In the realm of computer vision, Deep Convolutional Neural Networks (DCNNs) have
demonstrated excellent performance. Video Processing, Object Detection, Image …

Semantic segmentation has a broad range of applications in a variety of domains including
land coverage analysis, autonomous driving, and medical image analysis. Convolutional …

Multi-modality fusion strategy is currently the de-facto most competitive solution for 3D
perception tasks. In this work, we present a new framework termed BEVFormer, which learns …

Visual segmentation seeks to partition images, video frames, or point clouds into multiple
segments or groups. This technique has numerous real-world applications, such as …

For the last few decades, several major subfields of artificial intelligence including computer
vision, graphics, and robotics have progressed largely independently from each other …

Recent outstanding results of supervised object detection in competitions and challenges
are often associated with specific metrics and datasets. The evaluation of such methods …

Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have
recently become a hotspot across the fields of computer vision (CV) and remote sensing …

Video super-resolution (VSR) approaches tend to have more components than the image
counterparts as they need to exploit the additional temporal dimension. Complex designs …

Multi-agent collaborative perception has recently received widespread attention as an
emerging application in driving scenarios. Despite the advancements in previous efforts …

With the rapid development of deep learning techniques, deep convolutional neural
networks (DCNNs) have become more important for object detection. Compared with …