EyeCue detects driver cognitive distraction by modeling gaze-visual context interactions in egocentric videos and achieves 74.38% accuracy on the new CogDrive dataset, outperforming 11 baselines.
Egovideo: Exploring egocentric founda- tion model and downstream adaptation
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3roles
method 1polarities
use method 1representative citing papers
Introduces an interactive episodic memory task with user feedback and a Feedback Alignment Module that improves retrieval accuracy on video benchmarks while remaining efficient.
EggHand unifies VLA action decoding with viewpoint-aware video-text encoding to forecast egocentric hand poses, achieving SOTA accuracy on EgoExo4D while remaining robust to ego-motion and controllable via language prompts.
citing papers explorer
-
EyeCue: Driver Cognitive Distraction Detection via Gaze-Empowered Egocentric Video Understanding
EyeCue detects driver cognitive distraction by modeling gaze-visual context interactions in egocentric videos and achieves 74.38% accuracy on the new CogDrive dataset, outperforming 11 baselines.
-
Interactive Episodic Memory with User Feedback
Introduces an interactive episodic memory task with user feedback and a Feedback Alignment Module that improves retrieval accuracy on video benchmarks while remaining efficient.
-
EggHand: A Multimodal Foundation Model for Egocentric Hand Pose Forecasting
EggHand unifies VLA action decoding with viewpoint-aware video-text encoding to forecast egocentric hand poses, achieving SOTA accuracy on EgoExo4D while remaining robust to ego-motion and controllable via language prompts.