DCNN based Real-time Adaptive Ship License Plate Recognition (DRASLPR)
Ship license plate recognition is challenging due to the diversity of plate locations and text types. This paper proposes a DCNN-based (deep […]
Modeling IoT Equipment With Graph Neural Networks
Traditional neural networks usually concentrate on temporal data in system simulation, and lack of capabilities to reason inner logic relations between different […]
RPN-FCN based Rust detection on power equipment
This paper proposes a novel RPN-FCN based rust detection approach. The RPN-FCN generates region proposals with RPN and performs full convolution for […]
A Streaming Cloud Platform for Real-Time Video Processing on Embedded Devices
Real-time intelligent video processing on embedded devices with low power consumption can be useful for applications like drone surveillance, smart cars, and […]
From BoW to CNN
Texture is a fundamental characteristic of many types of images, and texture representation is one of the essential and challenging problems in […]
Micro-expression recognition with small sample size by transferring long-term convolutional neural network
Micro-expression is one of important clues for detecting lies. Its most outstanding characteristics include short duration and low intensity of movement. Therefore, […]
Deep Binary Representation of Facial Expressions
Automatic pain assessment is crucial in clinical diagnosis. Experiencing pain causes deformations in the facial structure resulting in different spontaneous facial expressions. […]
Summarization of User-Generated Sports Video by Using Deep Action Recognition Features
Automatically generating a summary of a sports video poses the challenge of detecting interesting moments, or highlights, of a game. Traditional sports […]
Learning visual and textual representations for multimodal matching and classification
Multimodal learning has been an important and challenging problem for decades, which aims to bridge the modality gap between heterogeneous representations, such […]
Deep & Deformable
Deep Convolutional Neural Networks (DCNNs) are currently the method of choice for tasks such that objects and parts detections. Before the advent […]