AG 1, AG 2, AG 3, AG 4, AG 5, RG1, SWS, MMCI   Info

Public Audience

English

60 Minutes

Saarbrücken

Object class detection in real world images has been a synonym for object localization for the longest time.  State-of-the-art detection methods, inspired by renowned detection benchmarks, typically target 2D bounding box localization of objects.  At the same time, due to the rapid technological and scientific advances, high-level vision applications, aiming at understanding the visual world as a whole, are coming into the focus. The diversity of the visual world challenges these applications in terms of representational complexity, robust inference and training data. As objects play a central role in any vision system, it has been argued that richer object representations, providing higher level of detail than modern detection methods, are a promising direction towards understanding visual scenes. Besides bridging the gap between object class detection and high-level tasks, richer object representations also lead to more natural object descriptions, bringing computer vision closer to human perception. Inspired by these prospects, this thesis explores four different directions towards richer object representations, namely, 3D object representations, fine-grained representations, occlusion representations, as well as understanding convnet representations. Moreover, this thesis illustrates that richer object representations can facilitate high-level applications, providing detailed and natural object descriptions. In addition, the presented representations attain high performance rates, at least on par or often superior to state-of-the-art methods.

Connie Balzert

0681 9325-2000

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