Learn More
Face alignment, which fits a face model to an image and extracts the semantic meanings of facial pixels, has been an important topic in CV community. However, most algorithms are designed for faces in small to medium poses (below 45), lacking the ability to align faces in large poses up to 90. The challenges are three-fold: Firstly, the commonly used(More)
Person re-identification is challenging due to the large variations of pose, illumination, occlusion and camera view. Owing to these variations, the pedestrian data is distributed as highly-curved manifolds in the feature space, despite the current convolutional neural networks (CNN)’s capability of feature extraction. However, the distribution is unknown,(More)
Person re-identification aims to re-identify the probe image from a given set of images under different camera views. It is challenging due to large variations of pose, illumination, occlusion and camera view. Since the convolutional neural networks (CNN) have excellent capability of feature extraction, certain deep learning methods have been recently(More)
This paper presents a real-time face detector, named Single Shot Scale-invariant Face Detector (SFD), which performs superiorly on various scales of faces with a single deep neural network, especially for small faces. Specifically, we try to solve the common problem that anchorbased detectors deteriorate dramatically as the objects become smaller. We make(More)
BACKGROUND LncRNA ROR, a tumor oncogene associated with various human cancers, has been reported to be involved in regulating various cellular processes, such as proliferation, apoptosis and invasion through targeting multiple genes. However, the molecular biological function in bladder cancer has not been clearly elucidated. The aim of this study is to(More)
In many face recognition applications, the modalities of face images between the gallery and probe sets are different, which is known as heterogeneous face recognition. How to reduce the feature gap between images from different modalities is a critical issue to develop a highly accurate face recognition algorithm. Recently, joint Bayesian (JB) has(More)
Simulating inertial system was applied to the test of the starting and braking performance of rotary mechanism. The accuracy of inertia has an important impact on test precision of simulating inertial system. So the distribution of the inertia error caused by the mass error and the centroid error is analyzed and the models of the simulating inertial system(More)
Deep neural networks usually benefit from unsupervised pre-training, e.g. auto-encoders. However, the classifier further needs supervised fine-tuning methods for good discrimination. Besides, due to the limits of full-connection, the application of auto-encoders is usually limited to small, well aligned images. In this paper, we incorporate the supervised(More)
Although tremendous strides have been made in face detection, one of the remaining open challenges is to achieve real-time speed on the CPU as well as maintain high performance, since effective models for face detection tend to be computationally prohibitive. To address this challenge, we propose a novel face detector, named FaceBoxes, with superior(More)