Salient Object Detection in the Deep Learning Era: An In-Depth Survey

Wang, Wenguan; Lai, Qiuxia; Fu, Huazhu; Shen, Jianbing; Ling, Haibin; Yang, Ruigang

doi:10.1109/tpami.2021.3051099

Cited by 433 publications

(209 citation statements)

References 156 publications

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“…Hou et al (2019) merge multi-level features of a VGG network with fusion and cross-entropy losses. A survey by Wang et al (2021) reveals that most SOD models employ VGG and ResNet as backbone architectures and train the model with the standard binary cross-entropy loss. More recent work has developed the end-to-end framework with GANs (Wang et al 2020a) and some works include depth information from RGB-D cameras .…”

Section: Salient Object Detectionmentioning

confidence: 99%

Artificial intelligence in the creative industries: a review

2021

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This paper reviews the current state of the art in artificial intelligence (AI) technologies and applications in the context of the creative industries. A brief background of AI, and specifically machine learning (ML) algorithms, is provided including convolutional neural networks (CNNs), generative adversarial networks (GANs), recurrent neural networks (RNNs) and deep Reinforcement Learning (DRL). We categorize creative applications into five groups, related to how AI technologies are used: (i) content creation, (ii) information analysis, (iii) content enhancement and post production workflows, (iv) information extraction and enhancement, and (v) data compression. We critically examine the successes and limitations of this rapidly advancing technology in each of these areas. We further differentiate between the use of AI as a creative tool and its potential as a creator in its own right. We foresee that, in the near future, ML-based AI will be adopted widely as a tool or collaborative assistant for creativity. In contrast, we observe that the successes of ML in domains with fewer constraints, where AI is the ‘creator’, remain modest. The potential of AI (or its developers) to win awards for its original creations in competition with human creatives is also limited, based on contemporary technologies. We therefore conclude that, in the context of creative industries, maximum benefit from AI will be derived where its focus is human-centric—where it is designed to augment, rather than replace, human creativity.

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Section: Salient Object Detectionmentioning

confidence: 99%

Artificial intelligence in the creative industries: a review

2021

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“…As demonstrated in existing literature [57], the SOC dataset [58] is the most challenging dataset. Some attempts have been made on this dataset in Deepside [44] and SCRNet [59].…”

Section: Discussionmentioning

confidence: 99%

Multi-Scale Global Contrast CNN for Salient Object Detection

Feng

Gao

et al. 2020

Sensors

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Salient object detection (SOD) is a fundamental task in computer vision, which attempts to mimic human visual systems that rapidly respond to visual stimuli and locate visually salient objects in various scenes. Perceptual studies have revealed that visual contrast is the most important factor in bottom-up visual attention process. Many of the proposed models predict saliency maps based on the computation of visual contrast between salient regions and backgrounds. In this paper, we design an end-to-end multi-scale global contrast convolutional neural network (CNN) that explicitly learns hierarchical contrast information among global and local features of an image to infer its salient object regions. In contrast to many previous CNN based saliency methods that apply super-pixel segmentation to obtain homogeneous regions and then extract their CNN features before producing saliency maps region-wise, our network is pre-processing free without any additional stages, yet it predicts accurate pixel-wise saliency maps. Extensive experiments demonstrate that the proposed network generates high quality saliency maps that are comparable or even superior to those of state-of-the-art salient object detection architectures.

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“…本文以 4 个典型数据集 ECSSD [20] , PASCAL-S [21] , DUT-OMRON [22] 以及 DUTS [18] (1) P-R(precision-recall)曲线 [23] . (2) F-measure [23] . 当显著性检测算法用于给定的测试集时, 还可结合不同阈值下每个测试集…”

Section: 实验与结果分析unclassified

Image Saliency Detection via Two-Stream Feature Fusion and Adversarial Learning

Zhang

Huo

et al. 2021

J Comput-Aided Design Comput Graphics

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To achieve meaningful combination of low-level features and semantic information of salient regions or targets, and to obtain saliency detection results with more complete structure and clearer boundary, an algorithm of color image saliency detection via two-stream feature fusion and adversarial learning (SaTSAL) is proposed. Firstly, different levels of image features are extracted from bottom to top by means of a two-stream heterogeneous backbone network based on VGG-16 and Res2Net-50. Secondly, in each stream, different feature maps from the same level are fetched into one convolution tower module to enrich intra-level multi-scale information. Thirdly, a predicted saliency map is generated by top-down laterally fusing of cross-stream feature maps level by level, so as to effectively make full use of high-level semantic features and low-level image features. Finally, under the mainframe of conditional generative adversarial networks (CGAN), a higher structural similarity between detected results and salient objects can be strength-第 3 期张艺涵, 等: 结合双流特征融合及对抗学习的图像显著性检测 377

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Salient Object Detection in the Deep Learning Era: An In-Depth Survey

Cited by 433 publications

References 156 publications

Artificial intelligence in the creative industries: a review

Artificial intelligence in the creative industries: a review

Multi-Scale Global Contrast CNN for Salient Object Detection

Image Saliency Detection via Two-Stream Feature Fusion and Adversarial Learning

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