318 citations · 2.1k across the 42 of their papers we have counts for
42 papers
Understand Before Detect: Vision--Language Learning for Omni-Domain Infrared Small Target Detection
Haoyang Yuan, Boyang Li, Yingqian Wang +7
Omni-domain infrared small target (IRST) detection is crucial for infrared surveillance, yet remains challenging due to heterogeneous imaging domains and inconsistent target charac…
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…
Heterogeneous Graph Transformer for Multiple Tiny Object Tracking in RGB-T Videos
Qingyu Xu, Longguang Wang, Weidong Sheng +4
Tracking multiple tiny objects is highly challenging due to their weak appearance and limited features. Existing multi-object tracking algorithms generally focus on single-modality…
NTIRE 2024 Challenge on Stereo Image Super-Resolution: Methods and Results
Longguang Wang, Yulan Guo, Juncheng Li +6
This paper summarizes the 3rd NTIRE challenge on stereo image super-resolution (SR) with a focus on new solutions and results. The task of this challenge is to super-resolve a low-…
Visible-Thermal Tiny Object Detection: A Benchmark Dataset and Baselines
Xinyi Ying, Chao Xiao, Ruojing Li +13
Small object detection (SOD) has been a longstanding yet challenging task for decades, with numerous datasets and algorithms being developed. However, they mainly focus on either v…
SpecDETR: A transformer-based hyperspectral point object detection network
Zhaoxu Li, Wei An, Gaowei Guo +3
Hyperspectral target detection (HTD) aims to identify specific materials based on spectral information in hyperspectral imagery and can detect extremely small-sized objects, some o…
OccCasNet: Occlusion-aware Cascade Cost Volume for Light Field Depth Estimation
Wentao Chao, Fuqing Duan, Xuechun Wang +2
Light field (LF) depth estimation is a crucial task with numerous practical applications. However, mainstream methods based on the multi-view stereo (MVS) are resource-intensive an…
Learning Remote Sensing Object Detection with Single Point Supervision
Shitian He, Huanxin Zou, Yingqian Wang +3
Pointly Supervised Object Detection (PSOD) has attracted considerable interests due to its lower labeling cost as compared to box-level supervised object detection. However, the co…
NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results
Yingqian Wang, Longguang Wang, Zhengyu Liang +3
In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradati…
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…
Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision
Xinyi Ying, Li Liu, Yingqian Wang +5
Training a convolutional neural network (CNN) to detect infrared small targets in a fully supervised manner has gained remarkable research interests in recent years, but is highly…
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…
Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution
Zhengyu Liang, Yingqian Wang, Longguang Wang +3
Exploiting spatial-angular correlation is crucial to light field (LF) image super-resolution (SR), but is highly challenging due to its non-local property caused by the disparities…
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.,…
Learning Sub-Pixel Disparity Distribution for Light Field Depth Estimation
Wentao Chao, Xuechun Wang, Yingqian Wang +2
Light field (LF) depth estimation plays a crucial role in many LF-based applications. Existing LF depth estimation methods consider depth estimation as a regression problem, where…
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…
NTIRE 2022 Challenge on Stereo Image Super-Resolution: Methods and Results
Longguang Wang, Yulan Guo, Yingqian Wang +3
In this paper, we summarize the 1st NTIRE challenge on stereo image super-resolution (restoration of rich details in a pair of low-resolution stereo images) with a focus on new sol…
Occlusion-Aware Cost Constructor for Light Field Depth Estimation
Yingqian Wang, Longguang Wang, Zhengyu Liang +3
Matching cost construction is a key step in light field (LF) depth estimation, but was rarely studied in the deep learning era. Recent deep learning-based LF depth estimation metho…
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…
Local Motion and Contrast Priors Driven Deep Network for Infrared Small Target Super-Resolution
Xinyi Ying, Yingqian Wang, Longguang Wang +4
Infrared small target super-resolution (SR) aims to recover reliable and detailed high-resolution image with high-contrast targets from its low-resolution counterparts. Since the i…
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…
Dense Dual-Attention Network for Light Field Image Super-Resolution
Yu Mo, Yingqian Wang, Chao Xiao +2
Light field (LF) images can be used to improve the performance of image super-resolution (SR) because both angular and spatial information is available. It is challenging to incorp…