103 citations · 160 across the 15 of their papers we have counts for
17 papers
Evaluating the Efficacy of Sentinel-2 versus Aerial Imagery in Serrated Tussock Classification
Rezwana Sultana, Manzur Murshed, Kathryn Sheffield +3
Invasive species pose major global threats to ecosystems and agriculture. Serrated tussock (\textit{Nassella trichotoma}) is a highly competitive invasive grass species that disrup…
ReflectGAN: Modeling Vegetation Effects for Soil Carbon Estimation from Satellite Imagery
Dristi Datta, Manoranjan Paul, Manzur Murshed +2
Soil organic carbon (SOC) is a critical indicator of soil health, but its accurate estimation from satellite imagery is hindered in vegetated regions due to spectral contamination…
Hyperspectral Imaging-Based Grain Quality Assessment With Limited Labelled Data
Priyabrata Karmakar, Manzur Murshed, Shyh Wei Teng
Recently hyperspectral imaging (HSI)-based grain quality assessment has gained research attention. However, unlike other imaging modalities, HSI data lacks sufficient labelled samp…
FSDR: A Novel Deep Learning-based Feature Selection Algorithm for Pseudo Time-Series Data using Discrete Relaxation
Mohammad Rahman, Manzur Murshed, Shyh Wei Teng +1
Conventional feature selection algorithms applied to Pseudo Time-Series (PTS) data, which consists of observations arranged in sequential order without adhering to a conventional t…
A Guide to Employ Hyperspectral Imaging for Assessing Wheat Quality at Different Stages of Supply Chain in Australia: A Review
Priyabrata Karmakar, Shyh Wei Teng. Manzur Murshed, Paul Pang +1
Wheat is one of the major staple crops across the globe. Therefore, it is mandatory to measure, maintain and improve the wheat quality for human consumption. Traditional wheat qual…
A new way of video compression via forward-referencing using deep learning
S. M. A. K. Rajin, M. Murshed, M. Paul +2
To exploit high temporal correlations in video frames of the same scene, the current frame is predicted from the already-encoded reference frames using block-based motion estimatio…
Anti-aliasing Deep Image Classifiers using Novel Depth Adaptive Blurring and Activation Function
Md Tahmid Hossain, Shyh Wei Teng, Ferdous Sohel +1
Deep convolutional networks are vulnerable to image translation or shift, partly due to common down-sampling layers, e.g., max-pooling and strided convolution. These operations vio…
A novel network training approach for open set image recognition
Md Tahmid Hossain, Shyh Wei Teng, Guojun Lu +1
Convolutional Neural Networks (CNNs) are commonly designed for closed set arrangements, where test instances only belong to some "Known Known" (KK) classes used in training. As suc…
Network Representation Learning: From Traditional Feature Learning to Deep Learning
Ke Sun, Lei Wang, Bo Xu +3
Network representation learning (NRL) is an effective graph analytics technique and promotes users to deeply understand the hidden characteristics of graph data. It has been succes…
Thank you for Attention: A survey on Attention-based Artificial Neural Networks for Automatic Speech Recognition
Priyabrata Karmakar, Shyh Wei Teng, Guojun Lu
Attention is a very popular and effective mechanism in artificial neural network-based sequence-to-sequence models. In this survey paper, a comprehensive review of the different at…
Integrated Generalized Zero-Shot Learning for Fine-Grained Classification
Tasfia Shermin, Shyh Wei Teng, Ferdous Sohel +2
Embedding learning (EL) and feature synthesizing (FS) are two of the popular categories of fine-grained GZSL methods. EL or FS using global features cannot discriminate fine detail…
Bidirectional Mapping Coupled GAN for Generalized Zero-Shot Learning
Tasfia Shermin, Shyh Wei Teng, Ferdous Sohel +2
Bidirectional mapping-based generalized zero-shot learning (GZSL) methods rely on the quality of synthesized features to recognize seen and unseen data. Therefore, learning a joint…
Adversarial Network with Multiple Classifiers for Open Set Domain Adaptation
Tasfia Shermin, Guojun Lu, Shyh Wei Teng +2
Domain adaptation aims to transfer knowledge from a domain with adequate labeled samples to a domain with scarce labeled samples. Prior research has introduced various open set dom…
Robust Image Classification Using A Low-Pass Activation Function and DCT Augmentation
Md Tahmid Hossain, Shyh Wei Teng, Ferdous Sohel +1
Convolutional Neural Network's (CNN's) performance disparity on clean and corrupted datasets has recently come under scrutiny. In this work, we analyse common corruptions in the fr…
Enhanced Transfer Learning with ImageNet Trained Classification Layer
Tasfia Shermin, Shyh Wei Teng, Manzur Murshed +3
Parameter fine tuning is a transfer learning approach whereby learned parameters from pre-trained source network are transferred to the target network followed by fine-tuning. Prio…
Transfer Learning Using Classification Layer Features of CNN
Tasfia Shermin, Manzur Murshed, Guojun Lu +1
Although CNNs have gained the ability to transfer learned knowledge from source task to target task by virtue of large annotated datasets but consume huge processing time to fine-t…
Distortion Robust Image Classification using Deep Convolutional Neural Network with Discrete Cosine Transform
Md Tahmid Hossain, Shyh Wei Teng, Dengsheng Zhang +2
Convolutional Neural Network is good at image classification. However, it is found to be vulnerable to image quality degradation. Even a small amount of distortion such as noise or…