8 accepted papers
Cross-subject visual decoding aims to reconstruct visual experiences from brain activity across individuals, enabling more scalable and practical brain-computer interfaces. However, existing methods often suffer from degraded performance when adapting to new subjects with limited data, as they strug
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
Model merging combines multiple fine-tuned models into a single model by $\textit{adding}$ their weight updates, providing a lightweight alternative to retraining. Existing methods primarily target resolving conflicts between task updates, leaving the failure mode of over-counting shared knowledge u…
Contrastive Language-Image Pretraining (CLIP) has been widely used in vision tasks. Notably, CLIP has demonstrated promising performance in few-shot learning (FSL). However, existing CLIP-based methods in training-free FSL (i.e., without the requirement of additional training) mainly learn different…
Domain Generalization (DG) aims to enable models to generalize to unseen target domains by learning from multiple source domains. Existing DG methods primarily rely on convolutional neural networks (CNNs), which inherently learn texture biases due to their limited receptive fields, making them prone…
Domain generalization (DG) aims to learn a model that generalizes well to unseen target domains utilizing multiple source domains without re-training. Most existing DG works are based on convolutional neural networks (CNNs). However, the local operation of the convolution kernel makes the model focu…
Deep Neural Networks have exhibited considerable success in various visual tasks. However, when applied to unseen test datasets, state-of-the-art models often suffer performance degradation due to domain shifts. In this paper, we introduce a novel approach for domain generalization from a novel pers…