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AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild
Paper • 2605.22715 • Published • 1 -
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents
Paper • 2508.04038 • Published • 1 -
SensorLLM: Human-Intuitive Alignment of Multivariate Sensor Data with LLMs for Activity Recognition
Paper • 2410.10624 • Published • 1 -
COMODO: Cross-Modal Video-to-IMU Distillation for Efficient Egocentric Human Activity Recognition
Paper • 2503.07259 • Published
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Papers
AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild
TrajDLM: Topology-Aware Block Diffusion Language Model for Trajectory Generation
Organization Card
CRUISE
Collaborative Human-Centric AI Systems (CRUISE) Lab, led by Prof. Flora Salim, works on machine learning for time-series, spatio-temporal data, and multimodal sensor data, and on trustworthy AI (including fairness, explainability, mechanistic interpretablity) for decision making systems. Our research is supported by the ARC, CRC, and many local and international industry and government partners. We share our codes and some sample datasets in our CRUISE GitHub repository.
Massive Semantic Trajectories for Understanding POI Check-ins (Wongso et al., 2025). https://github.com/cruiseresearchgroup/Massive-STEPS
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Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks
Paper • 2505.11239 • Published -
CRUISEResearchGroup/Massive-STEPS-Bandung
Viewer • Updated • 217k • 111 -
CRUISEResearchGroup/Massive-STEPS-Beijing
Viewer • Updated • 2.04k • 217 -
CRUISEResearchGroup/Massive-STEPS-Istanbul
Viewer • Updated • 761k • 56
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AnyMo: Geometry-Aware Setup-Agnostic Modeling of Human Motion in the Wild
Paper • 2605.22715 • Published • 1 -
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents
Paper • 2508.04038 • Published • 1 -
SensorLLM: Human-Intuitive Alignment of Multivariate Sensor Data with LLMs for Activity Recognition
Paper • 2410.10624 • Published • 1 -
COMODO: Cross-Modal Video-to-IMU Distillation for Efficient Egocentric Human Activity Recognition
Paper • 2503.07259 • Published
Massive Semantic Trajectories for Understanding POI Check-ins (Wongso et al., 2025). https://github.com/cruiseresearchgroup/Massive-STEPS
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Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks
Paper • 2505.11239 • Published -
CRUISEResearchGroup/Massive-STEPS-Bandung
Viewer • Updated • 217k • 111 -
CRUISEResearchGroup/Massive-STEPS-Beijing
Viewer • Updated • 2.04k • 217 -
CRUISEResearchGroup/Massive-STEPS-Istanbul
Viewer • Updated • 761k • 56
datasets 21
CRUISEResearchGroup/CGM-JEPA-Downstream
Viewer • Updated • 44 • 103
CRUISEResearchGroup/CGM-JEPA-Pretraining
Viewer • Updated • 389k • 80
CRUISEResearchGroup/TripWorld
Viewer • Updated • 189M • 143
CRUISEResearchGroup/Massive-STEPS-Palembang
Viewer • Updated • 19.2k • 31
CRUISEResearchGroup/Massive-STEPS-Bandung
Viewer • Updated • 217k • 111
CRUISEResearchGroup/Massive-STEPS-Tangerang
Viewer • Updated • 61.5k • 29
CRUISEResearchGroup/Massive-STEPS-Cairo
Viewer • Updated • 5.41k • 33
CRUISEResearchGroup/Avian-US_dataset
Updated • 15
CRUISEResearchGroup/Massive-STEPS-Beijing
Viewer • Updated • 2.04k • 217
CRUISEResearchGroup/Massive-STEPS-Tokyo
Viewer • Updated • 19.3k • 116