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Found 20 papers, 8 papers with code
Domain-Adaptive Full-Face Gaze Estimation via Novel-View-Synthesis and Feature Disentanglement
To bridge the inevitable gap between synthetic and real images, we further propose an unsupervised domain adaptation method suitable for synthetic full-face data.
Rotation-Constrained Cross-View Feature Fusion for Multi-View Appearance-based Gaze Estimation
This work proposes a generalizable multi-view gaze estimation task and a cross-view feature fusion method to address this issue.
Learning Video-independent Eye Contact Segmentation from In-the-Wild Videos
In this work, we address the task of one-way eye contact detection for videos in the wild.
Learning-by-Novel-View-Synthesis for Full-Face Appearance-Based 3D Gaze Estimation
Despite recent advances in appearance-based gaze estimation techniques, the need for training data that covers the target head pose and gaze distribution remains a crucial challenge for practical deployment.
Interact Before Align: Leveraging Cross-Modal Knowledge for Domain Adaptive Action Recognition
Different from previous works, we find that the cross-domain alignment can be more effectively done by using cross-modal interaction first.
Stacked Temporal Attention: Improving First-person Action Recognition by Emphasizing Discriminative Clips
Previous works explored to address this problem by applying temporal attention but failed to consider the global context of the full video, which is critical for determining the relatively significant parts.
Ego4D: Around the World in 3,000 Hours of Egocentric Video
We introduce Ego4D, a massive-scale egocentric video dataset and benchmark suite.
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2021: Team M3EM Technical Report
In this report, we describe the technical details of our submission to the 2021 EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition.
DRIV100: In-The-Wild Multi-Domain Dataset and Evaluation for Real-World Domain Adaptation of Semantic Segmentation
Together with the recent advances in semantic segmentation, many domain adaptation methods have been proposed to overcome the domain gap between training and deployment environments.
Shape-conditioned Image Generation by Learning Latent Appearance Representation from Unpaired Data
Conditional image generation is effective for diverse tasks including training data synthesis for learning-based computer vision.
Light Structure from Pin Motion: Simple and Accurate Point Light Calibration for Physics-based Modeling
We present a practical method for geometric point light source calibration.
A Multimodal Corpus of Expert Gaze and Behavior during Phonetic Segmentation Tasks
Phonetic segmentation is the process of splitting speech into distinct phonetic units.
MPIIGaze: Real-World Dataset and Deep Appearance-Based Gaze Estimation
Second, we present an extensive evaluation of state-of-the-art gaze estimation methods on three current datasets, including MPIIGaze.
It's Written All Over Your Face: Full-Face Appearance-Based Gaze Estimation
Eye gaze is an important non-verbal cue for human affect analysis.
Seeing with Humans: Gaze-Assisted Neural Image Captioning
Gaze reflects how humans process visual scenes and is therefore increasingly used in computer vision systems.
3D Gaze Estimation from 2D Pupil Positions on Monocular Head-Mounted Eye Trackers
3D gaze information is important for scene-centric attention analysis but accurate estimation and analysis of 3D gaze in real-world environments remains challenging.
Labeled pupils in the wild: A dataset for studying pupil detection in unconstrained environments
We further study the influence of image resolution, vision aids, as well as recording location (indoor, outdoor) on pupil detection performance.
Rendering of Eyes for Eye-Shape Registration and Gaze Estimation
Images of the eye are key in several computer vision problems, such as shape registration and gaze estimation.
Appearance-Based Gaze Estimation in the Wild
Appearance-based gaze estimation is believed to work well in real-world settings, but existing datasets have been collected under controlled laboratory conditions and methods have been not evaluated across multiple datasets.
Learning-by-Synthesis for Appearance-based 3D Gaze Estimation
Unlike existing appearance-based methods that assume person-specific training data, we use a large amount of cross-subject training data to train a 3D gaze estimator.
