Visual Recognition

Speaker  Fei Fei Li, Kristen Grauman, and Larry Zitnick

Host  Andrew Blake, Microsoft Research

Affiliation  University of Texas, Stanford University, Microsoft Research

Duration  01:31:15

Date recorded  16 July 2013

The fields of Computer Vision and Machine Learning are becoming increasingly intertwined, with many of the recent breakthroughs in object and scene recognition coming from the availability of large labeled datasets and sophisticated machine learning techniques.

In this session of the 2013 Microsoft Research Faculty Summit, leading researchers in these fields share their perspectives on recent advances and current challenges. How do sophisticated machine learning approaches aid in solving difficult recognition problems? What role do large labeled datasets and recognition challenges play in advancing the state of the art and enabling data-driven approaches to recognition? And how can the layout of objects in a scene as well as relationship to natural language models give us an edge in describing complex scenes with multiple actors and objects? These are just some of the questions at the forefront of this rapidly evolving research field.

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