The paper investigates the use of evidential deep learning (EDL) for multi‑modal anti‑UAV detection, comparing it with sigmoid baselines, Dempster‑Shafer evidence fusion, and uncertainty‑driven temporal sensor gating across three benchmarks (thermal tracking, RGB‑audio‑RF classification, and RGB‑IR tracking). EDL improves accuracy by up to 5.9 percentage points and better ranks classification errors, while the other components (DS fusion, Dirichlet vacuity, temporal gating) do not provide the expected benefits. The study concludes that the primary advantage of EDL stems from its training objective rather than its uncertainty estimates.
By Dmitry Golovchits, Seyed Sahand Mohammadi Ziabari, Ali Mohammed Mansoor Alsahag
arXiv:2608. 09202v1 Announce Type: new Abstract: Modern autonomous vehicles are equipped with multiple sensors, such as cameras, LiDAR, and radar, for comprehensive environmental perception.
By Junyao Wang, Yulin Xu, Yu Li, Pramod Khargonekar, Mohammad Abdullah Al Faruque
CUSP (Collective Uncertainty through Semantic Opinion Pooling) is a training‑free framework that aggregates responses from multiple vision‑language models into a shared semantic space, producing a pooled opinion and two system‑level uncertainty signals: collective uncertainty (dispersion) and Jensen‑Shannon divergence (model conflict). It decomposes collective entropy into the mean of individual semantic entropies plus JSD, enabling reliable uncertainty estimation without token logits or calibration labels. In static ensembles, collective uncertainty outperforms baseline methods for error detection and abstention, while JSD excels in commercial settings, and the pooled prediction consistently improves accuracy over individual models.
"whyItMatters":"CUSP provides a practical, model‑agnostic way to quantify system‑level reliability and improve decision‑making in multimodal reasoning tasks."
By Chung-En Johnny Yu, David Garcia, Brian Jalaian, Nathaniel D. Bastian
The paper introduces FlexibleFusion, a method for infrared-visible object detection that adapts to both complete and missing-modality scenarios. It employs a Modality-Aware Experts Collaboration mechanism to selectively fuse cross-modal or intra-modal pathways, and a Residual Self-Paced Entropic Optimal Transport module to align heterogeneous feature distributions without heavy optimization. Experiments demonstrate consistent performance across various modality configurations.
By Yue Zhao, Hua Yu, Yukun Zhao, Yuzhi Zhang, Maoguo Gong, Xin Mei, Zhuping Hu, Yanchi Li, A. K. Qin
RA‑SOD is a new RGB‑Thermal salient object detection framework that explicitly models the reliability of each modality. It introduces a reliability‑conditioned representation, an uncertainty‑guided dual‑stream refinement, and a pixel‑wise modality competition mechanism to adaptively compensate degraded features and suppress unreliable evidence. Experiments on four benchmarks show that RA‑SOD achieves state‑of‑the‑art performance and remains robust under severe modality degradation.
By Hongbo Gao, Zhengyu Li, Xueru Nie, Dihao Zhu, Lijun Zhao, Yunke Wang, Chang Xu
arXiv:2608. 07183v1 Announce Type: new Abstract: Multimodal fusion architectures typically assume all modalities are available at inference, yet sensor failures, acquisition variability, and cost constraints routinely produce incomplete observations.
By Alireza Moayedikia
arXiv:2606. 10819v1 Announce Type: cross Abstract: RS-MLLMs enable natural-language understanding and spatial reasoning over earth observation imagery.
By Miaoxin Cai, Guanqun Wang, Wei Zhang, Guangyao Zhou, Yin Zhuang, Tong Zhang, Hao Wang, He Chen, Jun Li
The paper presents M2Heat, a physics-inspired framework for fusing hyperspectral and LiDAR data in land‑cover classification. It introduces a visual heat conduction module (vHeat) and Frequency Value Embeddings (FVEs) to model anisotropic information flow, capturing global dependencies with sub‑quadratic complexity. Combined with a Cross‑Frequency Fusion (CFF) module, M2Heat delivers discriminative, robust features and achieves competitive performance on Trento, Houston2013, and Augsburg benchmarks while offering an interpretable heat‑conduction perspective.
By Kan Wei, Jiahui Cui, Jing Yao, Xinyu Zhao, Lei Wang, Pedram Ghamisi
arXiv:2608.21099v1 Announce Type: cross
Abstract: Multi-modal object detection is essential for robust scene understanding in challenging conditions, including low-light and adverse environments. Rec...
By Jiekang Feng, Zhihe Fan, Yunqi Zhu, Xinjie Yao, Yueying Zhang, Yike Gao, Ranxin Li, Guanzuo Chen
arXiv:2609.24668v1 Announce Type: new
Abstract: Reliable uncertainty estimation is essential for deploying object detectors in autonomous systems operating in uncertain environments. Evidential Deep...
By Simon Barbarit-Gaboriau (LITIS - STI, INSA Rouen Normandie), Hind Laghmara (LITIS - STI), R\'emi Boutteau (LITIS - STI), Samia Ainouz (LITIS, LITIS - STI)
arXiv:2609.13777v1 Announce Type: cross
Abstract: Learned components are increasingly integrated into geometric visual--inertial estimators to provide motion, depth, bias, uncertainty, or confidence...
By Jinchang Zhang, Guoyu Lu
arXiv:2607. 25524v1 Announce Type: cross Abstract: Unmanned aerial vehicle (UAV)-satellite cross-view geo-localization matches UAV images against satellite imagery and has achieved impressive accuracy on clean (non-degraded) image benchmarks.
By Haochen Jiang, Jialei Pan, Yuzhe Sun, Zhe Dong, Lecheng Ren, Yanfeng Gu, Tianzhu Liu