4,326 research outputs found
Online learning of personalised human activity recognition models from user-provided annotations
PhD ThesisIn Human Activity Recognition (HAR), supervised and semi-supervised training are
important tools for devising parametric activity models. For the best modelling performance,
large amounts of annotated personalised sample data are typically required.
Annotating often represents the bottleneck in the overall modelling process as it usually
involves retrospective analysis of experimental ground truth, like video footage. These
approaches typically neglect that prospective users of HAR systems are themselves
key sources of ground truth for their own activities.
This research therefore involves the users of HAR monitors in the annotation process.
The process relies solely on users' short term memory and engages with them to
parsimoniously provide annotations for their own activities as they unfold. E ects
of user input are optimised by using Online Active Learning (OAL) to identify the
most critical annotations which are expected to lead to highly optimal HAR model
performance gains.
Personalised HAR models are trained from user-provided annotations as part of the
evaluation, focusing mainly on objective model accuracy. The OAL approach is contrasted
with Random Selection (RS) { a naive method which makes uninformed annotation
requests. A range of simulation-based annotation scenarios demonstrate that
using OAL brings bene ts in terms of HAR model performance over RS. Additionally,
a mobile application is implemented and deployed in a naturalistic context to collect
annotations from a panel of human participants. The deployment is proof that the
method can truly run in online mode and it also shows that considerable HAR model
performance gains can be registered even under realistic conditions.
The ndings from this research point to the conclusion that online learning from userprovided
annotations is a valid solution to the problem of constructing personalised
HAR models
An introduction to crowdsourcing for language and multimedia technology research
Language and multimedia technology research often relies on
large manually constructed datasets for training or evaluation of algorithms and systems. Constructing these datasets is often expensive with significant challenges in terms of recruitment of personnel to carry out the work. Crowdsourcing methods using scalable pools of workers available on-demand offers a flexible means of rapid low-cost construction of many of these datasets to support existing research requirements and potentially promote new research initiatives that would otherwise not be possible
Deep Active Learning for Computer Vision: Past and Future
As an important data selection schema, active learning emerges as the
essential component when iterating an Artificial Intelligence (AI) model. It
becomes even more critical given the dominance of deep neural network based
models, which are composed of a large number of parameters and data hungry, in
application. Despite its indispensable role for developing AI models, research
on active learning is not as intensive as other research directions. In this
paper, we present a review of active learning through deep active learning
approaches from the following perspectives: 1) technical advancements in active
learning, 2) applications of active learning in computer vision, 3) industrial
systems leveraging or with potential to leverage active learning for data
iteration, 4) current limitations and future research directions. We expect
this paper to clarify the significance of active learning in a modern AI model
manufacturing process and to bring additional research attention to active
learning. By addressing data automation challenges and coping with automated
machine learning systems, active learning will facilitate democratization of AI
technologies by boosting model production at scale.Comment: Accepted by APSIPA Transactions on Signal and Information Processin
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