From images via symbols to contexts: using augmented reality for interactive model acquisition

Systems that perform in real environments need to bind the internal state to externally perceived objects, events, or complete scenes. How to learn this correspondence has been a long standing problem in computer vision as well as artificial intelligence. Augmented Reality provides an interesting perspective on this problem because a human user can directly relate displayed system results to real environments. In the following we present a system that is able to bootstrap internal models from user-system interactions. Starting from pictorial representations it learns symbolic object labels that provide the basis for storing observed episodes. In a second step, more complex relational information is extracted from stored episodes that enables the system to react on specific scene contexts

Towards Rapid Generation and Visualisation of Large 3D Urban Landscapes for Mobile Device Navigation

In this paper a procedural 3D modelling solution for mobile devices is presented based on scripting algorithms allowing for both the automatic and also semi-automatic creation of photorealistic quality virtual urban content. The combination of aerial images, GIS data, 2D ground maps and terrestrial photographs as input data coupled with a user-friendly customized interface permits the automatic and interactive generation of large-scale, accurate, georeferenced and fully-textured 3D virtual city content, content that can be specially optimized for use with mobile devices but also with navigational tasks in mind. Furthermore, a user-centred mobile virtual reality (VR) visualisation and interaction tool operating on PDAs (Personal Digital Assistants) for pedestrian navigation is also discussed. Via this engine, the import and display of various navigational file formats (2D and 3D) is supported, including a comprehensive front-end user-friendly graphical user interface providing immersive virtual 3D navigation

Learning a Policy for Opportunistic Active Learning

Active learning identifies data points to label that are expected to be the most useful in improving a supervised model. Opportunistic active learning incorporates active learning into interactive tasks that constrain possible queries during interactions. Prior work has shown that opportunistic active learning can be used to improve grounding of natural language descriptions in an interactive object retrieval task. In this work, we use reinforcement learning for such an object retrieval task, to learn a policy that effectively trades off task completion with model improvement that would benefit future tasks.Comment: EMNLP 2018 Camera Read

Hand2Face: Automatic Synthesis and Recognition of Hand Over Face Occlusions

A person's face discloses important information about their affective state. Although there has been extensive research on recognition of facial expressions, the performance of existing approaches is challenged by facial occlusions. Facial occlusions are often treated as noise and discarded in recognition of affective states. However, hand over face occlusions can provide additional information for recognition of some affective states such as curiosity, frustration and boredom. One of the reasons that this problem has not gained attention is the lack of naturalistic occluded faces that contain hand over face occlusions as well as other types of occlusions. Traditional approaches for obtaining affective data are time demanding and expensive, which limits researchers in affective computing to work on small datasets. This limitation affects the generalizability of models and deprives researchers from taking advantage of recent advances in deep learning that have shown great success in many fields but require large volumes of data. In this paper, we first introduce a novel framework for synthesizing naturalistic facial occlusions from an initial dataset of non-occluded faces and separate images of hands, reducing the costly process of data collection and annotation. We then propose a model for facial occlusion type recognition to differentiate between hand over face occlusions and other types of occlusions such as scarves, hair, glasses and objects. Finally, we present a model to localize hand over face occlusions and identify the occluded regions of the face.Comment: Accepted to International Conference on Affective Computing and Intelligent Interaction (ACII), 201

RGBD Datasets: Past, Present and Future

Since the launch of the Microsoft Kinect, scores of RGBD datasets have been released. These have propelled advances in areas from reconstruction to gesture recognition. In this paper we explore the field, reviewing datasets across eight categories: semantics, object pose estimation, camera tracking, scene reconstruction, object tracking, human actions, faces and identification. By extracting relevant information in each category we help researchers to find appropriate data for their needs, and we consider which datasets have succeeded in driving computer vision forward and why. Finally, we examine the future of RGBD datasets. We identify key areas which are currently underexplored, and suggest that future directions may include synthetic data and dense reconstructions of static and dynamic scenes.Comment: 8 pages excluding references (CVPR style