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Found 34 papers, 11 papers with code
Semantic Invariant Multi-view Clustering with Fully Incomplete Information
To address this problem, we present a novel framework called SeMantic Invariance LEarning (SMILE) for multi-view clustering with incomplete information that does not require any paired samples.
Exploring and Exploiting Uncertainty for Incomplete Multi-View Classification
Second, the quality of the imputed data itself is of high uncertainty.
Reweighted Mixup for Subpopulation Shift
Subpopulation shift exists widely in many real-world applications, which refers to the training and test distributions that contain the same subpopulation groups but with different subpopulation proportions.
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification
Failure to recognize samples from the classes unseen during training is a major limit of artificial intelligence (AI) in real-world implementation of retinal anomaly classification.
Fairness-guided Few-shot Prompting for Large Language Models
Large language models have demonstrated surprising ability to perform in-context learning, i. e., these models can be directly applied to solve numerous downstream tasks by conditioning on a prompt constructed by a few input-output examples.
Balanced Audiovisual Dataset for Imbalance Analysis
We surprisingly find that: the multimodal models with existing imbalance algorithms consistently perform worse than the unimodal one on specific subsets, in accordance with the modality bias.
Graph Matching with Bi-level Noisy Correspondence
In this paper, we study a novel and widely existing problem in graph matching (GM), namely, Bi-level Noisy Correspondence (BNC), which refers to node-level noisy correspondence (NNC) and edge-level noisy correspondence (ENC).
Ranked #1 on
Graph Matching
on Willow Object Class
UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup
Importance reweighting is a normal way to handle the subpopulation shift issue by imposing constant or adaptive sampling weights on each sample in the training dataset.
Trusted Multi-View Classification with Dynamic Evidential Fusion
With this in mind, we propose a novel multi-view classification algorithm, termed trusted multi-view classification (TMC), providing a new paradigm for multi-view learning by dynamically integrating different views at an evidence level.
Uncertainty-Aware Multi-View Representation Learning
Under the help of uncertainty, DUA-Nets weigh each view of individual sample according to data quality so that the high-quality samples (or views) can be fully exploited while the effects from the noisy samples (or views) will be alleviated.
Multimodal Dynamics: Dynamical Fusion for Trustworthy Multimodal Classification
To the best of our knowledge, this is the first work to jointly model both feature and modality variation for different samples to provide trustworthy fusion in multi-modal classification.
Trustworthy Long-Tailed Classification
To address these issues, we propose a Trustworthy Long-tailed Classification (TLC) method to jointly conduct classification and uncertainty estimation to identify hard samples in a multi-expert framework.
Ranked #14 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions
Multimodal regression is a fundamental task, which integrates the information from different sources to improve the performance of follow-up applications.
Identifying Incorrect Classifications with Balanced Uncertainty
Uncertainty estimation is critical for cost-sensitive deep-learning applications (i. e. disease diagnosis).
Trusted Multi-View Classification
To this end, we propose a novel multi-view classification method, termed trusted multi-view classification, which provides a new paradigm for multi-view learning by dynamically integrating different views at an evidence level.
Multi-View Disentangled Representation
Learning effective representations for data with multiple views is crucial in machine learning and pattern recognition.
Deep Partial Multi-View Learning
Although multi-view learning has made signifificant progress over the past few decades, it is still challenging due to the diffificulty in modeling complex correlations among different views, especially under the context of view missing.
LRA: an accelerated rough set framework based on local redundancy of attribute for feature selection
Based on the theorem, we propose the LRA framework for accelerating rough set algorithms.
SPL-MLL: Selecting Predictable Landmarks for Multi-Label Learning
Although significant progress achieved, multi-label classification is still challenging due to the complexity of correlations among different labels.
M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients
Then, we use a modality-specific network to extract implicit and high-level features from different MR scans.
Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning
In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images.
Ball k-means
The assigned cluster of the points in the stable area is not changed in the current iteration while the points in the annulus area will be adjusted within a few neighbor clusters in the current iteration.
CPM-Nets: Cross Partial Multi-View Networks
Despite multi-view learning progressed fast in past decades, it is still challenging due to the difficulty in modeling complex correlation among different views, especially under the context of view missing.
Multi-view Deep Subspace Clustering Networks
2) The end-to-end learning manner of deep learning is not well used in multi-view clustering.
Ranked #1 on
Multi-view Subspace Clustering
on ORL
AE2-Nets: Autoencoder in Autoencoder Networks
The proposed method has the following merits: (1) our model jointly performs view-specific representation learning (with the inner autoencoder networks) and multi-view information encoding (with the outer autoencoder networks) in a unified framework; (2) due to the degradation process from the latent representation to each single view, our model flexibly balances the complementarity and consistence among multiple views.
Unsupervised Degradation Learning for Single Image Super-Resolution
Specifically, we propose a degradation network to model the real-world degradation process from HR to LR via generative adversarial networks, and these generated realistic LR images paired with real-world HR images are exploited for training the SR reconstruction network, forming the first cycle.
Disc-aware Ensemble Network for Glaucoma Screening from Fundus Image
Specifically, a novel Disc-aware Ensemble Network (DENet) for automatic glaucoma screening is proposed, which integrates the deep hierarchical context of the global fundus image and the local optic disc region.
Saliency Detection for Stereoscopic Images Based on Depth Confidence Analysis and Multiple Cues Fusion
Stereoscopic perception is an important part of human visual system that allows the brain to perceive depth.
K-means clustering for efficient and robust registration of multi-view point sets
Subsequently, the shape comprised by all cluster centroids is used to sequentially estimate the rigid transformation for each point set.
Exclusivity-Consistency Regularized Multi-View Subspace Clustering
Multi-view subspace clustering aims to partition a set of multi-source data into their underlying groups.
Latent Multi-View Subspace Clustering
In this paper, we propose a novel Latent Multi-view Subspace Clustering (LMSC) method, which clusters data points with latent representation and simultaneously explores underlying complementary information from multiple views.
SketchNet: Sketch Classification With Web Images
In this study, we present a weakly supervised approach that discovers the discriminative structures of sketch images, given pairs of sketch images and web images.
Low-Rank Tensor Constrained Multiview Subspace Clustering
We introduce a low-rank tensor constraint to explore the complementary information from multiple views and, accordingly, establish a novel method called Low-rank Tensor constrained Multiview Subspace Clustering (LT-MSC).
Diversity-Induced Multi-View Subspace Clustering
In this paper, we focus on how to boost the multi-view clustering by exploring the complementary information among multi-view features.
