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- GazeGene: Large-scale Synthetic Gaze Dataset with 3D Eyeball Annotations
In this section, we conduct extensive cross-domain evalua- tions to verify the domain gap between GazeGene and other real-world datasets Random translation and color jitter is applied for all datasets
- [2411. 19913] Quantifying the synthetic and real domain gap in aerial . . .
This paper introduces a novel methodology for scene complexity assessment using Multi-Model Consensus Metric (MMCM) and depth-based structural metrics, enabling a robust evaluation of perceptual and structural disparities between domains
- Mind the (domain) gap: Metrics for the diferences in synthetic and real . . .
Synthetic data are frequently used to augment a dataset with the goal of improving a model’s ability to generalize to unseen data 1 This generalization is commonly referred to as invariance and equivariance; equivariance refers to a change model’s output correctly correlated to a change of the model’s input, and invariance is a special
- GazeGene Dataset - phi-ai. buaa. edu. cn
Limitations in annotation accuracy and variety have constrained the progress in research and development of deep-learning methods for appearance-based gaze-related tasks In this paper, we
- Cross-Dataset Generalization: Bridging the Gap Between Real and . . .
From our per-spective, a better understanding of the scenes and scenarios could bridge the gap between the real and the synthetic data - which leads to our purpose for this paper
- TOWARDS A METHODOLOGY FOR SYNTHETIC DATA GENERATION, DOMAIN GAP . . .
Using a model trained on source dataset on other target dataset using only unlabeled target data Domain Gap must be taken into account using data sources like existing datasets or synthetic data Enough target NO domain data ? Domain gap ?
- Quantifying the Synthetic and Real Domain Gap in Aerial Scene Understanding
Analyzing the synthetic-to-real domain gap in aerial scene understanding is essential for identifying limitations in existing datasets, improving synthetic dataset design, and guiding model development to enhance real-world applicability
- Gaze Estimation and GANs: Driving Model Performance with Synthetic Data . . .
Today, we begin with refinement for the same special case of eye gaze estimation that kickstarted synthetic data refinement a few years ago and still remains an important success story for this approach, but then continue and extend the story of refinement to other computer vision problems
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