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Recent advances in convolutional neural networks
In the last few years, deep learning has led to very good performance on a variety of problems, such as visual recognition, speech recognition and natural language processing. Among different typesExpand
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A Siamese Long Short-Term Memory Architecture for Human Re-identification
Matching pedestrians across multiple camera views known as human re-identification (re-identification) is a challenging problem in visual surveillance. In the existing works concentrating on featureExpand
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DAG-Recurrent Neural Networks for Scene Labeling
In image labeling, local representations for image units are usually generated from their surrounding image patches, thus long-range contextual information is not effectively encoded. In this paper,Expand
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Deep Level Sets for Salient Object Detection
Deep learning has been applied to saliency detection in recent years. The superior performance has proved that deep networks can model the semantic properties of salient objects. Yet it is difficultExpand
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Convolutional recurrent neural networks: Learning spatial dependencies for image representation
In existing convolutional neural networks (CNNs), both convolution and pooling are locally performed for image regions separately, no contextual dependencies between different image regions have beenExpand
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Scene Segmentation with DAG-Recurrent Neural Networks
In this paper, we address the challenging task of scene segmentation. In order to capture the rich contextual dependencies over image regions, we propose Directed Acyclic Graph-Recurrent NeuralExpand
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Integrating parametric and non-parametric models for scene labeling
We adopt Convolutional Neural Networks (CNN) as our parametric model to learn discriminative features and classifiers for local patch classification. As visually similar pixels are indistinguishableExpand
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Context Contrasted Feature and Gated Multi-scale Aggregation for Scene Segmentation
Scene segmentation is a challenging task as it need label every pixel in the image. It is crucial to exploit discriminative context and aggregate multi-scale features to achieve better segmentation.Expand
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Joint Learning of Convolutional Neural Networks and Temporally Constrained Metrics for Tracklet Association
  • B. Wang, L. Wang, +4 authors G. Wang
  • Computer Science
  • IEEE Conference on Computer Vision and Pattern…
  • 15 May 2016
In this paper, we study the challenging problem of multiobject tracking in a complex scene captured by a single camera. Different from the existing tracklet associationbased tracking methods, weExpand
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Learning Contextual Dependence With Convolutional Hierarchical Recurrent Neural Networks
Deep convolutional neural networks (CNNs) have shown their great success on image classification. CNNs mainly consist of convolutional and pooling layers, both of which are performed on local imageExpand
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