Program

Kristen Grauman

University of Texas at Austin

"My research interests are in computer vision and machine learning. In general, the goal of computer vision is to develop the algorithms and representations that will allow a computer to autonomously analyze visual information. I am especially interested in learning and recognizing visual object categories, and scalable methods for content-based retrieval and visual search."

Kavita Bala

Cornell

Crowdsourcing for Material Recognition in the Wild

Human beings are good at perceiving subtle distinctions in material appearance (e.g., is this fabric silk or velvet?), but computers lag far behind in the task of material recognition. I will describe how we collected several large-scale, crowdsourced datasets of materials and reflectance comparisons from consumer photographs, and how we used these datasets to achieve state-of-the-art results in material recognition, intrinsic image decomposition, and material-based image browsing and design.

Kavita Bala is a Professor in the Computer Science Department and Program of Computer Graphics at Cornell University. Prof. Bala specializes in computer graphics and computer vision, leading research projects in material perception, recognition, and acquisition; realistic rendering; perception; and computational lighting design.

Ariel Shamir

Efi Arazi School of Computer Science at the Interdisciplinary Center Israel

Passive Human Computation

Image and video processing have gone a long way in recent years with automatic algorithms. However, some simple tasks that even a child can do, still pose a challenge for automatic algorithms. Especially if these tasks involve some semantic understanding. Children and humans often learn by observing others. In this talk I will present some examples where algorithms for image and video processing learn semantic understanding by observing human behaviors. This can be seen as a form of passive human computation - humans must participate in the computation process, but they are not actively computing anything.

Ariel Shamir is interested in geometric modeling, computer graphics, fabrication, visualization, and machine learning. Besides his professorial work, he is or was affiliated with Mitsubishi Electric Research Labs, Disney Research, NASA, Lawrence Livermore National Labs, and a number of high-tech companies in Israel. Prof. Shamir has been a part of many seminal projects in computer vision and computational photography such as Seam Carving, Sketch2Photo, and 3-Sweep.

Sanja Fidler

University of Toronto

"My work is in the area of Computer Vision. My main research interests are 2D and 3D object detection, particularly scalable multi-class detection, object segmentation and image labeling, and (3D) scene understanding. I am also interested in the interplay between language and vision: generating sentential descriptions about complex scenes, as well as using textual descriptions for better scene parsing (e.g., in the scenario of the human-robot interaction)."

Kotaro Hara

University of Maryland, College Park

Using Crowdsourcing, Computer Vision, and Google Street View to Collect Sidewalk Accessibility Data

Poorly maintained sidewalks pose considerable accessibility challenges for people with mobility impairments. Despite comprehensive civil rights legislation of Americans with Disabilities Act, many city streets and sidewalks in the U.S. remain inaccessible. The problem is not just that sidewalk accessibility fundamentally affects where and how people travel in cities, but also that there are few, if any, mechanisms to determine accessible areas of a city a priori.

In this talk, I will introduce a scalable data collection method for acquiring street-level accessibility information using a combination of crowdsourcing, computer vision, automatic workflow controller, and Google Street View. Our work shows that by combining crowdsourcing and automated methods, we can increase data collection efficiency by 13% compared to a fully manual approach without sacrificing accuracy. Our overarching goal is to transform the ways in which accessibility information is collected and visualized for every sidewalk, street, and building facade in the world.

"My research focuses on design of systems to collect and deliver information about accessibility of streets and sidewalks that help people with mobility impairments. Using geo-tagged street-level imagery like Google Street View as data source, we locate sidewalk accessibility problems with crowdsourcing and computer vision technologies."