arXiv:2605. 22259v2 Announce Type: replace Abstract: Heterogeneous sensor fusion is vital for detecting, localizing, and classifying CBRNE threats.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
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.
The paper introduces the Multi-Context Fusion Transformer (MFT), a model that predicts pedestrian crossing intentions in urban settings by integrating four types of contextual information—pedestrian behavior, environment, localization, and vehicle motion—through a progressive fusion strategy. MFT uses intra-context attention for reciprocal interactions within each context, cross-context attention to combine these contexts into a global representation, and guided attention mechanisms to refine both context tokens and the global token. Experiments on JAADbeh, JAADall, and PIE datasets show MFT outperforms existing methods with accuracies of 73%, 93%, and 90% respectively, and ablation studies confirm the importance of each network component and input context.
arXiv:2603. 26629v2 Announce Type: replace Abstract: Multimodal fusion requires integrating information from multiple sources that may conflict depending on context.
arXiv:2606. 20538v1 Announce Type: new Abstract: Bayesian predictive inference provides a principled framework for uncertainty quantification, data efficiency, and robust generalization.
arXiv:2509.00754v3 Announce Type: replace Abstract: Dempster--Shafer theory (DST) provides an attractive framework for multi-source information fusion. However, its effectiveness critically depends o...
Infrared-visible image fusion (IVIF) is pivotal for multimodal perception, yet reconciling the inherent information disparity between thermal and textural features remains a fundamental challenge. Existing prior-guided methods often rely on static constraints that induce optimization conflicts or utilize extrinsic semantic priors from large-scale foundation models (e.