Search Results for author:
Found 16 papers, 10 papers with code
Contrastive Clustering
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.
Ranked #4 on
Image Clustering
on STL-10
(using extra training data)
Twin Contrastive Learning for Online Clustering
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.
Ranked #1 on
Short Text Clustering
on Biomedical
Comprehensive and Delicate: An Efficient Transformer for Image Restoration
Vision Transformers have shown promising performance in image restoration, which usually conduct window- or channel-based attention to avoid intensive computations.
Noisy-Correspondence Learning for Text-to-Image Person Re-identification
Text-to-image person re-identification (TIReID) is a compelling topic in the cross-modal community, which aims to retrieve the target person based on a textual query.
Ranked #2 on
Text based Person Retrieval
on ICFG-PEDES
(using extra training data)
RONO: Robust Discriminative Learning With Noisy Labels for 2D-3D Cross-Modal Retrieval
Recently, with the advent of Metaverse and AI Generated Content, cross-modal retrieval becomes popular with a burst of 2D and 3D data.
Cross-modal Active Complementary Learning with Self-refining Correspondence
Recently, image-text matching has attracted more and more attention from academia and industry, which is fundamental to understanding the latent correspondence across visual and textual modalities.
Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric
Fair clustering aims to divide data into distinct clusters while preventing sensitive attributes (\textit{e. g.}, gender, race, RNA sequencing technique) from dominating the clustering.
Ranked #1 on
Image Clustering
on HAR
Decoupled Contrastive Multi-View Clustering with High-Order Random Walks
In recent, some robust contrastive multi-view clustering (MvC) methods have been proposed, which construct data pairs from neighborhoods to alleviate the false negative issue, i. e., some intra-cluster samples are wrongly treated as negative pairs.
Adaptive Meta-learner via Gradient Similarity for Few-shot Text Classification
To address this issue, we propose a novel Adaptive Meta-learner via Gradient Similarity (AMGS) method to improve the model generalization ability to a new task.
Correspondence-Free Domain Alignment for Unsupervised Cross-Domain Image Retrieval
Thanks to the ISS and CCA, our method could encode the discrimination into the domain-invariant embedding space for unsupervised cross-domain image retrieval.
Multiobjective Test Problems with Degenerate Pareto Fronts
The redundancy of some objectives can lead to the multiobjective problem having a degenerate Pareto front, i. e., the dimension of the Pareto front of the $m$-objective problem be less than (m-1).
Kernel Truncated Regression Representation for Robust Subspace Clustering
Our method has the advantages of a closed-form solution and the capacity of clustering data points that lie on nonlinear subspaces.
Locally linear representation for image clustering
There are two popular schemes to construct a similarity graph, i. e., pairwise distance based scheme and linear representation based scheme.
Incomplete Multi-view Clustering via Prototype-based Imputation
Thanks to our dual-stream model, both cluster- and view-specific information could be captured, and thus the instance commonality and view versatility could be preserved to facilitate IMvC.
Image Clustering with External Guidance
The core of clustering is incorporating prior knowledge to construct supervision signals.
PointCloud-Text Matching: Benchmark Datasets and a Baseline
We observe that the data is challenging and with noisy correspondence due to the sparsity, noise, or disorder of point clouds and the ambiguity, vagueness, or incompleteness of texts, which make existing cross-modal matching methods ineffective for PTM.
