318 citations · 777 across the 9 of their papers we have counts for
10 papers
Towards UAV Detection in the Real World: A New Multispectral Dataset UAVNet-MS and a New Method
Yihang Luo, Jun Chen, Chao Xiao +13
The proliferation of unmanned aerial vehicles (UAVs) has created urgent demand for precise UAV monitoring. Existing RGB-based systems rely on spatial cues that degrade at small sca…
Dynamic High-frequency Convolution for Infrared Small Target Detection
Ruojing Li, Chao Xiao, Qian Yin +5
Infrared small targets are typically tiny and locally salient, which belong to high-frequency components (HFCs) in images. Single-frame infrared small target (SIRST) detection is c…
Deep Lookup Network
Yulan Guo, Longguang Wang, Wendong Mao +4
Convolutional neural networks are constructed with massive operations with different types and are highly computationally intensive. Among these operations, multiplication operatio…
Probing Deep into Temporal Profile Makes the Infrared Small Target Detector Much Better
Ruojing Li, Wei An, Yingqian Wang +6
Infrared small target (IRST) detection is challenging in simultaneously achieving precise, robust, and efficient performance due to extremely dim targets and strong interference. C…
Monte Carlo Linear Clustering with Single-Point Supervision is Enough for Infrared Small Target Detection
Boyang Li, Yingqian Wang, Longguang Wang +5
Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds on infrared images. Recently, deep learning based methods have achiev…
You Only Train Once: Learning a General Anomaly Enhancement Network with Random Masks for Hyperspectral Anomaly Detection
Zhaoxu Li, Yingqian Wang, Chao Xiao +3
In this paper, we introduce a new approach to address the challenge of generalization in hyperspectral anomaly detection (AD). Our method eliminates the need for adjusting paramete…
MTU-Net: Multi-level TransUNet for Space-based Infrared Tiny Ship Detection
Tianhao Wu, Boyang Li, Yihang Luo +6
Space-based infrared tiny ship detection aims at separating tiny ships from the images captured by earth orbiting satellites. Due to the extremely large image coverage area (e.g.,…
Real-World Light Field Image Super-Resolution via Degradation Modulation
Yingqian Wang, Zhengyu Liang, Longguang Wang +3
Recent years have witnessed the great advances of deep neural networks (DNNs) in light field (LF) image super-resolution (SR). However, existing DNN-based LF image SR methods are d…
Disentangling Light Fields for Super-Resolution and Disparity Estimation
Yingqian Wang, Longguang Wang, Gaochang Wu +4
Light field (LF) cameras record both intensity and directions of light rays, and encode 3D scenes into 4D LF images. Recently, many convolutional neural networks (CNNs) have been p…
Detecting and Tracking Small and Dense Moving Objects in Satellite Videos: A Benchmark
Qian Yin, Qingyong Hu, Hao Liu +5
Satellite video cameras can provide continuous observation for a large-scale area, which is important for many remote sensing applications. However, achieving moving object detecti…