6 accepted papers
Existing state-of-the-art feature matchers capture long-range dependencies with Transformers but are hindered by high spatial complexity,leading to demanding training and high-latency inference.Striking a better balance between performance and efficiency remains a critical challenge in feature match…
In this paper, we study the intrinsic connections between informative representation among different views and sufficient information among reconstructed views in multi-view clustering. To this end, we propose a novel method called underlying-complementarity and surrounding-correspondence for multi-…
Object detection is a fundamental task in the field of computer vision, and one of its essential requirements is high-quality feature fusion. Previous works have made various efforts in this regard: CNN-based detectors use convolutional blocks to fuse local features and dense prior knowledge to pred…
Existing feature matching methods tend to extract feature descriptors by feeding down-sampled feature maps into a Transformer that is unable to extend feature scales, leading to false correspondences between small-size objects. This paper proposes MSFormer, which uses Transformers situated in differ…
Heavy computation is a bottleneck limiting deep-learning-based feature matching algorithms to be applied in many real-time applications. However, existing lightweight networks optimized for Euclidean data cannot address classical feature matching tasks, since sparse keypoint based descriptors are ex…
Current feature matching methods focus on point-level matching, pursuing better representation learning of individual features, but lacking further understanding of the scene. This results in significant performance degradation when handling challenging scenes such as scenes with large viewpoint and…