This paper addresses cross-view geo-localization in real-world scenarios, where the field-of-view (FoV) is restricted and the orientation is unknown for ground-view images. This task is extremely challenging due to the huge domain gap. Existing methods typically treat tasks with different FoVs as independent tasks. These approaches not only require separate retraining for each FoV, but also neglect the strong correlations between different FoVs, leading to poor performance under extremely limited FoV. To overcome these limitations, we propose HCL-Geo, a framework follows human-like continual learning paradigm of "first learn, then review" for geo-localization: in the first "learn" stage, tasks are presented to the model in an easy-to-hard sequence to enable gradual learning and knowledge retention, so that their natural correlations could be exploited to facilitate knowledge transfer. In the second "review" stage, expert modules are incorporated to efficiently handle tasks with varying FoVs. This approach eliminates the need for retraining separate models and demonstrates state-of-the-art performance across different FoVs with strong generalization capabilities. Remarkably, the recall rate@top-1 improves from 49.1% to 68.3% and from 24.6% to 34.3% respectively on CVUSA and CVACT benchmarks with 70° FoV.

Recent learning-based methods for event-based optical flow estimation utilize cost volumes for pixel matching but suffer from redundant computations and limited scalability to higher resolutions for flow refinement. In this work, we take advantage of the complementarity between temporally dense feature differences of adjacent event frames and cost volume and present a lightweight event-based optical flow network (EDCFlow) to achieve high-quality flow estimation at a higher resolution. Specifically, an attention-based multi-scale temporal feature difference layer is developed to capture diverse motion patterns at high resolution in a computation-efficient manner. An adaptive fusion of high-resolution difference motion features and low-resolution correlation motion features is performed to enhance motion representation and model generalization. Notably, EDCFlow can serve as a plug-and-play refinement module for RAFT-like event-based methods to enhance flow details. Extensive experiments demonstrate that EDCFlow achieves better performance with lower complexity compared to existing methods, offering superior generalization. Codes and models will be available at here.

Event cameras are bio-inspired dynamic vision sensors that are superior to frame-based cameras in terms of low power consumption, high dynamic range, and high temporal resolution in computer vision tasks. Recent advances in voxel-based representation learning have successfully exploited the sparsity of events with low computational complexity, but face challenges in extracting spatio-temporal features within voxels and representative global dependencies between voxels, thus limiting their representation power. In this work, towards a better trade-off between accuracy and computation overhead, we propose a novel voxel-based multi-scale transformer network (VMST-Net) to process event streams. Specifically, VMST-Net projects events within voxels into multi-channel frames along the time axis, such that 2D convolutions could be leveraged to encode spatio-temporal features in voxels. Then, VMST-Net utilizes a novel multi-scale multi-head self-attention (MSMHSA) mechanism with a multi-scale fusion (MSF) module that allows different heads within each layer to attend different scale 3D neighborhoods to adaptively aggregate the coarse-to-fine voxel features with little computational costs and parameters. Moreover, to model effective global features while saving computations, we aggregate features in a local-to-global manner by enlarging the coverage of 3D neighborhoods as the network gets deeper. Extensive experimental results on benchmark datasets demonstrate that our model advances state-of-the-art accuracy with low model complexity and computational complexity in all three visual tasks, including object classification, action recognition, and human pose estimation.