146 citations · 490 across the 20 of their papers we have counts for
20 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…
Focus on What Really Matters in Low-Altitude Governance: A Management-Centric Multi-Modal Benchmark with Implicitly Coordinated Vision-Language Reasoning Framework
Hao Chang, Zhihui Wang, Lingxiang Wu +5
Low-altitude vision systems are becoming a critical infrastructure for smart city governance. However, existing object-centric perception paradigms and loosely coupled vision-langu…
RRCANet: Recurrent Reusable-Convolution Attention Network for Infrared Small Target Detection
Yongxian Liu, Boyang Li, Ting Liu +2
Infrared small target detection is a challenging task due to its unique characteristics (e.g., small, dim, shapeless and changeable). Recently published CNN-based methods have achi…
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…
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…
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…
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 Nested Attention Network for Infrared Small Target Detection
Boyang Li, Chao Xiao, Longguang Wang +5
Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds. With the advances of deep learning, CNN-based methods have yielded p…
Parallax Attention for Unsupervised Stereo Correspondence Learning
Longguang Wang, Yulan Guo, Yingqian Wang +4
Stereo image pairs encode 3D scene cues into stereo correspondences between the left and right images. To exploit 3D cues within stereo images, recent CNN based methods commonly us…
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-…
Learning A Single Network for Scale-Arbitrary Super-Resolution
Longguang Wang, Yingqian Wang, Zaiping Lin +3
Recently, the performance of single image super-resolution (SR) has been significantly improved with powerful networks. However, these networks are developed for image SR with a si…
Deep Video Super-Resolution using HR Optical Flow Estimation
Longguang Wang, Yulan Guo, Li Liu +3
Video super-resolution (SR) aims at generating a sequence of high-resolution (HR) frames with plausible and temporally consistent details from their low-resolution (LR) counterpart…
Learning Parallax Attention for Stereo Image Super-Resolution
Longguang Wang, Yingqian Wang, Zhengfa Liang +4
Stereo image pairs can be used to improve the performance of super-resolution (SR) since additional information is provided from a second viewpoint. However, it is challenging to i…
Learning for Video Super-Resolution through HR Optical Flow Estimation
Longguang Wang, Yulan Guo, Zaiping Lin +2
Video super-resolution (SR) aims to generate a sequence of high-resolution (HR) frames with plausible and temporally consistent details from their low-resolution (LR) counterparts.…
Fast single image super-resolution based on sigmoid transformation
Longguang Wang, Zaiping Lin, Jinyan Gao +2
Single image super-resolution aims to generate a high-resolution image from a single low-resolution image, which is of great significance in extensive applications. As an ill-posed…
Multi-frame image super-resolution with fast upscaling technique
Longguang Wang, Zaiping Lin, Xinpu Deng +1
Multi-frame image super-resolution (MISR) aims to fuse information in low-resolution (LR) image sequence to compose a high-resolution (HR) one, which is applied extensively in many…