Denoising Diffusion Models are Good General Gaze Feature Learners
Authors: Guanzhong Zeng, Jingjing Wang, Pengwei Yin, Zefu Xu, Mingyang Zhou
IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate denoising diffusion models are also good general gaze feature learners. ... 5 Experiments |
| Researcher Affiliation | Collaboration | 1Hikvision Research Institute 2Shenzhen University |
| Pseudocode | No | The paper describes methods in paragraph text and uses diagrams, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain an explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | Gaze Diff is a general self-supervised representation learning framework. Therefore, we use the following datasets in our experiments as previous methods do: ETH-XGaze [Zhang et al., 2020], EVE [Park et al., 2020], Gaze360 [Kellnhofer et al., 2019], Gaze Capture [Krafka et al., 2016], Columbia Gaze [Smith et al., 2013], MPIIFace Gaze [Zhang et al., 2017b], Eye Diap [Funes Mora et al., 2014] and VGG-Face2 [Cao et al., 2018]. |
| Dataset Splits | Yes | For the self-supervised methods, we pre-train in DX, then freeze the pre-trained learner G and add a FC layer as gaze regressor M for linear-probe analysis following [Chen et al., 2020]. We use different proportions of annotated training data to adapt M and evaluate on the separate validation data. ... Specifically, we use the leave-one-out (15-fold) and 5-fold cross-validation for DD and DO. In each fold, we randomly select 50 samples with labels to fine-tune M and repeat each experiment for 10 times. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU models, CPU types, memory amounts) used for running the experiments in the main text. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., Python 3.8, PyTorch 1.9, CUDA 11.1) needed to replicate the experiments. |
| Experiment Setup | No | The paper mentions training models and fine-tuning, but does not provide specific experimental setup details such as concrete hyperparameter values (e.g., learning rate, batch size, number of epochs), optimizer settings, or other system-level training configurations in the main text. |