[2M5-J-10] Vision, speech: movement of bodies and objects

[2M5-J-10-05] Gesture Recognition by Multi-task Learning using Parallel Residual Adapters

〇Noeru Suzuki¹, Kosuke Kikui², Yuta Itoh³, Hisashi Kashima¹, Makoto Yamada¹ (1. Kyoto University, 2. Keio University, 3. Tokyo Institute of Technology)

Keywords:Gesture Recognition, Multi-task Learning, Transfar Learning

The photo reflective sensor (PRS) is a tiny distant-measurement module whilch is widely used in wearable user-interface. A issue of such wearable PRS devices is the performance degradation when a user data is not included in the training set (we call the inter-user setup). Moreover, the recognition accuracy also degrades when the same user re-wears the device (we call the intra-user setup). In this paper, we introduce the multi-task learning algorithm using the Parallel residual adapters as a method to perform additional training at high speed using a small amount of additional data. In the experiments, we demonstrate the performance improvement in inter-user and intra-user setup with the data of an actual PRS device.