24 citations · 45 across the 3 of their papers we have counts for
12 papers
Interactive Multi-Class Tiny-Object Detection
Chunggi Lee, Seonwook Park, Heon Song +5
Annotating tens or hundreds of tiny objects in a given image is laborious yet crucial for a multitude of Computer Vision tasks. Such imagery typically contains objects from various…
Weakly-Supervised Physically Unconstrained Gaze Estimation
Rakshit Kothari, Shalini De Mello, Umar Iqbal +3
A major challenge for physically unconstrained gaze estimation is acquiring training data with 3D gaze annotations for in-the-wild and outdoor scenarios. In contrast, videos of hum…
Self-Learning Transformations for Improving Gaze and Head Redirection
Yufeng Zheng, Seonwook Park, Xucong Zhang +2
Many computer vision tasks rely on labeled data. Rapid progress in generative modeling has led to the ability to synthesize photorealistic images. However, controlling specific asp…
ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation
Xucong Zhang, Seonwook Park, Thabo Beeler +3
Gaze estimation is a fundamental task in many applications of computer vision, human computer interaction and robotics. Many state-of-the-art methods are trained and tested on cust…
Towards End-to-end Video-based Eye-Tracking
Seonwook Park, Emre Aksan, Xucong Zhang +1
Estimating eye-gaze from images alone is a challenging task, in large parts due to un-observable person-specific factors. Achieving high accuracy typically requires labeled data fr…
Detecting Relevance during Decision-Making from Eye Movements for UI Adaptation
Anna Maria Feit, Lukas Vordemann, Seonwook Park +2
This paper proposes an approach to detect information relevance during decision-making from eye movements in order to enable user interface adaptation. This is a challenging task b…