Feedforward Semantic Segmentation with Zoom-out Features


August 14, 2015


Greg Shakhnarovich




I will introduce a novel feed-forward architecture for semantic segmentation. We map small image elements (superpixels) to rich feature representations extracted from a sequence of nested regions of increasing extent. These regions are obtained by “zooming out” from the superpixel all the way to scene-level resolution. Our approach exploits statistical structure in the image and in the label space without setting up explicit structured prediction mechanisms, and thus avoids complex and expensive inference. Instead superpixels are classified by a feedforward multilayer network with skip-layer connections spanning the zoomout levels. Using off-the-shelf network pre-trained on ImageNet classification task, this zoom-out architecture achieves 69.6% average accuracy on the PASCAL VOC 2012 test set, near current state of the art. Joint work with Mohammadreza Mostajabi and Payman Yadollahpour.


Greg Shakhnarovich

Greg Shakhnarovich received a BSc degree in Mathematics and Computer Science from Hebrew University, Jerusalem, in 1994, a MSc degree in Computer Science from the Technion, Haifa, in 2001, and a PhD degree in Electrical Engineering and Computer Science from MIT in 2005. Prior to joining TTIC in 2008, Greg was a postdoctoral scholar at Brown University. He is a recipient of IBM Faculty Award and the Google Faculty Research Award.