328 citations · 782 across the 45 of their papers we have counts for
11 papers · 1 filter
Adaptive Quantization for Deep Neural Network
Yiren Zhou, Seyed-Mohsen Moosavi-Dezfooli, Ngai-Man Cheung +1
In recent years Deep Neural Networks (DNNs) have been rapidly developed in various applications, together with increasingly complex architectures. The performance gain of these DNN…
Aircraft Fuselage Defect Detection using Deep Neural Networks
Touba Malekzadeh, Milad Abdollahzadeh, Hossein Nejati +1
To ensure flight safety of aircraft structures, it is necessary to have regular maintenance using visual and nondestructive inspection (NDI) methods. In this paper, we propose an a…
Compact Hash Code Learning with Binary Deep Neural Network
Thanh-Toan Do, Tuan Hoang, Dang-Khoa Le Tan +2
Learning compact binary codes for image retrieval problem using deep neural networks has recently attracted increasing attention. However, training deep hashing networks is challen…
Accessible Melanoma Detection using Smartphones and Mobile Image Analysis
T. -T. Do, T. Hoang, V. Pomponiu +6
We investigate the design of an entire mobile imaging system for early detection of melanoma. Different from previous work, we focus on smartphone-captured visible light images. Ou…
Supervised Hashing with End-to-End Binary Deep Neural Network
Dang-Khoa Le Tan, Thanh-Toan Do, Ngai-Man Cheung
Image hashing is a popular technique applied to large scale content-based visual retrieval due to its compact and efficient binary codes. Our work proposes a new end-to-end deep ne…
Truly Multi-modal YouTube-8M Video Classification with Video, Audio, and Text
Zhe Wang, Kingsley Kuan, Mathieu Ravaut +13
The YouTube-8M video classification challenge requires teams to classify 0.7 million videos into one or more of 4,716 classes. In this Kaggle competition, we placed in the top 3% o…
Selective Deep Convolutional Features for Image Retrieval
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan +1
Convolutional Neural Network (CNN) is a very powerful approach to extract discriminative local descriptors for effective image search. Recent work adopts fine-tuned strategies to f…
Deep neural networks on graph signals for brain imaging analysis
Yiluan Guo, Hossein Nejati, Ngai-Man Cheung
Brain imaging data such as EEG or MEG are high-dimensional spatiotemporal data often degraded by complex, non-Gaussian noise. For reliable analysis of brain imaging data, it is imp…
Simultaneous Feature Aggregating and Hashing for Large-scale Image Search
Thanh-Toan Do, Dang-Khoa Le Tan, Trung T. Pham +1
In most state-of-the-art hashing-based visual search systems, local image descriptors of an image are first aggregated as a single feature vector. This feature vector is then subje…
Enhance Feature Discrimination for Unsupervised Hashing
Tuan Hoang, Thanh-Toan Do, Dang-Khoa Le Tan +1
We introduce a novel approach to improve unsupervised hashing. Specifically, we propose a very efficient embedding method: Gaussian Mixture Model embedding (Gemb). The proposed met…
On Classification of Distorted Images with Deep Convolutional Neural Networks
Yiren Zhou, Sibo Song, Ngai-Man Cheung
Image blur and image noise are common distortions during image acquisition. In this paper, we systematically study the effect of image distortions on the deep neural network (DNN)…