23 citations · 87 across the 8 of their papers we have counts for
8 papers
Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications
Qing Qu, Zhihui Zhu, Xiao Li +3
The problem of finding the sparsest vector (direction) in a low dimensional subspace can be considered as a homogeneous variant of the sparse recovery problem, which finds applicat…
Basis Pursuit and Orthogonal Matching Pursuit for Subspace-preserving Recovery: Theoretical Analysis
Daniel P. Robinson, Rene Vidal, Chong You
Given an overcomplete dictionary and a signal for some sparse vector whose nonzero entries correspond to linearly independent columns of , classical sparse…
Dual Principal Component Pursuit: Probability Analysis and Efficient Algorithms
Zhihui Zhu, Yifan Wang, Daniel P. Robinson +3
Recent methods for learning a linear subspace from data corrupted by outliers are based on convex and nuclear norm optimization and require the dimension of the subspace a…
Information Pursuit: A Bayesian Framework for Sequential Scene Parsing
Ehsan Jahangiri, Erdem Yoruk, Rene Vidal +2
Despite enormous progress in object detection and classification, the problem of incorporating expected contextual relationships among object instances into modern recognition syst…
Temporal Convolutional Networks for Action Segmentation and Detection
Colin Lea, Michael D. Flynn, Rene Vidal +2
The ability to identify and temporally segment fine-grained human actions throughout a video is crucial for robotics, surveillance, education, and beyond. Typical approaches decoup…
Temporal Convolutional Networks: A Unified Approach to Action Segmentation
Colin Lea, Rene Vidal, Austin Reiter +1
The dominant paradigm for video-based action segmentation is composed of two steps: first, for each frame, compute low-level features using Dense Trajectories or a Convolutional Ne…