Results 271 to 280 of about 5,326,339 (296)

Fully Convolutional Recurrent Networks for Speech Enhancement

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
Convolutional recurrent neural networks (CRNs) using convolutional encoder-decoder (CED) structures have shown promising performance for single-channel speech enhancement. These CRNs handle temporal modeling through integrating long short-term memory (LSTM) layers in between convolutional encoder and decoder. However, in such a CRN, the organization of
Maximilian Strake, Bruno Defraene, Kristoff Fluyt, Wouter Tirry, Tim Fingscheidt   +4 more
openaire   +1 more source

Crowd Counting with Fully Convolutional Neural Network

2018 25th IEEE International Conference on Image Processing (ICIP), 2018
Crowd counting estimation is an extremely challenging task due to various crowded scenarios. In this paper, we present a deep learning framework for crowd counting from a single static image with different number of people and arbitrary perspective. In the design of convolutional neural network structure, we employ the VGG16 model but drop the fully ...
Ming Liu, Yang Liu, Jue Jiang, Zhenwei Guo, Zenan Wang   +4 more
openaire   +1 more source

Saliency Detection with Recurrent Fully Convolutional Networks

2016
Deep networks have been proved to encode high level semantic features and delivered superior performance in saliency detection. In this paper, we go one step further by developing a new saliency model using recurrent fully convolutional networks (RFCNs).
Linzhao Wang, Lijun Wang, Huchuan Lu, Pingping Zhang, Xiang Ruan   +4 more
openaire   +2 more sources

Object Proposal Generation With Fully Convolutional Networks

IEEE Transactions on Circuits and Systems for Video Technology, 2018
Object proposal generation, as a preprocessing technique, has been widely used in current object detection pipelines to guide the search of objects and avoid exhaustive sliding window search across images. Current object proposals are mostly based on low-level image cues, such as edges and saliency. However, objectness is possibly a high-level semantic
Zequn Jie, Wen Feng Lu, Siavash Sakhavi, Yunchao Wei, Francis Eng Hock Tay, Shuicheng Yan   +5 more
openaire   +1 more source

Joint segmentation and classification with fully convolutional networks

2016 3rd International Conference on Systems and Informatics (ICSAI), 2016
This paper describes a joint segmentation and classification approach that exploits global image features to validate the predictions from local appearance descriptors and to ensure their consistent labeling. The in-between interplay is encoded by a parameter-learning process of a unified deep learning model embedding a fully convolution network (FCN ...
Falong Shen, Rui Gan
openaire   +1 more source

Multiscale fully convolutional network for image saliency

Journal of Electronic Imaging, 2018
We focus on saliency estimation in digital images. We describe why it is important to adopt a data-driven model for such an illposed problem, allowing for a universal concept of “saliency” to naturally emerge from data that are typically annotated with drastically heterogeneous criteria.
Simone Bianco 0001, Marco Buzzelli, Raimondo Schettini   +2 more
openaire   +2 more sources

Automatic colorization using fully convolutional networks

Journal of Electronic Imaging, 2018
We propose an approach for automatically colorizing grayscale images using fully convolutional networks (FCNs). In contrast to traditional colorization methods, our approach operates only on grayscale images without any manual assistance. We first build an end-to-end deep learning network based on an FCN.
Jingjing Zhuge, Jiajun Lin, Wei An 0002
openaire   +1 more source

A Fully Convolutional Network for Salient Object Detection

2017
In this paper we address the task of salient object detection without requiring an explicit object class recognition. To this end, we propose a solution that exploits intermediate activations of a Fully Convolutional Neural Network previously trained for the recognition of 1,000 object classes, in order to gather generic object information at different
Bianco, S, Buzzelli, M, Schettini, R.
openaire   +1 more source

Robust Face Detector with Fully Convolutional Networks

2018
Many of the exist face detection algorithms are based on the generic object detection methods and have achieved desirable results. However, these methods still struggle in solving the problem of partial occluded face detection. In this paper, we introduce a simple and effective face detector which uses a fully convolutional networks (FCN) for face ...
Yingcheng Su, Xiaopei Wan, Zhenhua Guo 0001   +2 more
openaire   +2 more sources

Lithological Mapping Based on Fully Convolutional Network and Multi-Source Geological Data

Remote Sensing, 2021
Ziye Wang, Renguang Zuo, Wang Ziye
exaly