@conference {2585, title = {Human Pose Estimation Using Deep Consensus Voting}, booktitle = {ECCV 2016}, year = {2016}, abstract = {

In this paper we consider the problem of human pose estimation from a single still image.\  We propose a novel approach where each location in the\  image\  votes\  for\  the\  position\  of\  each\  keypoint\  using\  a\  convolutional neural net.\  The voting scheme allows us to utilize information from the whole image, rather than rely on a sparse set of keypoint locations.\  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.\  This differs from most previous methods where joint probabilities are learned from relative keypoint locations and are independent of the image.\  We finally combine the keypoints votes and joint probabilities in order to identify the optimal pose configuration.\  We show our competitive performance on the MPII Human Pose and Leeds Sports Pose datasets.

}, author = {Ita Lifshitz and Ethan Fetaya and Shimon Ullman} } @proceedings {793, title = {Graph Approximation and Clustering on a Budget}, volume = {38}, year = {2015}, abstract = {

We consider the problem of learning from a\  similarity matrix (such as spectral cluster-\  ing and low-dimensional embedding), when\  computing pairwise similarities are costly,\  and only a limited number of entries can be\  observed. We provide a theoretical anal-\  ysis using standard notions of graph ap-\  proximation, significantly generalizing pre-\  vious results, which focused on spectral\  clustering with two clusters. We also pro-\  pose a new algorithmic approach based on\  adaptive sampling, which experimentally\  matches or improves on previous methods,\  while being considerably more general and\  computationally cheaper.

}, author = {Ethan Fetaya and Ohad Shamir and Shimon Ullman} }