Guided Masked Self-Distillation Modeling for Distributed Multimedia Sensor Event Analysis
<|reference_start|>Guided Masked Self-Distillation Modeling for Distributed Multimedia Sensor Event Analysis: 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 combine such distributed observations. Therefore, we propose Guided Masked sELf-Distillation modeling (Guided-MELD) for inter-sensor relationship modeling. The basic idea of Guided-MELD is to learn to supplement the information from the masked sensor with information from other sensors needed to detect the event. Guided-MELD is expected to enable the system to effectively distill the fragmented or redundant target event information obtained by the sensors without being overly dependent on any specific sensors. To validate the effectiveness of the proposed method in novel tasks of distributed multimedia sensor event analysis, we recorded two new datasets that fit the problem setting: MM-Store and MM-Office. These datasets consist of human activities in a convenience store and an office, recorded using distributed cameras and microphones. Experimental results on these datasets show that the proposed Guided-MELD improves event tagging and detection performance and outperforms conventional inter-sensor relationship modeling methods. Furthermore, the proposed method performed robustly even when sensors were reduced.<|reference_end|>
@article{yasuda2024guided,
    title={Guided Masked Self-Distillation Modeling for Distributed Multimedia
  Sensor Event Analysis},
    author={Masahiro Yasuda, Noboru Harada, Yasunori Ohishi, Shoichiro Saito,
  Akira Nakayama, Nobutaka Ono},
    journal={arXiv preprint arXiv:2404.08264},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08264},
    primaryClass={cs.MM cs.CV eess.AS}
}