Towards Holistic Scene Understanding: Hybrid Semantic Segmentation & Articulated Object Modeling

Recent trends in semantic image segmentation have pushed for
holistic scene understanding models that also reason about
complementary tasks such as scene classification and object detection.
In the first part of the talk, I will describe a hybrid human-machine
scene understanding model. In this work, we are interested in
understanding the roles of different cues in aiding semantic
segmentation. Towards this goal, we "plug-in" human subjects for each
of the various components in the model to show how much "head room"
there is to improve semantic segmentation.

The second part of the talk will be about the models we have developed
to better capture deformations of articulated objects. The performance
of current object detectors usually degrades for highly flexible
objects. In this talk, I will explain how we overcome this shortcoming
to achieve the state-of-the-art performance on difficult object
detection benchmarks such as PASCAL VOC.