53 citations · 95 across the 10 of their papers we have counts for
10 papers
Spectral point transformer for significant wave height estimation from sea clutter
Yi Zhou, Li Wang, Hang Su +1
This paper presents a method for estimating significant wave height (Hs) from sparse S_pectral P_oint using a T_ransformer-based approach (SPT). Based on empirical observations tha…
EasyRobust: A Comprehensive and Easy-to-use Toolkit for Robust and Generalized Vision
Xiaofeng Mao, Yuefeng Chen, Rong Zhang +3
Deep neural networks (DNNs) has shown great promise in computer vision tasks. However, machine vision achieved by DNNs cannot be as robust as human perception. Adversarial attacks…
A Unified Interactive Model Evaluation for Classification, Object Detection, and Instance Segmentation in Computer Vision
Changjian Chen, Yukai Guo, Fengyuan Tian +7
Existing model evaluation tools mainly focus on evaluating classification models, leaving a gap in evaluating more complex models, such as object detection. In this paper, we devel…
Robust Unsupervised Small Area Change Detection from SAR Imagery Using Deep Learning
Xinzheng Zhang, Hang Su, Ce Zhang +3
Small area change detection from synthetic aperture radar (SAR) is a highly challenging task. In this paper, a robust unsupervised approach is proposed for small area change detect…
A Robust Imbalanced SAR Image Change Detection Approach Based on Deep Difference Image and PCANet
Xinzheng Zhang, Hang Su, Ce Zhang +4
In this research, a novel robust change detection approach is presented for imbalanced multi-temporal synthetic aperture radar (SAR) image based on deep learning. Our main contribu…
Biometrics Recognition Using Deep Learning: A Survey
Shervin Minaee, Amirali Abdolrashidi, Hang Su +2
Deep learning-based models have been very successful in achieving state-of-the-art results in many of the computer vision, speech recognition, and natural language processing tasks…
Open Logo Detection Challenge
Hang Su, Xiatian Zhu, Shaogang Gong
Existing logo detection benchmarks consider artificial deployment scenarios by assuming that large training data with fine-grained bounding box annotations for each class are avail…
Scalable Deep Learning Logo Detection
Hang Su, Shaogang Gong, Xiatian Zhu
Existing logo detection methods usually consider a small number of logo classes and limited images per class with a strong assumption of requiring tedious object bounding box annot…
Deep Learning Logo Detection with Data Expansion by Synthesising Context
Hang Su, Xiatian Zhu, Shaogang Gong
Logo detection in unconstrained images is challenging, particularly when only very sparse labelled training images are accessible due to high labelling costs. In this work, we desc…
Experiments on Parallel Training of Deep Neural Network using Model Averaging
Hang Su, Haoyu Chen
In this work we apply model averaging to parallel training of deep neural network (DNN). Parallelization is done in a model averaging manner. Data is partitioned and distributed to…