mlsanityData to 7 Oct 2026

Trusted Multi-View Classification

arXiv 2102.02051 ↗First posted 3 Feb 2021cs.LG cs.CVcited by 94

Not on the map. Why?

Abstract (arXiv)

Multi-view classification (MVC) generally focuses on improving classification accuracy by using information from different views, typically integrating them into a unified comprehensive representation for downstream tasks. However, it is also crucial to dynamically assess the quality of a view for different samples in order to provide reliable uncertainty estimations, which indicate whether predictions can be trusted. 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. The algorithm jointly utilizes multiple views to promote both classification reliability and robustness by integrating evidence from each view. To achieve this, the Dirichlet distribution is used to model the distribution of the class probabilities, parameterized with evidence from different views and integrated with the Dempster-Shafer theory. The unified learning framework induces accurate uncertainty and accordingly endows the model with both reliability and robustness for out-of-distribution samples. Extensive experimental results validate the effectiveness of the proposed model in accuracy, reliability and robustness.

Key facts. Cited by 94 papers in the map (data through 7 Oct 2026). Builds on 21 of its 56 references that are in the map. First posted on arXiv 3 Feb 2021.

Builds on

21 of 56 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
  5. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  6. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  7. 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,345
  8. 2019Contrastive Multiview Codingcited by 1,297
  9. 2019Learning Representations by Maximizing Mutual Information Across Viewscited by 733
  10. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 728
  11. 2020Contrastive Multi-View Representation Learning on Graphscited by 651
  12. 2019What Makes Training Multi-Modal Classification Networks Hard?cited by 340
9 more
  1. 2013Black Box Variational Inferencecited by 317
  2. 2016On Deep Multi-View Representation Learning: Objectives and Optimizationcited by 162
  3. 2015Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inferencecited by 159
  4. 2019Supervised Multimodal Bitransformers for Classifying Images and Textcited by 130
  5. 2020Confidence-Aware Learning for Deep Neural Networkscited by 113
  6. 2018Efficient Large-Scale Multi-Modal Classificationcited by 57
  7. 2016Deep Variational Canonical Correlation Analysiscited by 44
  8. 2018Uncertainty-Aware Attention for Reliable Interpretation and Predictioncited by 24
  9. 2017Revisiting the Effectiveness of Off-the-shelf Temporal Modeling Approaches for Large-scale Video Classificationcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 11; 2023: 20; 2024: 31; 2025: 13; 2026: 15.

94 papers.

Sort:

By area:

  1. 2022Trusted Multi-View Classification with Dynamic Evidential Fusioncited by 167
  2. 2023Multi-modal Learning with Missing Modality via Shared-Specific Feature Modellingcited by 159
  3. 2021What Makes Multi-modal Learning Better than Single (Provably)cited by 151
  4. 2022Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networkscited by 92
  5. 2021Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibrationcited by 91
  6. 2023Provable Dynamic Fusion for Low-Quality Multimodal Datacited by 81
  7. 2023A Review of Uncertainty Estimation and its Application in Medical Imagingcited by 59
  8. 2024ReconBoost: Boosting Can Achieve Modality Reconcilementcited by 56
  9. 2021Trustworthy Long-Tailed Classificationcited by 51
  10. 2024Multimodal Fusion on Low-quality Data: A Comprehensive Surveycited by 48
  11. 2024Reliable Conflictive Multi-View Learningcited by 45
  12. 2023ID-like Prompt Learning for Few-Shot Out-of-Distribution Detectioncited by 42
38 more
  1. 2021Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributionscited by 39
  2. 2022TBraTS: Trusted Brain Tumor Segmentationcited by 30
  3. 2024Coupled Mamba: Enhanced Multi-modal Fusion with Coupled State Space Modelcited by 30
  4. 2023Decoupled Contrastive Multi-View Clustering with High-Order Random Walkscited by 23
  5. 2023Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrievalcited by 22
  6. 2023Uncertainty-inspired Open Set Learning for Retinal Anomaly Identificationcited by 18
  7. 2022Fast Road Segmentation via Uncertainty-aware Symmetric Networkcited by 17
  8. 2023Information Recovery-Driven Deep Incomplete Multiview Clustering Networkcited by 16
  9. 2024Trusted Unified Feature-Neighborhood Dynamics for Multi-View Classificationcited by 15
  10. 2025Uncertainty-Aware Graph Neural Networks: A Multi-Hop Evidence Fusion Approachcited by 14
  11. 2022Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Predictioncited by 13
  12. 2023Semantic Invariant Multi-view Clustering with Fully Incomplete Informationcited by 12
  13. 2024CREST: Cross-modal Resonance through Evidential Deep Learning for Enhanced Zero-Shot Learningcited by 12
  14. 2024Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language Modelcited by 12
  15. 2024Revisiting Multimodal Fusion for 3D Anomaly Detection from an Architectural Perspectivecited by 12
  16. 2023Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimationcited by 11
  17. 2022Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picturecited by 10
  18. 2024Beyond Uncertainty: Evidential Deep Learning for Robust Video Temporal Groundingcited by 10
  19. 2024Rethinking Misalignment in Vision-Language Model Adaptation from a Causal Perspectivecited by 9
  20. 2024Trustworthy Multimodal Fusion for Sentiment Analysis in Ordinal Sentiment Spacecited by 8
  21. 2024Trusted Multi-view Learning under Noisy Supervisioncited by 8
  22. 2023Uncertainty-informed Mutual Learning for Joint Medical Image Classification and Segmentationcited by 7
  23. 2023Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Stagingcited by 7
  24. 2023E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognitioncited by 7
  25. 2024Masked Two-channel Decoupling Framework for Incomplete Multi-view Weak Multi-label Learningcited by 7
  26. 2026Unbiased Dynamic Multimodal Fusioncited by 7
  27. 2023Multi-View Class Incremental Learningcited by 5
  28. 2023What Makes for Robust Multi-Modal Models in the Face of Missing Modalities?cited by 5
  29. 2024Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuitscited by 5
  30. 2024Meta-Learned Modality-Weighted Knowledge Distillation for Robust Multi-Modal Learning with Missing Datacited by 5
  31. 2024Towards the Causal Complete Cause of Multi-Modal Representation Learningcited by 5
  32. 2024Rebalanced Vision-Language Retrieval Considering Structure-Aware Distillationcited by 5
  33. 2022Trusted Multi-Scale Classification Framework for Whole Slide Imagecited by 4
  34. 2023Deep Equilibrium Multimodal Fusioncited by 4
  35. 2024EPL: Evidential Prototype Learning for Semi-supervised Medical Image Segmentationcited by 4
  36. 2024Navigating Conflicting Views: Harnessing Trust for Learningcited by 4
  37. 2024Completed Feature Disentanglement Learning for Multimodal MRIs Analysiscited by 4
  38. 2024RSEA-MVGNN: Multi-View Graph Neural Network with Reliable Structural Enhancement and Aggregationcited by 4

50 most cited of 94 All 94 (JSON)