Journal ArticleDOI
Saliency detection via conditional adversarial image-to-image network
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TLDR
This work proposes to conduct saliency detection by exploiting conditional adversarial network under the cGAN framework, in which saliency map prediction is transformed as a saliency segmentation task by using pair-wised image-to-ground-truth saliency.About:
This article is published in Neurocomputing.The article was published on 2018-11-17. It has received 34 citations till now. The article focuses on the topics: Salience (neuroscience).read more
Citations
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“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告
Journal ArticleDOI
Ternary Adversarial Networks With Self-Supervision for Zero-Shot Cross-Modal Retrieval
TL;DR: A novel model called ternary adversarial networks with self-supervision (TANSS) is proposed, inspired by zero-shot learning, to overcome the limitation of the existing methods on this challenging task of cross-modal retrieval.
Journal ArticleDOI
Deep Learning in the Biomedical Applications: Recent and Future Status
TL;DR: This paper reviews the major deep learning concepts pertinent to biomedical applications and concludes with a critical discussion, interpretation and relevant open challenges of the Omics and the BBMI.
Journal ArticleDOI
CSGAN: Cyclic-Synthesized Generative Adversarial Networks for image-to-image transformation
TL;DR: The proposed CSGAN uses a new objective function (loss) called Cyclic-Synthesized Loss (CS) between the synthesized image of one domain and cycled image of another domain and exhibits the promising and comparable performance over Facades dataset in terms of both qualitative and quantitative measures.
Journal ArticleDOI
Multi-scale semantic image inpainting with residual learning and GAN
TL;DR: A combination of an encoder–decoder generator for image semantic inpainting and a multi-layer convolutional net for image seamless fusion, which is capable of restoring image effectively and seamlessly.
References
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Journal ArticleDOI
Salient Object Detection: A Survey
TL;DR: A comprehensive review of recent progress in salient object detection is provided and this field is situate among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction.
Posted Content
Deep Contrast Learning for Salient Object Detection
Guanbin Li,Yizhou Yu +1 more
TL;DR: This paper proposes an end-to-end deep contrast network that significantly improves the state of the art in salient object detection and extracts segment-wise features very efficiently, and better models saliency discontinuities along object boundaries.
Book ChapterDOI
Saliency Detection with Recurrent Fully Convolutional Networks
TL;DR: This paper develops a new saliency model using recurrent fully convolutional networks (RFCNs) that is able to incorporate saliency prior knowledge for more accurate inference and enables the network to capture generic representations of objects for saliency detection.
Proceedings ArticleDOI
What Makes a Patch Distinct
TL;DR: A novel and fast approach to compute pattern distinctness that relies on the inner statistics of the patches in the image for identifying unique patterns and outperforms all state-of-the-art methods on the five most commonly-used datasets.
Journal ArticleDOI
DeepSaliency: Multi-Task Deep Neural Network Model for Salient Object Detection
TL;DR: This paper proposes a multi-task deep saliency model based on a fully convolutional neural network with global input (whole raw images) and global output (Whole saliency maps) and presents a graph Laplacian regularized nonlinear regression model for saliency refinement.