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

Changeable Context of the New Technology Artefact and the Changeable Research Outcomes

Computer Aided Drawing (vector based) and painting (raster based) packages, allow the mock-up of designs in virtual space. Whilst this is beneficial for visualising the end product, both methods of drawing have been applied to new and existing machining technologies so that some aspect of the product is derived from a computer file. Today, the applied artist now has an abundance of CAD/CAM (Computer Aided Drawing / Computer Aided Manufacturing) technologies awaiting them. So much so, that in the past fifteen years, many makers have embarked upon practice-led research to find out what a particular technology can do with regard to their design interests. Within such research, the object is the manifestation of what has been discovered through the research activity. This paper considers the relationship between the content of the research object and the context for the object’s reception. This is examined with regard to the author’s research with new technologies for the applied arts. Examples will highlight how the characteristics of artefacts that arise from the research can help determine who the audience for the work is, and how the technology might be used by different kinds of craft practitioners. References will also be made to the work of other designer-makers working with and researching similar technologies. Evidence from practical examples will also be supported by a more theoretical discussion. The implications of supplying, or not supplying, background information for an audience within a variety of settings on the perceived content/context of the object, and the communication of the research, will also be discussed. It is concluded that when developing new processes, keeping the work open to a number of audiences can maximise the outcomes and increase the chances of the process being integrated within practice. The discussion also highlights a trend of positioning the consumer/viewer at the forefront of the research, and a need to evaluate their experiences

View-Invariant Object Category Learning, Recognition, and Search: How Spatial and Object Attention Are Coordinated Using Surface-Based Attentional Shrouds

Air Force Office of Scientific Research (F49620-01-1-0397); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Matterport3D: Learning from RGB-D Data in Indoor Environments

Access to large, diverse RGB-D datasets is critical for training RGB-D scene understanding algorithms. However, existing datasets still cover only a limited number of views or a restricted scale of spaces. In this paper, we introduce Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views from 194,400 RGB-D images of 90 building-scale scenes. Annotations are provided with surface reconstructions, camera poses, and 2D and 3D semantic segmentations. The precise global alignment and comprehensive, diverse panoramic set of views over entire buildings enable a variety of supervised and self-supervised computer vision tasks, including keypoint matching, view overlap prediction, normal prediction from color, semantic segmentation, and region classification

Pose Induction for Novel Object Categories

We address the task of predicting pose for objects of unannotated object categories from a small seed set of annotated object classes. We present a generalized classifier that can reliably induce pose given a single instance of a novel category. In case of availability of a large collection of novel instances, our approach then jointly reasons over all instances to improve the initial estimates. We empirically validate the various components of our algorithm and quantitatively show that our method produces reliable pose estimates. We also show qualitative results on a diverse set of classes and further demonstrate the applicability of our system for learning shape models of novel object classes