15 citations · 28 across the 11 of their papers we have counts for
10 papers · 1 filter
The First Challenge on Remote Sensing Infrared Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview
Kai Liu, Haoyang Yue, Zeli Lin +65
This paper presents the NTIRE 2026 Remote Sensing Infrared Image Super-Resolution (x4) Challenge, one of the associated challenges of NTIRE 2026. The challenge aims to recover high…
CuriosAI Submission to the EgoExo4D Proficiency Estimation Challenge 2025
Hayato Tanoue, Hiroki Nishihara, Yuma Suzuki +9
This report presents the CuriosAI team's submission to the EgoExo4D Proficiency Estimation Challenge at CVPR 2025. We propose two methods for multi-view skill assessment: (1) a mul…
HOpenCls: Training Hyperspectral Image Open-Set Classifiers in Their Living Environments
Hengwei Zhao, Xinyu Wang, Zhuo Zheng +2
Hyperspectral image (HSI) open-set classification is critical for HSI classification models deployed in real-world environments, where classifiers must simultaneously classify know…
AnomalyCD: A benchmark for Earth anomaly change detection with high-resolution and time-series observations
Jingtao Li, Qian Zhu, Xinyu Wang +2
Various Earth anomalies have destroyed the stable, balanced state, resulting in fatalities and serious destruction of property. With the advantages of large-scale and precise obser…
Learning a Cross-modality Anomaly Detector for Remote Sensing Imagery
Jingtao Li, Xinyu Wang, Hengwei Zhao +2
Remote sensing anomaly detector can find the objects deviating from the background as potential targets for Earth monitoring. Given the diversity in earth anomaly types, designing…
Class Prior-Free Positive-Unlabeled Learning with Taylor Variational Loss for Hyperspectral Remote Sensing Imagery
Hengwei Zhao, Xinyu Wang, Jingtao Li +1
Positive-unlabeled learning (PU learning) in hyperspectral remote sensing imagery (HSI) is aimed at learning a binary classifier from positive and unlabeled data, which has broad p…
One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship Learning
Jingtao Li, Xinyu Wang, Shaoyu Wang +3
Hyperspectral anomaly detection (HAD) involves identifying the targets that deviate spectrally from their surroundings, without prior knowledge. Recently, deep learning based metho…
Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors
Jingtao Li, Xinyu Wang, Hengwei Zhao +2
Anomaly segmentation in high spatial resolution (HSR) remote sensing imagery is aimed at segmenting anomaly patterns of the earth deviating from normal patterns, which plays an imp…
One-Class Risk Estimation for One-Class Hyperspectral Image Classification
Hengwei Zhao, Yanfei Zhong, Xinyu Wang +1
Hyperspectral imagery (HSI) one-class classification is aimed at identifying a single target class from the HSI by using only knowing positive data, which can significantly reduce…
The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery
Omid Ghorbanzadeh, Yonghao Xu, Hengwei Zhao +12
The scientific outcomes of the 2022 Landslide4Sense (L4S) competition organized by the Institute of Advanced Research in Artificial Intelligence (IARAI) are presented here. The obj…