5 citations · 16 across the 7 of their papers we have counts for
7 papers
Plug & Play Convolutional Regression Tracker for Video Object Detection
Ye Lyu, Michael Ying Yang, George Vosselman +1
Video object detection targets to simultaneously localize the bounding boxes of the objects and identify their classes in a given video. One challenge for video object detection is…
Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features
Du Chen, Zewei He, Yanpeng Cao +5
Recently, Convolutional Neural Networks (CNNs) have been successfully adopted to solve the ill-posed single image super-resolution (SISR) problem. A commonly used strategy to boost…
LIP: Learning Instance Propagation for Video Object Segmentation
Ye Lyu, George Vosselman, Gui-Song Xia +1
In recent years, the task of segmenting foreground objects from background in a video, i.e. video object segmentation (VOS), has received considerable attention. In this paper, we…
Unsupervised Domain Adaptation for Multispectral Pedestrian Detection
Dayan Guan, Xing Luo, Yanpeng Cao +4
Multimodal information (e.g., visible and thermal) can generate robust pedestrian detections to facilitate around-the-clock computer vision applications, such as autonomous driving…
Robust object extraction from remote sensing data
Sophie Crommelinck, Mila Koeva, Michael Ying Yang +1
The extraction of object outlines has been a research topic during the last decades. In spite of advances in photogrammetry, remote sensing and computer vision, this task remains c…
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection
Yanpeng Cao, Dayan Guan, Yulun Wu +3
Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situatio…