1 citations · 2 across the 5 of their papers we have counts for
6 papers
Fusion Meets Diverse Conditions: A High-diversity Benchmark and Baseline for UAV-based Multimodal Object Detection with Condition Cues
Chen Chen, Kangcheng Bin, Ting Hu +6
Unmanned aerial vehicles (UAV)-based object detection with visible (RGB) and infrared (IR) images facilitates robust around-the-clock detection, driven by advancements in deep lear…
ATR-UMMIM: A Benchmark Dataset for UAV-Based Multimodal Image Registration under Complex Imaging Conditions
Kangcheng Bin, Chen Chen, Ting Hu +2
Multimodal fusion has become a key enabler for UAV-based object detection, as each modality provides complementary cues for robust feature extraction. However, due to significant d…
Nearshore Underwater Target Detection Meets UAV-borne Hyperspectral Remote Sensing: A Novel Hybrid-level Contrastive Learning Framework and Benchmark Dataset
Jiahao Qi, Chuanhong Zhou, Xingyue Liu +4
UAV-borne hyperspectral remote sensing has emerged as a promising approach for underwater target detection (UTD). However, its effectiveness is hindered by spectral distortions in…
Beyond Sharing Weights in Decoupling Feature Learning Network for UAV RGB-Infrared Vehicle Re-Identification
Xingyue Liu, Jiahao Qi, Chen Chen +2
Owing to the capacity of performing full-time target search, cross-modality vehicle re-identification (Re-ID) based on unmanned aerial vehicle (UAV) is gaining more attention in bo…
Masked Spatial-Spectral Autoencoders Are Excellent Hyperspectral Defenders
Jiahao Qi, Zhiqiang Gong, Xingyue Liu +6
Deep learning methodology contributes a lot to the development of hyperspectral image (HSI) analysis community. However, it also makes HSI analysis systems vulnerable to adversaria…
Transferable Physical Attack against Object Detection with Separable Attention
Yu Zhang, Zhiqiang Gong, Yichuang Zhang +5
Transferable adversarial attack is always in the spotlight since deep learning models have been demonstrated to be vulnerable to adversarial samples. However, existing physical att…