Factorized Graph Matching is proposed for better optimizing and understanding graph matching problems.
Canonical Time Warping is proposed for temporally aligning multi-modal sequences.
Aligned Cluster Analysis is proposed to temporally cluster human behavior.
An Unsupervised System is proposed to discover facial events from video of naturally occurring facial behavior.
Exemplar-based Graph Matching is proposed to robustly localize facial landmarks on image.
Time Mapping generates low-frame-rate video from a high-frame-rate input using Video Saliency.
Spatio-temporal Matching is proposed for detecting and tracking humans in videos.
Bipartite-Graph Labels is proposed to exploit the rich relationships among fine-grained object classes.
MagFace learns a universal feature embedding whose magnitude can measure the quality of the given face.