arXiv AI
Jul 14
Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos
arXiv:2607. 11523v1 Announce Type: cross Abstract: When should an intelligent assistant speak up without being asked?
MAND: Modality-Aware Novelty Detection for Open-World Egocentric Activity Recognition
arXiv:2603. 16970v2 Announce Type: replace-cross Abstract: Multimodal egocentric activity recognition integrates visual and inertial cues for robust first-person behavior understanding.
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arXiv Machine Learning
Jun 19
UNIEGO: Proxies as Mediators for Unified Egocentric Video Representation Learning
arXiv:2606. 20559v1 Announce Type: cross Abstract: Egocentric video understanding is inherently limited by the narrow perspective of wearable cameras: a single viewpoint, a single modality, a single model cannot capture the full richness of human action.
Hugging Face Trending Papers
Jul 13
Vinci2: Providing Proactive Assistance in Continuous Egocentric Videos
When should an intelligent assistant speak up without being asked? Continuous egocentric video offers rich, evolving context that enables a new form of assistance: one that is proactive rather than merely reactive.
arXiv AI
Jul 21
Joint-Embedding Predictive Architecture for Sensor-based Activity Recognition
arXiv:2607. 16350v1 Announce Type: cross Abstract: Sensor-based human activity recognition (HAR) has achieved significant progressed in fully supervised learning settings.
arXiv Machine Learning
Jul 31
Regularizing modality contribution drift in multimodal continual learning
arXiv:2607. 27260v1 Announce Type: new Abstract: Multimodal continual learning (MMCL) aims to learn emerging knowledge from multimodal data while preserving knowledge.
arXiv AI
Jun 2
Understanding-Enhanced Model Collaboration for Long-Tailed Egocentric Mistake Detection
arXiv:2606. 02120v1 Announce Type: cross Abstract: In this report, we address the problem of determining whether a user performs an action incorrectly from egocentric video data.
arXiv Machine Learning
Aug 3
TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners
arXiv:2607. 29592v1 Announce Type: cross Abstract: The primary challenge of continual learning (CL) systems is to learn new tasks while remaining performant on previously learned tasks.
arXiv Machine Learning
Jun 29
A Comparison of Fusion Techniques for Multi-Modal Human Activity Recognition on the HARMES Dataset
arXiv:2606. 27886v1 Announce Type: new Abstract: Recent advances in Human Activity Recognition (HAR) from wearable sensors have shown that multi-modal deep learning models consistently outperform their uni-modal counterparts.
arXiv AI
Jun 29
EXPLORE-Bench: Egocentric Scene Prediction with Long-Horizon Reasoning
arXiv:2603. 09731v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are increasingly considered as a foundation for embodied agents, yet it remains unclear whether they can reliably reason about the long-term physical consequences of actions from an egocentric viewpoint.
Hugging Face Trending Papers
Jul 9
Do Egocentric Video-Language Models Capture Both Hand- and Object-Centric Cues?
Hand-object interaction (HOI) recognition requires capturing both hand manipulations and object transformations. However, existing video-language models often fall into shortcuts by relying on spurious correlations among hands, objects, or environmental context, rather than reasoning from the appearance and dynamics of hands and objects themselves.
arXiv AI
Jul 1
SkillSpotter: Pose-Aware Multi-View Skilled Action Detection and Grading in Ego-Exo Videos
arXiv:2606. 31127v1 Announce Type: cross Abstract: To enable personalized, real-time coaching using Augmented Reality glasses or fixed camera setups in domains such as sports, cooking, or music, a system must understand not just what a person does, but how well they execute an activity.
arXiv AI
6d ago
Glance, Scrutinize, and Think: Advancing Video Anomaly Detection from Training-Free to Agentic Reasoning
arXiv:2608. 11260v1 Announce Type: new Abstract: Video Anomaly Detection (VAD) aims to identify anomalous events and localize their temporal intervals.