SGSM: A Foundation-model-like Semi-generalist Sensing Model
arxiv(2024)
摘要
The significance of intelligent sensing systems is growing in the realm of
smart services. These systems extract relevant signal features and generate
informative representations for particular tasks. However, building the feature
extraction component for such systems requires extensive domain-specific
expertise or data. The exceptionally rapid development of foundation models is
likely to usher in newfound abilities in such intelligent sensing. We propose a
new scheme for sensing model, which we refer to as semi-generalist sensing
model (SGSM). SGSM is able to semiautomatically solve various tasks using
relatively less task-specific labeled data compared to traditional systems.
Built through the analysis of the common theoretical model, SGSM can depict
different modalities, such as the acoustic and Wi-Fi signal. Experimental
results on such two heterogeneous sensors illustrate that SGSM functions across
a wide range of scenarios, thereby establishing its broad applicability. In
some cases, SGSM even achieves better performance than sensor-specific
specialized solutions. Wi-Fi evaluations indicate a 20% accuracy improvement
when applying SGSM to an existing sensing model.
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