arXiv Computer Vision
Knowing When to Trust a Prior: Reliability-Gated Cue Fusion for Video Gaze Prediction
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv Computer Vision
Oct 1
GazeFlow: From Human Gaze Behavior to Generative Egocentric Gaze Prediction
GazeFlow is a new framework for egocentric gaze prediction that models gaze as a joint distribution of temporal positions conditioned on both top‑down task cues and bottom‑up visual saliency. It employs conditional flow matching to iteratively transform Gaussian noise into realistic gaze trajectories, using a velocity field informed by video‑encoded visual features and global task queries. On standard benchmarks, GazeFlow outperforms existing methods on per‑frame metrics and produces trajectories that better reflect human gaze dynamics.
arXiv Computer Vision
5d ago
GazeLens for Efficient Egocentric Video Question Answering with Simple Gaze-Driven Adaptive Token Reduction
arXiv:2610.28502v1 Announce Type: new Abstract: Smart glasses can help users understand their activities and surroundings, but small vision-language models must preserve useful evidence within limite...
arXiv Computer Vision
Oct 2
EyeTAG: Eye Trajectory-Aware Gaze Estimation
arXiv:2610.00922v1 Announce Type: new Abstract: Gaze estimation under natural head-eye motion underpins applications from driver monitoring to human-computer interaction. Single-frame methods predict...
arXiv Computer Vision
Sep 14
Context-Aware Causal Gaze Forecasting for Human-Vehicle Interaction During In-Cabin Tracking Dropouts
The paper introduces the Causal Context-Gated Forecaster (CCGF) for predicting a driver's gaze during dashboard-mounted tracker dropouts. CCGF uses a 60‑frame history of gaze and head pose combined with DINOv3 scene features, and a learned reliability gate adjusts the influence of these inputs as the dropout progresses. Experiments on 2,047 naturalistic driving events show that live scene updates reduce median error by 33% compared to history‑only forecasting, while frozen scene input yields higher error, demonstrating the value of real‑time scene information.
arXiv Machine Learning
Aug 11
Can Webcam Gaze Constrain Mesa-Objectives in Driving Models? An Instrument Precision Analysis
arXiv:2608. 08947v1 Announce Type: cross Abstract: Current hazard detection systems in autonomous driving may develop mesa objectives, learned internal goals that achieve high training performance through spurious correlations rather than genuine hazard recognition.
arXiv Computer Vision
Oct 5
Moving Forward with Video Saliency: A New Dataset and Benchmark where Motion Matters
The paper introduces SalTempto, a new video saliency benchmark featuring 224 highly dynamic clips from HACS‑Segments, each with event context and gaze data from up to 16 subjects. It shows that static baselines perform poorly on SalTempto compared to LEDOV, while fine‑tuned temporal models achieve significant gains, yet still leave a large portion of explainable gaze information unaccounted for. The dataset highlights the need for better temporal modeling in video saliency research.