Supervised adversarial networks for image saliency detection

  title={Supervised adversarial networks for image saliency detection},
  author={Hengyue Pan and Hui Jiang},
  booktitle={International Conference on Graphic and Image Processing},
  • H. Pan, Hui Jiang
  • Published in
    International Conference on…
    24 April 2017
  • Computer Science
In the past few years, Generative Adversarial Network (GAN) became a prevalent research topic. [] Key Method However, different from GAN, the proposed method uses fully supervised learning to learn both G-Network and D-Network by applying class labels of the training set. Moreover, a novel kind of layer call conv-comparison layer is introduced into the D-Network to further improve the saliency performance. Experimental results on Pascal VOC 2012 database show that the SAN model can generate high quality…

Multi-Scale Adversarial Feature Learning for Saliency Detection

A new multi-scale adversarial feature learning (MAFL) model for image saliency detection, which is composed of two convolutional neural network modules: the multi- scale G-network takes natural images as inputs and generates the corresponding synthetic saliency map, and a novel layer in the D-network, namely a correlation layer, is designed.

Salient Object Detection With Capsule-Based Conditional Generative Adversarial Network

The experimental result showed that the proposed novel capsule-based salient object detection framework by integrating the novel capsule blocks into both the generator and discriminator of GAN architecture is able to generate accurate saliency maps.

SDP-GAN: Saliency Detail Preservation Generative Adversarial Networks for High Perceptual Quality Style Transfer

The paper introduces a saliency network, which is trained with the generator simultaneously, providing constraints for content loss to increase punishment for salient regions, and supplying saliency features to generator to produce coherent results.

ConnNet: A Long-Range Relation-Aware Pixel-Connectivity Network for Salient Segmentation

This work argues that semantic salient segmentation can be effectively resolved by reformulating it as a simple yet intuitive pixel-pair-based connectivity prediction task, and proposes a pure Connectivity Net (ConnNet), which predicts the connectivity probabilities of each pixel with its neighboring pixels.

Port ship Detection Based on Visual Saliency model and Center Dark Channel Prior

The watershed segmentation algorithm based on spectral intensity and image texture information achieves land and sea segmentation and is robust, has a good detection effect, and its accuracy and recall rate are greatly improved.



Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

This work introduces a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrates that they are a strong candidate for unsupervised learning.

Semantic Segmentation using Adversarial Networks

An adversarial training approach to train semantic segmentation models that can detect and correct higher-order inconsistencies between ground truth segmentation maps and the ones produced by the segmentation net.

Conditional Image Synthesis with Auxiliary Classifier GANs

A variant of GANs employing label conditioning that results in 128 x 128 resolution image samples exhibiting global coherence is constructed and it is demonstrated that high resolution samples provide class information not present in low resolution samples.

Saliency detection by multi-context deep learning

This paper proposes a multi-context deep learning framework for salient object detection that employs deep Convolutional Neural Networks to model saliency of objects in images and investigates different pre-training strategies to provide a better initialization for training the deep neural networks.

A fast method for saliency detection by back-propagating a convolutional neural network and clamping its partial outputs

  • H. PanHui Jiang
  • Computer Science
    2017 International Joint Conference on Neural Networks (IJCNN)
  • 2017
In this paper, we propose a fast deep learning method for object saliency detection using convolutional neural networks. In our approach, we use a gradient descent method to iteratively modify the

Deep Learning for Object Saliency Detection and Image Segmentation

This work uses a gradient descent method to iteratively modify an input image based on the pixel-wise gradients to reduce a cost function measuring the class-specific objectness of the image, and uses the computed saliency maps for image segmentation.

Improved Techniques for Training GANs

This work focuses on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic, and presents ImageNet samples with unprecedented resolution and shows that the methods enable the model to learn recognizable features of ImageNet classes.

A Deep Learning Based Fast Image Saliency Detection Algorithm

In this paper, we propose a fast deep learning method for object saliency detection using convolutional neural networks. In our approach, we use a gradient descent method to iteratively modify the

Image-to-Image Translation with Conditional Adversarial Networks

Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems and it is demonstrated that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks.

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

This paper addresses the visualisation of image classification models, learnt using deep Convolutional Networks (ConvNets), and establishes the connection between the gradient-based ConvNet visualisation methods and deconvolutional networks.