8. A. Kushal, C. Schmid, J. Ponce: Weakly Supervised Learning of Models for Multi-View Object Recognition

Today's visual object recognition systems largely focus on fronto-parallel views of mostly upright, nearly rigid objects with characteristic texture patterns. To overcome these limitations, we propose a novel framework for visual object recognition with the following characteristics: (1) model learning is seen as a weakly supervised image segmentation task, capturing both geometric and photometric regularities despite intra-class variations; (2) geometric consistency is enforced locally both at the level of model parts and at the level of individual features within the parts, making the system more robust to viewpoint change and intra-class variability.