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Face Clustering in the videos
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Greatest papers with code
Learning to Cluster Faces via Confidence and Connectivity Estimation
CVPR 2020
• yl-1993/learn-to-cluster
• 
With the vertex confidence and edge connectivity, we can naturally organize more relevant vertices on the affinity graph and group them into clusters.
Linkage Based Face Clustering via Graph Convolution Network
CVPR 2019
• yl-1993/learn-to-cluster
•
The key idea is that we find the local context in the feature space around an instance (face) contains rich information about the linkage relationship between this instance and its neighbors.
Sparse Subspace Clustering: Algorithm, Theory, and Applications
• panji1990/Deep-subspace-clustering-networks
• 
In this paper, we propose and study an algorithm, called Sparse Subspace Clustering (SSC), to cluster data points that lie in a union of low-dimensional subspaces.
Ranked #4 on
Motion Segmentation
on Hopkins155
Video Face Clustering with Unknown Number of Clusters
ICCV 2019
• makarandtapaswi/BallClustering_ICCV2019
•
Understanding videos such as TV series and movies requires analyzing who the characters are and what they are doing.
Scalable Sparse Subspace Clustering by Orthogonal Matching Pursuit
Subspace clustering methods based on $\ell_1$, $\ell_2$ or nuclear norm regularization have become very popular due to their simplicity, theoretical guarantees and empirical success.
Ranked #6 on
Image Clustering
on Extended Yale-B
Self-Supervised Learning of Face Representations for Video Face Clustering
In this paper, we address video face clustering using unsupervised methods.
AN ONLINE ALGORITHM FOR CONSTRAINED FACE CLUSTERING IN VIDEOS
We address the problem of face clustering in long, real world videos. This is a challenging task because faces in such videos exhibit wid evariability in scale, pose, illumination, expressions, and may also be partially occluded.
Robust Subspace Clustering via Tighter Rank Approximation
For this nonconvex minimization problem, we develop an effective optimization procedure based on a type of augmented Lagrange multipliers (ALM) method.
Robust Subspace Clustering via Smoothed Rank Approximation
However, for many real-world applications, nuclear norm approximation to the rank function can only produce a result far from the optimum.