Face Clustering
21 papers with code • 1 benchmarks • 3 datasets
Face Clustering in the videos
Libraries
Use these libraries to find Face Clustering models and implementations
EasyCom
MovieGraphs
A View From Somewhere (AVFS)
Latest papers with no code
Improved Face Representation via Joint Label Classification and Supervised Contrastive Clustering
Face clustering tasks can learn hierarchical semantic information from large-scale data, which has the potential to help facilitate face recognition.
Learn to Cluster Faces with Better Subgraphs
Face clustering can provide pseudo-labels to the massive unlabeled face data and improve the performance of different face recognition models.
Face Clustering via Graph Convolutional Networks with Confidence Edges
Experiments show that our method outperforms existing methods on the face and person datasets to achieve state-of-the-art.
CLIP-Cluster: CLIP-Guided Attribute Hallucination for Face Clustering
Furthermore, we develop a neighbor-aware proxy generator that fuses the features describing various attributes into a proxy feature to build a bridge among different sub-clusters and reduce the intra-class variance.
On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses
Clustering models constitute a class of unsupervised machine learning methods which are used in a number of application pipelines, and play a vital role in modern data science.
Learn to Cluster Faces via Pairwise Classification
However, they usually suffer from excessive memory consumption especially on large-scale graphs, and rely on empirical thresholds to determine the connectivities between samples in inference, which restricts their applications in various real-world scenes.
Character-focused Video Thumbnail Retrieval
Prominence and interactions: Character(s) in the thumbnail should be important character(s) in the video, to prevent the algorithm from suggesting non-representative frames as candidates.
Analysis of Sparse Subspace Clustering: Experiments and Random Projection
Clustering can be defined as the process of assembling objects into a number of groups whose elements are similar to each other in some manner.
Using Active Speaker Faces for Diarization in TV shows
Speaker diarization is one of the critical components of computational media intelligence as it enables a character-level analysis of story portrayals and media content understanding.
Self-supervised Video-centralised Transformer for Video Face Clustering
We also investigate face clustering in egocentric videos, a fast-emerging field that has not been studied yet in works related to face clustering.
