7 accepted papers
Multi-modal image matching is a fundamental task in multi-view and multi-modal image processing. Its key challenge lies in extracting features that remain consistent despite drastic appearance variations across modalities. However, the learning of the feature is hindered by the scarcity and the inac
Compared to images, videos better reflect real-world acquisition and possess valuable temporal cues. However, existing multi-sensor fusion research predominantly integrates complementary context from multiple images rather than videos due to the scarcity of large-scale multi-sensor video datasets, l
Current deep homography estimation methods are typically constrained to processing low-resolution image pairs due to network architecture and computational limitations. For high-resolution images, downsampling is often required, which can greatly degrade estimation accuracy. In contrast, image match…
Establishing dense correspondences is crucial yet computationally demanding in multi-view tasks. Although coarse-to-fine schemes mitigate computational costs, their efficiency remains limited by the substantial demands of heavy feature extractors and global matchers. In this paper, we propose Adapti…
Vehicle-to-infrastructure (V2I) cooperative perception systems can enhance the sensing abilities of autonomous vehicles. Existing V2I solutions often consider LiDARs devices instead of cameras, the most prevalent sensors with low cost and wide installation. In addition, a major challenge that has be…
Attention-based graph neural networks have made great progress in feature matching. However, the literature lacks a comprehensive understanding of how the attention mechanism operates for feature matching. In this paper, we rethink cross- and self-attention from the viewpoint of traditional feature…
Rescaling the backpropagated gradient of contrastive loss has made significant progress in descriptor learning. However, current gradient modulation strategies have no regard for the varying distribution of global gradients, so they would suffer from changes in training phases or datasets. In this p…