12 accepted papers
Multi-agent multi-objective systems (MAMOS) have emerged as powerful frameworks for modelling complex decision-making problems across various real-world domains, such as robotic exploration, autonomous traffic management, and sensor network optimisation. MAMOS enhances scalability and robustness thr
Vision-language models (VLMs) enable text-guided object detection but degrade severely under cross-view scenarios where ground and aerial viewpoints differ in altitude, scale, and spatial layout. These geometric changes introduce systematic complexity variations between viewpoints, e.g., ground view
Segmenting oil spills from Synthetic Aperture Radar (SAR) imagery remains challenging due to severe appearance variability, scale heterogeneity, and the absence of temporal continuity in real world monitoring scenarios. While foundation models such as Segment Anything (SAM) enable prompt driven segm…
Multi-UAV cooperative coverage missions across geographically separated regions face significant challenges due to potential UAV failures during mission execution. To address the challenges of efficient multi-region coverage and dynamic failure handling, this paper presents a novel real-time path-re
跨越地理分离的多无人机协作覆盖任务 各地区因潜在无人机故障面临重大挑战 在执行任务时。解决效率挑战 本文介绍了多区域覆盖与动态失效处理 一种新型实时路径可重构覆盖规划算法 跨越地理区域的多无人机覆盖路径规划 具备实时路径重配置功能。拟议 方法，GRIT-M（贪婪修复初始化多重的禁忌搜索） 扩展了GRIT算法，以高效处理初始 无人机在任务中故障时的规划和在线路径修复 执行。与现有方法不同，这些方法要么只专注于单一区域 无论覆盖率或缺失，GRIT-M都包含了故障处理机制 通过三项关键创新实现区域特定知识：（1） a 优化区域间的复合过渡成本函数 运动，（2ᦀ
Multimodal learning often encounters the under-optimized problem and may have worse performance than unimodal learning. Existing methods attribute this problem to the imbalanced learning between modalities and rebalance them through gradient modulation. However, they fail to explain why the dominant…
Multimodal learning often encounters the under-optimized problem and may perform worse than unimodal learning. Existing approaches attribute this issue to imbalanced learning across modalities and tend to address it through gradient balancing. However, this paper argues that balanced learning is not…
Earth observation foundation models have shown strong generalization across multiple Earth observation tasks, but their robustness under real-world perturbations remains underexplored. To bridge this gap, we introduce REOBench, the first comprehensive benchmark for evaluating the robustness of Earth…
Logit knowledge distillation attracts increasing attention due to its practicality in recent studies. However it often suffers inferior performance compared to the feature knowledge distillation. In this paper we argue that existing logit-based methods may be sub-optimal since they only leverage the…
Multimodal learning has shown great potentials in numerous scenes and attracts increasing interest recently. However, it often encounters the problem of missing modality data and thus suffers severe performance degradation in practice. To this end, we propose a general framework called MMANet to ass…
Change detection plays a vital role in monitoring and analyzing temporal changes in Earth observation tasks. This paper proposes a novel adaptive multi-scale and multi-level features fusion network for change detection in very-high-resolution bi-temporal remote sensing images. The proposed approach…
In this paper, we propose a novel video coding scheme to significantly reduce the coding complexity and enhance overall coding efficiency in videos acquired by high mobility devices such as unmanned aerial vehicles (UAVs). In order to reduce the encoded data bits and encoding time to facilitate real…