Dempster-Shafer Theory

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Evidential Deep Learning for Multi-Modal Anti-UAV Detection
Sep 1, 2026Dmitry Golovchits, Seyed Sahand Mohammadi Ziabari, Ali Mohammed Mansoor AlsahagEvidential Deep Learning Dempster-Shafer Theory
University of Amsterdam Amsterdam, The Netherlands
Robust Dempster-Shafer Evidence Fusion with Chaos-Conflict Measurement and Historical-Experience Weighting
Aug 13, 2026Huiyu Li, Weibo Liu, Xinru Xu +3Dempster-Shafer Theory
School of Business, Central South University, Changsha, Hunan, People’s Republic of China, 410083 · School of Management, Shandong University, Jinan, Shandong, People’s Republic of China, 250100
Decision-Aware Approximation of Belief Functions for Evidential Combinatorial Optimization
Aug 11, 2026Sohaib AfifiDempster-Shafer Theory Combinatorial Optimization
Univ. Artois, UR 3926, Laboratoire de Génie Informatique et d’Automatique de l’Artois (LGI2A), Béthune, France
Conformal Fusion Under Missing Modalities
Aug 7, 2026Alireza MoayedikiaMissing-Modality Learning Group-Conditional Conformal Prediction
Department of Business Technology and Entrepreneurship Swinburne University of Technology Hawthorn, VIC 3122, Australia
Evidential Fusion Network for Multimodal Survival Prediction under Missing Modalities
Jun 18, 2026Yucheng Xing, Hailan Mo, Zi Wang +2Missing-Modality Learning Uncertainty Quantification
National University of Singapore, Singapore · Imperial College London, London, UK
Multi-Agent Framework for Audit Risk Assessment with Explicit Uncertainty and Evidence Conflict Modeling
Jun 14, 2026Yuhan Wang, Manqing Wang, Yixuan Lu +2Uncertainty Quantification Dempster-Shafer Theory
Columbia University New York, USA · Trine University Phoenix, USA · University of Sofia Palo Alto, USA +2
BELIEF: Structured Evidence Modeling and Uncertainty-Aware Fusion for Biomedical Question Answering
May 17, 2026Chang Zong, Hao Ning, Siliang Tang +2Healthcare Evidence-Grounded Reasoning
Evidential Information Fusion on Possibilistic Structure
May 16, 2026Qianli Zhou, Ye Cui, Zhen Li +2Dempster-Shafer Theory Decision-Making under Uncertainty
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China · Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada · China Mobile Information Technology Center, Beijing, 100029, China +4
Statistical inference with belief functions: A survey
May 8, 2026Fabio CuzzolinDempster-Shafer Theory
Institute for AI, Data Analysis and Systems (AIDAS) · School of Engineering, Computing & Mathematics, Oxford Brookes University

Sep 1, 2026, cs.CVJ/K move · Enter open · S save

Evidential Deep Learning for Multi-Modal Anti-UAV Detection

Dmitry Golovchits, Seyed Sahand Mohammadi Ziabari, Ali Mohammed Mansoor Alsahag

University of Amsterdam Amsterdam, The Netherlands

Evidential Deep Learning Dempster-Shafer Theory Object Detection Aerial Object Detection Multimodal Sensor Fusion

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Anti-UAV systems increasingly fuse multiple sensors, yet their detection heads provide no per-modality reliability signal. This study evaluates whether evidential deep learning (EDL) heads, Dempster-Shafer (DS) evidence fusion, and uncertainty-driven temporal sensor gating improve anti-UAV detection through a controlled ablation on three benchmarks: thermal tracking (AntiUAV600), RGB-audio-RF classification (TRIDENT), and RGB-IR tracking (MM-UAV). The EDL training objective improves accuracy over retrained sigmoid baselines (+5.9 percentage points in accuracy and a tripled tracker-on-absent rate in E1; +4.8 percentage points in classification accuracy in E2, surviving a clip-clustered bootstrap, p = 0.011) and ranks classification errors substantially better (entropy UAUC approximately 0.94 vs. 0.51). The remaining components do not support their respective hypotheses. DS fusion does not outperform simple probability averaging. Dirichlet vacuity adds no ranking power beyond predictive entropy and inverts at the detection level, where extreme background imbalance causes it to encode class membership rather than error likelihood, a failure also observed for entropy and sigmoid confidence. Temporal gating preserves accuracy only when nearly inactive and yields no realised latency saving on shared-backbone hardware. The benefit of evidential learning therefore arises primarily from its training objective rather than its uncertainty estimate; a crop-level control further localises the detection-level breakdown to anchor-level evaluation rather than the learned representation.

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