6 accepted papers
Cross-view multi-object tracking (CVMOT) aims to simultaneously track a group of objects over time in each view and to associate the same objects across different views. This task is essential for swarm monitoring and has attracted considerable attention in the literature. However, most existing dat
Autonomous maneuver decision in air combat is a challenging task with high-dimensional state-action spaces and nonlinear dynamics. Existing approaches are usually based on online learning paradigms, which hinders their application to real-world scenarios where online interactions, <italic xmlns:mml=
Depth completion has been widely studied to predict a dense depth image from its sparse measurement and a single color image. However, most state-of-the-art methods rely on static convolutional neural networks (CNNs) which are not flexible enough for capturing the dynamic nature of input contexts. I…
We present a novel unsupervised framework for instance-level image-to-image translation. Although recent advances have been made by incorporating additional object annotations, existing methods often fail to handle images with multiple disparate objects. The main cause is that, during inference, the…
Depth completion is an important task in computer vision and robotics applications, which aims at predicting accurate dense depth from a single RGB-LiDAR image. Convolutional neural networks (CNNs) have been widely used for depth completion to learn a mapping function from sparse to dense depth. How…
Regularization-based image restoration has remained an active research topic in image processing and computer vision. It often leverages a guidance signal captured in different fields as an additional cue. In this work, we present a general framework for image restoration, called deeply aggregated a…