49,916 research outputs found
Becoming the Expert - Interactive Multi-Class Machine Teaching
Compared to machines, humans are extremely good at classifying images into
categories, especially when they possess prior knowledge of the categories at
hand. If this prior information is not available, supervision in the form of
teaching images is required. To learn categories more quickly, people should
see important and representative images first, followed by less important
images later - or not at all. However, image-importance is individual-specific,
i.e. a teaching image is important to a student if it changes their overall
ability to discriminate between classes. Further, students keep learning, so
while image-importance depends on their current knowledge, it also varies with
time.
In this work we propose an Interactive Machine Teaching algorithm that
enables a computer to teach challenging visual concepts to a human. Our
adaptive algorithm chooses, online, which labeled images from a teaching set
should be shown to the student as they learn. We show that a teaching strategy
that probabilistically models the student's ability and progress, based on
their correct and incorrect answers, produces better 'experts'. We present
results using real human participants across several varied and challenging
real-world datasets.Comment: CVPR 201
Teaching Categories to Human Learners with Visual Explanations
We study the problem of computer-assisted teaching with explanations.
Conventional approaches for machine teaching typically only provide feedback at
the instance level e.g., the category or label of the instance. However, it is
intuitive that clear explanations from a knowledgeable teacher can
significantly improve a student's ability to learn a new concept. To address
these existing limitations, we propose a teaching framework that provides
interpretable explanations as feedback and models how the learner incorporates
this additional information. In the case of images, we show that we can
automatically generate explanations that highlight the parts of the image that
are responsible for the class label. Experiments on human learners illustrate
that, on average, participants achieve better test set performance on
challenging categorization tasks when taught with our interpretable approach
compared to existing methods
Teaching programming at a distance: the Internet software visualization laboratory
This paper describes recent developments in our approach to teaching computer programming in the context of a part-time Masters course taught at a distance. Within our course, students are sent a pack which contains integrated text, software and video course material, using a uniform graphical representation to tell a consistent story of how the programming language works. The students communicate with their tutors over the phone and through surface mail.
Through our empirical studies and experience teaching the course we have identified four current problems: (i) students' difficulty mapping between the graphical representations used in the course and the programs to which they relate, (ii) the lack of a conversational context for tutor help provided over the telephone, (iii) helping students who due to their other commitments tend to study at 'unsociable' hours, and (iv) providing software for the constantly changing and expanding range of platforms and operating systems used by students.
We hope to alleviate these problems through our Internet Software Visualization Laboratory (ISVL), which supports individual exploration, and both synchronous and asynchronous communication. As a single user, students are aided by the extra mappings provided between the graphical representations used in the course and their computer programs, overcoming the problems of the original notation. ISVL can also be used as a synchronous communication medium whereby one of the users (generally the tutor) can provide an annotated demonstration of a program and its execution, a far richer alternative to technical discussions over the telephone. Finally, ISVL can be used to support asynchronous communication, helping students who work at unsociable hours by allowing the tutor to prepare short educational movies for them to view when convenient. The ISVL environment runs on a conventional web browser and is therefore platform independent, has modest hardware and bandwidth requirements, and is easy to distribute and maintain. Our planned experiments with ISVL will allow us to investigate ways in which new technology can be most appropriately applied in the service of distance education
Crowdsourcing in Computer Vision
Computer vision systems require large amounts of manually annotated data to
properly learn challenging visual concepts. Crowdsourcing platforms offer an
inexpensive method to capture human knowledge and understanding, for a vast
number of visual perception tasks. In this survey, we describe the types of
annotations computer vision researchers have collected using crowdsourcing, and
how they have ensured that this data is of high quality while annotation effort
is minimized. We begin by discussing data collection on both classic (e.g.,
object recognition) and recent (e.g., visual story-telling) vision tasks. We
then summarize key design decisions for creating effective data collection
interfaces and workflows, and present strategies for intelligently selecting
the most important data instances to annotate. Finally, we conclude with some
thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in
Computer Graphics and Vision, 201
Recommended from our members
Participatory online environmental education at the Open University UK
The role of education in helping our societies put sustainability into practice is crucial. The motivation, awareness and empowerment, necessary for citizens to understand the concept and take part in its operationalisation, ought to emerge from environmental education, since we might encounter some difficulty when trying to teach, or impose these notions in a theoretical way. In developing environmental courses, we therefore ought to progressively replace pedagogical approaches based on (relatively 'authoritarian') transfers of information with more interactive and collaborative learning processes: citizens' participation can start with the creation of communities of learners. This paper describes the construction of two web courses: a first level teaching module on environmental systems and a third level interdisciplinary environmental course, both developed at the Open University, specialised in distance and open learning. The themes of the course include participatory processes in decision-making, the perception and representation of environmental systems, alternative leadership, biodiversity, climate change and integrated water management, environmental action and governance. The concepts focused on include sustainability, complexity, uncertainty, globalisation and 'systemic problem solving'. In both courses, the overall pedagogical process is based on the notion of environmental governance. This means that the web has been chosen as a learning platform, because
- it provides various types of up to date information as well as archives,
- it allows various types of users to communicate between different countries, and also
- it encourages collaborative and interactive learning.
This paper describes the experience of the author in creating web environmental courses at the Open University. Components of the courses such as interactive activities are discussed, as well as the pedagogical focus progressively shifted towards more participatory processes of learning
- …