaze datasets for our problem, including ETH-XGaze [Zhang et al., 2020], Gaze360 [Kellnhofer et al., 2019], Gaze Capture [Krafka et al., 2016] and MPIIGaze [Zhang et al., 2019]. |
| Dataset Splits | Yes | During offline training (called meta-training using metalearning terminology), we are given a labeled source dataset S = {(xi, yi)}Ns i=1... In addition to the source dataset S, we also have access to an unlabeled person-specific dataset T. This person-specific dataset contains unlabeled images of K persons, where each person has multiple images, i.e. T = {D1, D2, ...DK}... We select 993 persons with over 400 samples per person as the unlabeled person-specific dataset for training. We select 109 persons with over 1000 samples per person are used for testing. For each task, we construct a task with a support set Dtr and query set Dval. The support set Dtr is used to update the model parameter ψ to obtain a user-adapted model ψ . The query set Dval is used to measure the performance of the adapted model ψ . |
| Hardware Specification | Yes | All experiments are performed on a single RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify a version number or other software dependencies with version numbers. |
| Experiment Setup | Yes | We use SGD for the meta-optimization with a fixed learning rate of β = 10 4. The inner optimization is done using 3 gradient steps with an adaptation learning rate of α = 10 2. During meta-testing, we use the same fixed learning rate and 3 gradient steps as meta-optimization to keep consistent with training. We set the weight of the distance between source and target data to γ = 0.1 (Eq. 4). The meta-batch size is set to n = 10 (Eq. 5) and each task consists of K = 5 face images. All images are cropped to the resolution of 224 224. |