# Trusted Unified Feature-Neighborhood Dynamics for Multi-View Classification

arXiv: https://arxiv.org/abs/2409.00755 · HTML: https://mlsanity.com/paper/2409.00755/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 1 Sep 2024.

## Builds on (most cited first, 12 of 12)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018)
- [Trusted Multi-View Classification with Dynamic Evidential Fusion](https://mlsanity.com/paper/2204.11423/) (2022)
- [On Deep Multi-View Representation Learning: Objectives and Optimization](https://mlsanity.com/paper/1602.01024/) (2016)
- [Trusted Multi-View Classification](https://mlsanity.com/paper/2102.02051/) (2021)
- [Greedy Policy Search: A Simple Baseline for Learnable Test-Time Augmentation](https://mlsanity.com/paper/2002.09103/) (2020)
- [Noisy-Correspondence Learning for Text-to-Image Person Re-identification](https://mlsanity.com/paper/2308.09911/) (2023)
- [Uncertainty-Aware Multi-View Representation Learning](https://mlsanity.com/paper/2201.05776/) (2022)
- [Unpaired Multi-View Graph Clustering with Cross-View Structure Matching](https://mlsanity.com/paper/2307.03476/) (2023)
- [Beyond Uncertainty: Evidential Deep Learning for Robust Video Temporal Grounding](https://mlsanity.com/paper/2408.16272/) (2024)
- [Evidential Deep Partial Multi-View Classification With Discount Fusion](https://mlsanity.com/paper/2408.13123/) (2024)

## Built on by (most cited first, 15 of 15)

- [VistaDPO: Video Hierarchical Spatial-Temporal Direct Preference Optimization for Large Video Models](https://mlsanity.com/paper/2504.13122/) (2025)
- [Temporal Regularization Makes Your Video Generator Stronger](https://mlsanity.com/paper/2503.15417/) (2025)
- [EvoEmpirBench: Dynamic Spatial Reasoning with Agent-ExpVer](https://mlsanity.com/paper/2509.12718/) (2025)
- [Interpretable Generative and Discriminative Learning for Multimodal and Incomplete Clinical Data](https://mlsanity.com/paper/2510.09513/) (2025)
- [No Place to Hide: Benchmarking Video Hallucination with Background-Controlled Pairs](https://mlsanity.com/paper/2606.31933/) (2026)
- [Towards Robust Uncertainty-Aware Incomplete Multi-View Classification](https://mlsanity.com/paper/2409.06270/) (2024)
- [Enhancing Multi-view Open-set Learning via Ambiguity Uncertainty Calibration and View-wise Debiasing](https://mlsanity.com/paper/2508.01227/) (2025)
- [A Re-node Self-training Approach for Deep Graph-based Semi-supervised Classification on Multi-view Image Data](https://mlsanity.com/paper/2510.24791/) (2025)
- [Class Similarity-Based Multimodal Classification under Heterogeneous Category Sets](https://mlsanity.com/paper/2506.09745/) (2025)
- [Structure-Aware Prototype Guided Trusted Multi-View Classification](https://mlsanity.com/paper/2511.21021/) (2025)
- [Bayesian Integration of Nonlinear Incomplete Clinical Data](https://mlsanity.com/paper/2602.01924/) (2026)
- [Are Independently Estimated View Uncertainties Comparable? Unified Routing for Trusted Multi-View Classification](https://mlsanity.com/paper/2604.09288/) (2026)
- [Robust Fuzzy Multi-view Learning under View Conflict](https://mlsanity.com/paper/2605.24475/) (2026)
- [Pretraining Reusable Inference Across Views with Synthetic Task Priors](https://mlsanity.com/paper/2608.19115/) (2026)
- [Synchronous Multi-view Neural Diffusion](https://mlsanity.com/paper/2609.39019/) (2026)

## Abstract (arXiv)

Multi-view classification (MVC) faces inherent challenges due to domain gaps and inconsistencies across different views, often resulting in uncertainties during the fusion process. While Evidential Deep Learning (EDL) has been effective in addressing view uncertainty, existing methods predominantly rely on the Dempster-Shafer combination rule, which is sensitive to conflicting evidence and often neglects the critical role of neighborhood structures within multi-view data. To address these limitations, we propose a Trusted Unified Feature-NEighborhood Dynamics (TUNED) model for robust MVC. This method effectively integrates local and global feature-neighborhood (F-N) structures for robust decision-making. Specifically, we begin by extracting local F-N structures within each view. To further mitigate potential uncertainties and conflicts in multi-view fusion, we employ a selective Markov random field that adaptively manages cross-view neighborhood dependencies. Additionally, we employ a shared parameterized evidence extractor that learns global consensus conditioned on local F-N structures, thereby enhancing the global integration of multi-view features. Experiments on benchmark datasets show that our method improves accuracy and robustness over existing approaches, particularly in scenarios with high uncertainty and conflicting views. The code will be made available at https://github.com/JethroJames/TUNED.