4 accepted papers
Instance-level change detection in 3D scenes presents significant challenges, particularly under uncontrolled conditions without labeled image pairs, varying camera poses, or restricted lighting. This paper addresses this challenge by developing a novel approach to detect changes in real-world scena…
Most existing change detection (CD) methods focus on spatial domain modeling, while ignore the rich information of frequency domain. In this paper, we enhance the feature representative ability with spatial and frequency feature cross enhancement. Specifically, we propose a Change Feature Extract Mo…
Multiscale convolutional neural network (CNN) has demonstrated remarkable capabilities in solving various vision problems. However, fusing features of different scales always results in large model sizes, impeding the application of multiscale CNNs in RGB-D saliency detection. In this paper, we prop…
Change detection (CD) aims at finding change objects from bi-temporal images, which has wide applications in different vision tasks. Previous CD methods focus more on fusing inter-layer multiscale features while ignoring the intra-layer multiscale characteristics, which hurts the integrity of change…