Online Clustering
25 papers with code • 0 benchmarks • 0 datasets
Models that learn to label each image (i.e. cluster the dataset into its ground truth classes) without seeing the ground truth labels. Under the online scenario, data is in the form of streams, i.e., the whole dataset could not be accessed at the same time and the model should be able to make cluster assignments for new data without accessing the former data.
Image Credit: Online Clustering by Penalized Weighted GMM
Benchmarks
These leaderboards are used to track progress in Online Clustering
Most implemented papers
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.
Twin Contrastive Learning for Online Clustering
XLearning-SCU/2022-IJCV-TCL
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Specifically, we find that when the data is projected into a feature space with a dimensionality of the target cluster number, the rows and columns of its feature matrix correspond to the instance and cluster representation, respectively.
CrOC: Cross-View Online Clustering for Dense Visual Representation Learning
More importantly, the clustering algorithm conjointly operates on the features of both views, thereby elegantly bypassing the issue of content not represented in both views and the ambiguous matching of objects from one crop to the other.
Links: A High-Dimensional Online Clustering Method
We present a novel algorithm, called Links, designed to perform online clustering on unit vectors in a high-dimensional Euclidean space.
Contextual Bandit with Adaptive Feature Extraction
Our experiments on a variety of datasets, and both in stationary and non-stationary environments of several kinds demonstrate clear advantages of the proposed adaptive representation learning over the standard contextual bandit based on "raw" input contexts.
A real-time and unsupervised face Re-Identification system for Human-Robot Interaction
In this paper, we present an effective and unsupervised face Re-ID system which simultaneously re-identifies multiple faces for HRI.
Unsupervised Progressive Learning and the STAM Architecture
We first pose the Unsupervised Progressive Learning (UPL) problem: an online representation learning problem in which the learner observes a non-stationary and unlabeled data stream, learning a growing number of features that persist over time even though the data is not stored or replayed.
Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-means
Kaixhin/EC
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Recently, neuro-inspired episodic control (EC) methods have been developed to overcome the data-inefficiency of standard deep reinforcement learning approaches.
Contrastive Clustering
Yunfan-Li/Contrastive-Clustering
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In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning.
Group-aware Label Transfer for Domain Adaptive Person Re-identification
zkcys001/UDAStrongBaseline
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CVPR 2021
In this paper, we propose a Group-aware Label Transfer (GLT) algorithm, which enables the online interaction and mutual promotion of pseudo-label prediction and representation learning.
