Face Clustering Edit Task
Computer Vision • Image Clustering
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Orthogonal Subspace Decomposition: A New Perspective of Learning Discriminative Features for Face Clustering
Learning discriminative node features is the key to further improve the performance of graph-based face clustering.
A Clustering-Based Method for Automatic Educational Video Recommendation Using Deep Face-Features of Lecturers
Discovering and accessing specific content within educational video bases is a challenging task, mainly because of the abundance of video content and its diversity.
Multi-Face: Self-supervised Multiview Adaptation for Robust Face Clustering in Videos
Face clustering for long-form content such as movies is challenging because of variations in appearance and lack of large-scale labeled video resources.
Dynamic Character Graph via Online Face Clustering for Movie Analysis
An effective approach to automated movie content analysis involves building a network (graph) of its characters.
Improving Face Recognition by Clustering Unlabeled Faces in the Wild
While deep face recognition has benefited significantly from large-scale labeled data, current research is focused on leveraging unlabeled data to further boost performance, reducing the cost of human annotation.
FACE CLUSTERING FACE RECOGNITION OUT-OF-DISTRIBUTION DETECTION SEMI-SUPERVISED IMAGE CLASSIFICATION
Density-Aware Feature Embedding for Face Clustering
In this paper, we propose a Density-Aware Feature Embedding Network (DA-Net) for the task of face clustering, which utilizes both local and non-local information, to learn a robust feature embedding.
Is an Affine Constraint Needed for Affine Subspace Clustering?
Specifically, our analysis provides conditions that guarantee the correctness of affine subspace clustering methods both with and without the affine constraint, and shows that these conditions are satisfied for high-dimensional data.
Clustering based Contrastive Learning for Improving Face Representations
We demonstrate our method on the challenging task of learning representations for video face clustering.
Constructing the F-Graph with a Symmetric Constraint for Subspace Clustering
Based on further studying the low-rank subspace clustering (LRSC) and L2-graph subspace clustering algorithms, we propose a F-graph subspace clustering algorithm with a symmetric constraint (FSSC), which constructs a new objective function with a symmetric constraint basing on F-norm, whose the most significant advantage is to obtain a closed-form solution of the coefficient matrix.
Subspace Clustering with Active Learning
In this paper, we propose an active learning framework for subspace clustering that sequentially queries informative points and updates the subspace model.