Results 41 to 50 of about 52,284 (313)

DeepID-Net: Deformable deep convolutional neural networks for object detection [PDF]

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
In this paper, we propose deformable deep convolutional neural networks for generic object detection. This new deep learning object detection framework has innovations in multiple aspects. In the proposed new deep architecture, a new deformation constrained pooling (def-pooling) layer models the deformation of object parts with geometric constraint and
Ouyang, Wanli, Wang, Xiaogang, Zeng, Xingyu, Qiu, Shi, Luo, Ping, Tian, Yonglong, Li, Hongsheng, Yang, Shuo, Wang, Zhe, Loy, Chen-Change, Tang, Xiaoou   +10 more
openaire   +2 more sources

Monocular Object Instance Segmentation and Depth Ordering with CNNs

, 2015
In this paper we tackle the problem of instance-level segmentation and depth ordering from a single monocular image. Towards this goal, we take advantage of convolutional neural nets and train them to directly predict instance-level segmentations where ...
Fidler, Sanja, Schwing, Alexander G., Urtasun, Raquel, Zhang, Ziyu   +3 more
core   +1 more source

End‐to‐end global to local convolutional neural network learning for hand pose recovery in depth data

IET Computer Vision, 2022
Despite recent advances in 3‐D pose estimation of human hands, thanks to the advent of convolutional neural networks (CNNs) and depth cameras, this task is still far from being solved in uncontrolled setups.
Meysam Madadi, Sergio Escalera, Xavier Baró, Jordi Gonzàlez   +3 more
doaj   +1 more source

Augmenting Paraphrase Generation with Syntax Information Using Graph Convolutional Networks

Entropy, 2021
Paraphrase generation is an important yet challenging task in natural language processing. Neural network-based approaches have achieved remarkable success in sequence-to-sequence learning.
Xiaoqiang Chi, Yang Xiang
doaj   +1 more source

ISTA-Net: Interpretable Optimization-Inspired Deep Network for Image Compressive Sensing

, 2018
With the aim of developing a fast yet accurate algorithm for compressive sensing (CS) reconstruction of natural images, we combine in this paper the merits of two existing categories of CS methods: the structure insights of traditional optimization-based
Ghanem, Bernard, Zhang, Jian
core   +1 more source

Lesion Location and Functional Connections Reveal Cognitive Impairment Networks in Multiple Sclerosis

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective Cognitive impairment, fatigue, and depression are common in multiple sclerosis (MS), potentially due to disruption of regional functional connectivity caused by white matter (WM) lesions. We explored whether WM lesions functionally connected to specific brain regions contribute to these MS‐related manifestations.
Alessandro Franceschini, Paolo Preziosa, Paola Valsasina, Damiano Mistri, Monica Margoni, Federica Esposito, Massimo Filippi, Maria A. Rocca   +7 more
wiley   +1 more source

Generalizable and efficient cross‐domain person re‐identification model using deep metric learning

IET Computer Vision, 2023
Most of the successful person re‐ID models conduct supervised training and need a large number of training data. These models fail to generalise well on unseen unlabelled testing sets.
Saba Sadat Faghih Imani, Kazim Fouladi‐Ghaleh, Hossein Aghababa   +2 more
doaj   +1 more source

Diffusion Tractography Biomarker for Epilepsy Severity in Children With Drug‐Resistant Epilepsy

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective To develop a novel deep‐learning model of clinical DWI tractography that can accurately predict the general assessment of epilepsy severity (GASE) in pediatric drug‐resistant epilepsy (DRE) and test if it can screen diverse neurocognitive impairments identified through neuropsychological assessments.
Jeong‐Won Jeong, Min‐Hee Lee, Hiroshi Uda, Yoon Ho Hwang, Michael Behen, Aimee Luat, Csaba Juhász, Eishi Asano   +7 more
wiley   +1 more source

xUnit: Learning a Spatial Activation Function for Efficient Image Restoration

, 2018
In recent years, deep neural networks (DNNs) achieved unprecedented performance in many low-level vision tasks. However, state-of-the-art results are typically achieved by very deep networks, which can reach tens of layers with tens of millions of ...
Kligvasser, Idan, Michaeli, Tomer, Shaham, Tamar Rott   +2 more
core   +1 more source

Deep deformable registration: Enhancing accuracy by fully convolutional neural net [PDF]

Pattern Recognition Letters, 2017
Deformable registration is ubiquitous in medical image analysis. Many deformable registration methods minimize sum of squared difference (SSD) as the registration cost with respect to deformable model parameters. In this work, we construct a tight upper bound of the SSD registration cost by using a fully convolutional neural network (FCNN) in the ...
Ghosal, Sayan, Ray, Nilanjan
openaire   +2 more sources