177 citations · 548 across the 11 of their papers we have counts for
12 papers
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…
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…
Infrared Small Target Detection in Satellite Videos: A New Dataset and A Novel Recurrent Feature Refinement Framework
Xinyi Ying, Li Liu, Zaipin Lin +7
Multi-frame infrared small target (MIRST) detection in satellite videos is a long-standing, fundamental yet challenging task for decades, and the challenges can be summarized as: F…
The First Competition on Resource-Limited Infrared Small Target Detection Challenge: Methods and Results
Boyang Li, Xinyi Ying, Ruojing Li +3
In this paper, we briefly summarize the first competition on resource-limited infrared small target detection (namely, LimitIRSTD). This competition has two tracks, including weakl…
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…
Multi-Scale Direction-Aware Network for Infrared Small Target Detection
Jinmiao Zhao, Zelin Shi, Chuang Yu +3
Infrared small target detection faces the problem that it is difficult to effectively separate the background and the target. Existing deep learning-based methods focus on edge and…
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…
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…
Symmetric Parallax Attention for Stereo Image Super-Resolution
Yingqian Wang, Xinyi Ying, Longguang Wang +3
Although recent years have witnessed the great advances in stereo image super-resolution (SR), the beneficial information provided by binocular systems has not been fully used. Sin…
Light Field Image Super-Resolution Using Deformable Convolution
Yingqian Wang, Jungang Yang, Longguang Wang +4
Light field (LF) cameras can record scenes from multiple perspectives, and thus introduce beneficial angular information for image super-resolution (SR). However, it is challenging…
Exploring Sparsity in Image Super-Resolution for Efficient Inference
Longguang Wang, Xiaoyu Dong, Yingqian Wang +4
Current CNN-based super-resolution (SR) methods process all locations equally with computational resources being uniformly assigned in space. However, since missing details in low-…
Deformable 3D Convolution for Video Super-Resolution
Xinyi Ying, Longguang Wang, Yingqian Wang +3
The spatio-temporal information among video sequences is significant for video super-resolution (SR). However, the spatio-temporal information cannot be fully used by existing vide…