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
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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.
Fine-tuned VLMs guided by eye gaze and ego motion achieve 14.5% accuracy improvement over a transformer baseline for egocentric pedestrian intent decoding.
HiERO-StepG augments weakly-supervised HiERO features with level agreement, temporal monotonicity, and noise reduction to reach 56.27% R@1 (IoU=0.3) and second place on the Ego4D Step Grounding challenge without procedure annotations.
citing papers explorer
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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.
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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.
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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.
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Decoding Pedestrian Crossing Intention from Egocentric Vision via Vision Language Models
Fine-tuned VLMs guided by eye gaze and ego motion achieve 14.5% accuracy improvement over a transformer baseline for egocentric pedestrian intent decoding.
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HiERO-StepG @ Ego4D Step Grounding Challenge: hierarchical activity understanding enables zero-shot step grounding
HiERO-StepG augments weakly-supervised HiERO features with level agreement, temporal monotonicity, and noise reduction to reach 56.27% R@1 (IoU=0.3) and second place on the Ego4D Step Grounding challenge without procedure annotations.