Object lessons : a learning object approach to e-learning for social work education

Learning objects are bite-sized digital learning resources designed to tackle the e-learning adoption problem by virtue of their scale, adaptability, and interoperability. The learning object approach advocates the creation of small e-learning resources rather than whole courses: resources that can be mixed and matched; used in a traditional or online learning environment; and adapted for reuse in other discipline areas and in other countries. Storing learning objects within a subject specific digital repository to enable search, discovery, sharing and use adds considerable value to the model. This paper explores the rationale for a learning object approach to e-learning and reflects on early experiences in developing a national learning object repository for social work education in Scotland

SFNet: Learning Object-aware Semantic Correspondence

We address the problem of semantic correspondence, that is, establishing a dense flow field between images depicting different instances of the same object or scene category. We propose to use images annotated with binary foreground masks and subjected to synthetic geometric deformations to train a convolutional neural network (CNN) for this task. Using these masks as part of the supervisory signal offers a good compromise between semantic flow methods, where the amount of training data is limited by the cost of manually selecting point correspondences, and semantic alignment ones, where the regression of a single global geometric transformation between images may be sensitive to image-specific details such as background clutter. We propose a new CNN architecture, dubbed SFNet, which implements this idea. It leverages a new and differentiable version of the argmax function for end-to-end training, with a loss that combines mask and flow consistency with smoothness terms. Experimental results demonstrate the effectiveness of our approach, which significantly outperforms the state of the art on standard benchmarks.Comment: cvpr 2019 oral pape

A learning object success story

This paper outlines an approach to designing a course entirely in learning objects. It provides a theoretical basis for the design and then presents evaluation data from a master’s level course using this design. It also describes several re-uses of the learning objects on other courses and in different contexts. Each learning object is conceived as a whole learning experience, thus avoiding many of the problems associated with assembling components of disparate kinds

Relation Networks for Object Detection

Although it is well believed for years that modeling relations between objects would help object recognition, there has not been evidence that the idea is working in the deep learning era. All state-of-the-art object detection systems still rely on recognizing object instances individually, without exploiting their relations during learning. This work proposes an object relation module. It processes a set of objects simultaneously through interaction between their appearance feature and geometry, thus allowing modeling of their relations. It is lightweight and in-place. It does not require additional supervision and is easy to embed in existing networks. It is shown effective on improving object recognition and duplicate removal steps in the modern object detection pipeline. It verifies the efficacy of modeling object relations in CNN based detection. It gives rise to the first fully end-to-end object detector

Learning Object Repositories: Problems and Promise

Considers the state of the reuse and sharing of learning related Web-based material. Discusses higher education in relation to the broader world of e-learning, and limitations on the growth and impact of education delivered over the Web

One-shot learning of object categories

Learning visual models of object categories notoriously requires hundreds or thousands of training examples. We show that it is possible to learn much information about a category from just one, or a handful, of images. The key insight is that, rather than learning from scratch, one can take advantage of knowledge coming from previously learned categories, no matter how different these categories might be. We explore a Bayesian implementation of this idea. Object categories are represented by probabilistic models. Prior knowledge is represented as a probability density function on the parameters of these models. The posterior model for an object category is obtained by updating the prior in the light of one or more observations. We test a simple implementation of our algorithm on a database of 101 diverse object categories. We compare category models learned by an implementation of our Bayesian approach to models learned from by maximum likelihood (ML) and maximum a posteriori (MAP) methods. We find that on a database of more than 100 categories, the Bayesian approach produces informative models when the number of training examples is too small for other methods to operate successfully