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.
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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.
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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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Proceedings ArticleDOI
Saliency Optimization from Robust Background Detection
TL;DR: This work proposes a robust background measure, called boundary connectivity, which characterizes the spatial layout of image regions with respect to image boundaries and is much more robust and presents unique benefits that are absent in previous saliency measures.
Proceedings ArticleDOI
The Secrets of Salient Object Segmentation
TL;DR: An extensive evaluation of fixation prediction and salient object segmentation algorithms as well as statistics of major datasets identifies serious design flaws of existing salient object benchmarks and proposes a new high quality dataset that offers both fixation and salient objects segmentation ground-truth.
Proceedings ArticleDOI
Salient Object Detection: A Discriminative Regional Feature Integration Approach
TL;DR: This paper regards saliency map computation as a regression problem, which is based on multi-level image segmentation, and uses the supervised learning approach to map the regional feature vector to a saliency score, and finally fuses the saliency scores across multiple levels, yielding the salency map.
Journal ArticleDOI
Deeply Supervised Salient Object Detection with Short Connections
TL;DR: A new saliency method is proposed by introducing short connections to the skip-layer structures within the HED architecture, which produces state-of-the-art results on 5 widely tested salient object detection benchmarks, with advantages in terms of efficiency, effectiveness, and simplicity over the existing algorithms.
Proceedings ArticleDOI
Learning to Detect A Salient Object
TL;DR: A set of novel features including multi-scale contrast, center-surround histogram, and color spatial distribution are proposed to describe a salient object locally, regionally, and globally for salient object detection.