@conference {2585,
	title = {Human Pose Estimation Using Deep Consensus Voting},
	booktitle = {ECCV 2016},
	year = {2016},
	abstract = {<p>In this paper we consider the problem of human pose estimation from a single still image.\&nbsp; We propose a novel approach where each location in the\&nbsp; image\&nbsp; votes\&nbsp; for\&nbsp; the\&nbsp; position\&nbsp; of\&nbsp; each\&nbsp; keypoint\&nbsp; using\&nbsp; a\&nbsp; convolutional neural net.\&nbsp; The voting scheme allows us to utilize information from the whole image, rather than rely on a sparse set of keypoint locations.\&nbsp; Using dense, multi-target votes, not only produces good keypoint predictions, but also enables us to compute image-dependent joint keypoint probabilities by looking at consensus voting.\&nbsp; This differs from most previous methods where joint probabilities are learned from relative keypoint locations and are independent of the image.\&nbsp; We finally combine the keypoints votes and joint probabilities in order to identify the optimal pose configuration.\&nbsp; We show our competitive performance on the MPII Human Pose and Leeds Sports Pose datasets.</p>
},
	author = {Ita Lifshitz and Ethan Fetaya and Shimon Ullman}
}
@proceedings {793,
	title = {Graph Approximation and Clustering on a Budget},
	volume = {38},
	year = {2015},
	abstract = {<p>We consider the problem of learning from a\&nbsp; similarity matrix (such as spectral cluster-\&nbsp; ing and low-dimensional embedding), when\&nbsp; computing pairwise similarities are costly,\&nbsp; and only a limited number of entries can be\&nbsp; observed. We provide a theoretical anal-\&nbsp; ysis using standard notions of graph ap-\&nbsp; proximation, significantly generalizing pre-\&nbsp; vious results, which focused on spectral\&nbsp; clustering with two clusters. We also pro-\&nbsp; pose a new algorithmic approach based on\&nbsp; adaptive sampling, which experimentally\&nbsp; matches or improves on previous methods,\&nbsp; while being considerably more general and\&nbsp; computationally cheaper.</p>
},
	author = {Ethan Fetaya and Ohad Shamir and Shimon Ullman}
}
