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

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

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

Automatic validation of learning object compositions

Course construction using reusable learning objects is becoming ever more popular due to its’ efficiency. The course creator who uses this methodology may face problems due to the fact that he or she is not as intimately involved in the creation of every element of the course. In this paper we discuss one such problem faced by course creator known as “the competency gap”. Here, we define the competency gap, explain how it can be identified and suggest ways of correcting the problem

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

Hierarchical object detection with deep reinforcement learning

We present a method for performing hierarchical object detection in images guided by a deep reinforcement learning agent. The key idea is to focus on those parts of the image that contain richer information and zoom on them. We train an intelligent agent that, given an image window, is capable of deciding where to focus the attention among five different predefined region candidates (smaller windows). This procedure is iterated providing a hierarchical image analysis. We compare two different candidate proposal strategies to guide the object search: with and without overlap. Moreover, our work compares two different strategies to extract features from a convolutional neural network for each region proposal: a first one that computes new feature maps for each region proposal, and a second one that computes the feature maps for the whole image to later generate crops for each region proposal. Experiments indicate better results for the overlapping candidate proposal strategy and a loss of performance for the cropped image features due to the loss of spatial resolution. We argue that, while this loss seems unavoidable when working with large amounts of object candidates, the much more reduced amount of region proposals generated by our reinforcement learning agent allows considering to extract features for each location without sharing convolutional computation among regions.Postprint (published version