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 detection via Cellular Automata
TL;DR: A novel propagation mechanism dependent on Cellular Automata is proposed to exploit the intrinsic relevance of similar regions through interactions with neighbors and an integration algorithm in the Bayesian framework to take advantage of multiple saliency maps is presented.
Proceedings ArticleDOI
Deep Saliency with Encoded Low Level Distance Map and High Level Features
TL;DR: It is demonstrated that hand-crafted features can provide complementary information to enhance performance of saliency detection that utilizes only high level features.
Posted Content
Visual Saliency Based on Multiscale Deep Features
Guanbin Li,Yizhou Yu +1 more
TL;DR: This paper discovers that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks.
Proceedings ArticleDOI
Shallow and Deep Convolutional Networks for Saliency Prediction
TL;DR: In this paper, the authors proposed a completely data-driven approach by training a convolutional neural network (convnet) for saliency prediction, where the learning process is formulated as a minimization of a loss function that measures the Euclidean distance of the predicted saliency map with the provided ground truth.
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Bayesian Saliency via Low and Mid Level Cues
TL;DR: This paper proposes a novel model for bottom-up saliency within the Bayesian framework by exploiting low and mid level cues and proposes an algorithm in which a coarse saliency region is first obtained via a convex hull of interest points.