Guided Masked Self-Distillation Modeling for Distributed Multimedia Sensor Event Analysis

arXiv:2404.08264v1 Announce Type: cross
Abstract: Observations with distributed sensors are essential in analyzing a series of human and machine activities (referred to as 'events' in this paper) in complex and extensive real-world environments. This is because the information obtained from a single sensor is often missing or fragmented in such an environment; observations from multiple locations and modalities should be integrated to analyze events comprehensively. However, a learning method has yet to be established to extract joint representations that effectively …

abstract analysis arxiv cs.cv cs.mm distillation distributed eess.as environments event events human human and machine information machine modeling multimedia paper sensor sensors series the information type world