Semantics-Aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Yang LiuLiang Lin

Abstract

Existing vision-based action recognition is susceptible to occlusion and appearance variations, while wearable sensors can alleviate these challenges by capturing human motion with one-dimensional time-series signals (e.g. acceleration, gyroscope, and orientation). For the same action, the knowledge learned from vision sensors (videos or images) and wearable sensors, may be related and complementary. However, there exists a significantly large modality difference between action data captured by wearable-sensor and vision-sensor in data dimension, data distribution, and inherent information content. In this paper, we propose a novel framework, named Semantics-aware Adaptive Knowledge Distillation Networks (SAKDN), to enhance action recognition in vision-sensor modality (videos) by adaptively transferring and distilling the knowledge from multiple wearable sensors. The SAKDN uses multiple wearable-sensors as teacher modalities and uses RGB videos as student modalities. To preserve the local temporal relationship and facilitate employing visual deep learning models, we transform one-dimensional time-series signals of wearable sensors to two-dimensional images by designing a gramian angular field based virtual image generation mode...Continue Reading

Software Mentioned

Grad
SPAMFM
Fitnet
BNInception
Berkeley
MMAR MKL
SAKDN
PyTorch
CAM
BNIneption

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