Recent computer vision approaches are aimed at richer image interpretations that extend the standard recognition of objects in images (e.g., cars) to also recognize object attribu...
William Curran, Travis Moore, Todd Kulesza, Weng-K...
We propose a novel probabilistic framework for learning
visual models of 3D object categories by combining appearance
information and geometric constraints. Objects are
represen...
We propose Recursive Compositional Models (RCMs) for simultaneous multi-view multi-object detection and parsing (e.g. view estimation and determining the positions of the object s...
Leo Zhu, Yuanhao Chen, Antonio Torralba, William F...
Abstract. In this paper, we present a deformable-model based solution for segmenting objects with complex texture patterns of all scales. The external image forces in traditional d...
Xiaolei Huang, Zhen Qian, Rui Huang, Dimitris N. M...
Abstract. Robots need to ground their external vocabulary and internal symbols in observations of the world. In recent works, this problem has been approached through combinations ...