PAUL: Uncertainty-Guided Partition and Augmentation for Robust Cross-View Geo-Localization under Noisy Correspondence

Cross-view geo-localization is a critical task for UAV navigation, event detection, and aerial surveying, as it enables matching between drone-captured and satellite imagery. Most existing approaches embed multi-modal data into a joint feature space to maximize the similarity of paired images. However, these methods typically assume perfect alignment of image pairs during training, which rarely holds true in real-world scenarios. In practice, factors such as urban canyon effects, electromagnetic interference, and adverse weather frequently induce GPS drift, resulting in systematic alignment shifts where only partial correspondences exist between pairs. Despite its prevalence, this source of noisy correspondence has received limited attention in current research. In this paper, we formally introduce and address the Noisy Correspondence on Cross-View Geo-Localization (NC-CVGL) problem, aiming to bridge the gap between idealized benchmarks and practical applications. To this end, we propose PAUL (Partition and Augmentation by Uncertainty Learning), a novel framework that partitions and augments training data based on estimated data uncertainty through uncertainty-aware co-augmentation and evidential co-training. Specifically, PAUL selectively augments regions with high correspondence confidence and utilizes uncertainty estimation to refine feature learning, effectively suppressing noise from misaligned pairs. Distinct from traditional filtering or label correction, PAUL leverages both data uncertainty and loss discrepancy for targeted partitioning and augmentation, thus providing robust supervision for noisy samples. Comprehensive experiments validate the effectiveness of individual components in PAUL,which consistently achieves superior performance over other competitive noisy-correspondence-driven methods in various noise ratios.

Impact of Static Friction on Sim2Real in Robotic Reinforcement Learning

In robotic reinforcement learning, the Sim2Real gap remains a critical challenge. However, the impact of Static friction on Sim2Real has been underexplored. Conventional domain randomization methods typically exclude Static friction from their parameter space. In our robotic reinforcement learning task, such conventional domain randomization approaches resulted in significantly underperforming real-world models. To address this Sim2Real challenge, we employed Actuator Net as an alternative to conventional domain randomization. While this method enabled successful transfer to flat-ground locomotion, it failed on complex terrains like stairs. To further investigate physical parameters affecting Sim2Real in robotic joints, we developed a control-theoretic joint model and performed systematic parameter identification. Our analysis revealed unexpectedly high friction-torque ratios in our robotic joints. To mitigate its impact, we implemented Static friction-aware domain randomization for Sim2Real. Recognizing the increased training difficulty introduced by friction modeling, we proposed a simple and novel solution to reduce learning complexity. To validate this approach, we conducted comprehensive Sim2Sim and Sim2Real experiments comparing three methods: conventional domain randomization (without Static friction), Actuator Net, and our Static friction-aware domain randomization. All experiments utilized the Rapid Motor Adaptation (RMA) algorithm. Results demonstrated that our method achieved superior adaptive capabilities and overall performance.

PAL: Prompting Analytic Learning with Missing Modality for Multi-Modal Class-Incremental Learning

Multi-modal class-incremental learning (MMCIL) seeks to leverage multi-modal data, such as audio-visual and image-text pairs, thereby enabling models to learn continuously across a sequence of tasks while mitigating forgetting. While existing studies primarily focus on the integration and utilization of multi-modal information for MMCIL, a critical challenge remains: the issue of missing modalities during incremental learning phases. This oversight can exacerbate severe forgetting and significantly impair model performance. To bridge this gap, we propose PAL, a novel exemplar-free framework tailored to MMCIL under missing-modality scenarios. Concretely, we devise modality-specific prompts to compensate for missing information, facilitating the model to maintain a holistic representation of the data. On this foundation, we reformulate the MMCIL problem into a Recursive Least-Squares task, delivering an analytical linear solution. Building upon these, PAL not only alleviates the inherent under-fitting limitation in analytic learning but also preserves the holistic representation of missing-modality data, achieving superior performance with less forgetting across various multi-modal incremental scenarios. Extensive experiments demonstrate that PAL significantly outperforms competitive methods across various datasets, including UPMC-Food101 and N24News, showcasing its robustness towards modality absence and its anti-forgetting ability to maintain high incremental accuracy.