A Novel Attention-Based Network for Fast Salient Object Detection

In the current salient object detection network, the most popular method is using U-shape structure. However, the massive number of parameters leads to more consumption of computing and storage resources which are not feasible to deploy on the limited memory device. Some others shallow layer network will not maintain the same accuracy compared with U-shape structure and the deep network structure with more parameters will not converge to a global minimum loss with great speed. To overcome all of these disadvantages, we propose a new deep convolution network architecture with three contributions: (1) using smaller convolution neural networks (CNNs) to compress the model in our improved salient object features compression and reinforcement extraction module (ISFCREM) to reduce parameters of the model. (2) introducing channel attention mechanism to weigh different channels for improving the ability of feature representation. (3) applying a new optimizer to accumulate the long-term gradient information during training to adaptively tune the learning rate. The results demonstrate that the proposed method can compress the model to 1/3 of the original size nearly without losing the accuracy and converging faster and more smoothly on six widely used datasets of salient object detection compared with the others models. Our code is published in https://gitee.com/binzhangbinzhangbin/code-a-novel-attention-based-network-for-fast-salientobject-detection.git

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Semantic-Guided Attention Refinement Network for Salient Object Detection in Optical Remote Sensing Images

Remote Sensing ◽

10.3390/rs13112163 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2163

Author(s):

Zhou Huang ◽

Huaixin Chen ◽

Biyuan Liu ◽

Zhixi Wang

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Network Architecture ◽

Salient Object Detection ◽

Optical Remote Sensing ◽

Salient Object ◽

Remote Sensing Images ◽

Multi Level ◽

Object Area ◽

High Level

Although remarkable progress has been made in salient object detection (SOD) in natural scene images (NSI), the SOD of optical remote sensing images (RSI) still faces significant challenges due to various spatial resolutions, cluttered backgrounds, and complex imaging conditions, mainly for two reasons: (1) accurate location of salient objects; and (2) subtle boundaries of salient objects. This paper explores the inherent properties of multi-level features to develop a novel semantic-guided attention refinement network (SARNet) for SOD of NSI. Specifically, the proposed semantic guided decoder (SGD) roughly but accurately locates the multi-scale object by aggregating multiple high-level features, and then this global semantic information guides the integration of subsequent features in a step-by-step feedback manner to make full use of deep multi-level features. Simultaneously, the proposed parallel attention fusion (PAF) module combines cross-level features and semantic-guided information to refine the object’s boundary and highlight the entire object area gradually. Finally, the proposed network architecture is trained through an end-to-end fully supervised model. Quantitative and qualitative evaluations on two public RSI datasets and additional NSI datasets across five metrics show that our SARNet is superior to 14 state-of-the-art (SOTA) methods without any post-processing.

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Rethinking the U-shape Structure for Salient Object Detection

IEEE Transactions on Image Processing ◽

10.1109/tip.2021.3122093 ◽

2021 ◽

pp. 1-1

Author(s):

Jiang-Jiang Liu ◽

Zhi-Ang Liu ◽

Pai Peng ◽

Ming-Ming Cheng

Keyword(s):

Object Detection ◽

Salient Object Detection ◽

Salient Object ◽

Shape Structure

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Multiscale Deep Network with Centerness-Aware Loss for Salient Object Detection

Advances in Multimedia ◽

10.1155/2022/2243927 ◽

2022 ◽

Vol 2022 ◽

pp. 1-14

Author(s):

Liangliang Duan

Keyword(s):

Object Detection ◽

Network Architecture ◽

State Of The Art ◽

Saliency Detection ◽

Salient Object Detection ◽

Cross Entropy ◽

Salient Object ◽

Prediction Module ◽

Saliency Prediction ◽

Salient Regions

Deep encoder-decoder networks have been adopted for saliency detection and achieved state-of-the-art performance. However, most existing saliency models usually fail to detect very small salient objects. In this paper, we propose a multitask architecture, M2Net, and a novel centerness-aware loss for salient object detection. The proposed M2Net aims to solve saliency prediction and centerness prediction simultaneously. Specifically, the network architecture is composed of a bottom-up encoder module, top-down decoder module, and centerness prediction module. In addition, different from binary cross entropy, the proposed centerness-aware loss can guide the proposed M2Net to uniformly highlight the entire salient regions with well-defined object boundaries. Experimental results on five benchmark saliency datasets demonstrate that M2Net outperforms state-of-the-art methods on different evaluation metrics.

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Region Diversity Based Saliency Density Maximization for Salient Object Detection

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences ◽

10.1587/transfun.e96.a.394 ◽

2013 ◽

Vol E96.A (1) ◽

pp. 394-397 ◽

Cited By ~ 1

Author(s):

Xin HE ◽

Huiyun JING ◽

Qi HAN ◽

Xiamu NIU

Keyword(s):

Object Detection ◽

Salient Object Detection ◽

Salient Object

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Saliency detection in deep learning era: trends of development

Information and Control Systems ◽

10.31799/1684-8853-2019-3-10-36 ◽

2019 ◽

pp. 10-36 ◽

Cited By ~ 2

Author(s):

M. N. Favorskaya ◽

L. C. Jain

Keyword(s):

Deep Learning ◽

Object Detection ◽

Event Detection ◽

Visual Analysis ◽

Saliency Detection ◽

Salient Object Detection ◽

Public Image ◽

Detection Methods ◽

Salient Object ◽

Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

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