QUB-PHEO: A Visual-Based Dyadic Multi-View Dataset for Intention Inference in Collaborative Assembly
원문 초록
QUB-PHEO introduces a visual-based, dyadic dataset with the potential of advancing human-robot interaction (HRI) research in assembly operations and intention inference. This dataset captures rich multimodal interactions between two participants, one acting as a 'robot surrogate,' across a variety of assembly tasks that are further broken down into 36 distinct subtasks. With rich visual annotations, such as facial landmarks, gaze, hand movements, object localization, and more for 70 participants, QUB-PHEO offers two versions: full video data for 50 participants and visual cues for all 70. Designed to improve machine learning models for HRI, QUB-PHEO enables deeper analysis of subtle interaction cues and intentions, promising contributions to the field. The dataset will be available at https://github.com/exponentialR/QUB-PHEO subject to an End-User License Agreement (EULA).
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Multispectral Detection Transformer with Infrared-Centric Sensor Fusion
Extending Large Vision-Language Model for Diverse Interactive Tasks in Autonomous Driving
Enhancing Satellite Object Localization with Dilated Convolutions and Attention-aided Spatial Pooling
SoccerNet-v3D: Leveraging Sports Broadcast Replays for 3D Scene Understanding
Texture or Semantics? Vision-Language Models Get Lost in Font Recognition
PixelCAM: Pixel Class Activation Mapping for Histology Image Classification and ROI Localization
토론
이 논문과 연결된 포럼 글이 아직 없습니다.