arXiv:2511.21777v3 Announce Type: replace
Abstract: Methane is a potent greenhouse gas, responsible for roughly 30% of warming since pre-industrial times. A small number of large point sources account for a disproportionate share of emissions, creatin…
arXiv:2507.13706v2 Announce Type: replace
Abstract: This paper introduces two quasi-metrics for performance assessment of multi-object tracking (MOT) algorithms. One quasi-metric is an extension of the generalised optimal subpattern assignment (GOSPA)…
arXiv:2604.22579v1 Announce Type: cross
Abstract: We study whether deep networks for medical imaging learn useful nonrobust features – predictive input patterns that are not human interpretable and highly susceptible to small adversarial perturbations…
arXiv:2210.05513v2 Announce Type: replace
Abstract: We introduce ViFiCon, a self-supervised contrastive scheme which learns a cross-modal association between vision and wireless modalities. Specifically, the system uses pedestrian data collected from …
arXiv:2604.22177v1 Announce Type: new
Abstract: Multimodal MRI offers complementary information for brain tumor segmentation, but clinical scans often lack one or more modalities, which degrades segmentation performance. In this paper, we propose UniM…
arXiv:2604.22192v1 Announce Type: new
Abstract: Chart-to-code generation demands strict visual precision and syntactic correctness from Vision-Language Models (VLMs). However, existing approaches are fundamentally constrained by data-centric limitatio…
arXiv:2510.12328v5 Announce Type: replace
Abstract: Accurate rainfall forecasting, particularly for extreme events, remains a significant challenge in climatology and the Earth system. This paper presents novel physics-informed Graph Neural Networks (…
arXiv:2604.22348v1 Announce Type: new
Abstract: Clinical risk prediction using longitudinal medical data supports individualized care. Self-supervised foundation models have emerged as a promising approach for leveraging large-scale unlabeled healthca…
arXiv:2604.22240v1 Announce Type: new
Abstract: Generative world models increasingly rely on 4D occupancy for realistic autonomous driving simulation. However, existing generation frameworks depend on rigid geometric conditions (e.g., explicit traject…
arXiv:2504.06148v3 Announce Type: replace
Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive capabilities in visual-text processing. However, existing static image-text benchmarks are insufficient fo…