Zero-shot learning with matching networks for open-ended human activity recognition.
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WIJEKOON, A., WIRATUNGA, N. and SANI, S. 2018. Zero-shot learning with matching networks for open-ended human activity recognition. In Martin, K., Wiratunga, N. and Smith, L.S. (eds.) Proceedings of the Scottish Informatics and Computer Science Alliance (SCISA) workshop on reasoning, learning and explainability (ReaLX 2018), 27 June 2018, Aberdeen, UK. Aberdeen: CEUR [online], Session 2, paper 4. Available from: http://ceur-ws.org/Vol-2151/Paper_S9.pdf
A real-world solution for Human Activity Recognition (HAR) should cover a variety of activities. However training a model to cover each and every possible activity is not practical. Instead we need a solution that can adapt its learning to unseen activities; referred to as open-ended HAR. Recent advancements in deep learning have increasingly begun to focus on the need to learn from few examples, referred to as k-shot learning and to go beyond this to transfer that learning to situations with unseen classes, referred to as zero-shot learning. The latter is particularly relevant to our research in open-ended HAR; and as yet remains unexplored. This paper presents our preliminary work with Zero-shot Learning (ZSL) with a Matching Network to address openended HAR. A Matching Network has the desirable property of learning with few examples and so is well suited to explorations in ZSL. We evaluate Matching Networks for ZSL with a HAR dataset. We propose the use of a variable length support set at test time to overcome the search for the best support set combination that currently plagues the fixed length support set size used by matching nets. Our results show that the variable length approach to be an effective strategy to maintain accuracy whilst avoiding the combinatorial search for the best class combination to form the support set.