17 citations · 17 across the 10 of their papers we have counts for
7 papers · 1 filter
Scale-Aware Relay and Scale-Adaptive Loss for Tiny Object Detection in Aerial Images
Jinfu Li, Yuqi Huang, Hong Song +5
Recently, despite the remarkable advancements in object detection, modern detectors still struggle to detect tiny objects in aerial images. One key reason is that tiny objects carr…
Sculpting Margin Penalty: Intra-Task Adapter Merging and Classifier Calibration for Few-Shot Class-Incremental Learning
Liang Bai, Hong Song, Jinfu Li +6
Real-world applications often face data privacy constraints and high acquisition costs, making the assumption of sufficient training data in incremental tasks unrealistic and leadi…
GMOR: A Lightweight Robust Point Cloud Registration Framework via Geometric Maximum Overlapping
Zhao Zheng, Jingfan Fan, Long Shao +6
Point cloud registration based on correspondences computes the rigid transformation that maximizes the number of inliers constrained within the noise threshold. Current state-of-th…
MoCTEFuse: Illumination-Gated Mixture of Chiral Transformer Experts for Multi-Level Infrared and Visible Image Fusion
Li Jinfu, Song Hong, Xia Jianghan +5
While illumination changes inevitably affect the quality of infrared and visible image fusion, many outstanding methods still ignore this factor and directly merge the information…
Efficient Non-Exemplar Class-Incremental Learning with Retrospective Feature Synthesis
Liang Bai, Hong Song, Yucong Lin +5
Despite the outstanding performance in many individual tasks, deep neural networks suffer from catastrophic forgetting when learning from continuous data streams in real-world scen…
Double-Shot 3D Shape Measurement with a Dual-Branch Network for Structured Light Projection Profilometry
Mingyang Lei, Jingfan Fan, Long Shao +6
The structured light (SL)-based three-dimensional (3D) measurement techniques with deep learning have been widely studied to improve measurement efficiency, among which fringe proj…
BIRNet: Brain Image Registration Using Dual-Supervised Fully Convolutional Networks
Jingfan Fan, Xiaohuan Cao, Pew-Thian Yap +1
In this paper, we propose a deep learning approach for image registration by predicting deformation from image appearance. Since obtaining ground-truth deformation fields for train…