Proceedings ArticleDOI
A Stagewise Refinement Model for Detecting Salient Objects in Images
Tiantian Wang,Ali Borji,Lihe Zhang,Pingping Zhang,Huchuan Lu +4 more
- pp 4039-4048
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TLDR
This work proposes to augment feedforward neural networks with a novel pyramid pooling module and a multi-stage refinement mechanism for saliency detection and shows that the proposed method compares favorably against the state-of-the-art approaches.Abstract:
Deep convolutional neural networks (CNNs) have been successfully applied to a wide variety of problems in computer vision, including salient object detection. To detect and segment salient objects accurately, it is necessary to extract and combine high-level semantic features with low-levelfine details simultaneously. This happens to be a challenge for CNNs as repeated subsampling operations such as pooling and convolution lead to a significant decrease in the initial image resolution, which results in loss of spatial details and finer structures. To remedy this problem, here we propose to augment feedforward neural networks with a novel pyramid pooling module and a multi-stage refinement mechanism for saliency detection. First, our deep feedward net is used to generate a coarse prediction map with much detailed structures lost. Then, refinement nets are integrated with local context information to refine the preceding saliency maps generated in the master branch in a stagewise manner. Further, a pyramid pooling module is applied for different-region-based global context aggregation. Empirical evaluations over six benchmark datasets show that our proposed method compares favorably against the state-of-the-art approaches.read more
Citations
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Proceedings ArticleDOI
BASNet: Boundary-Aware Salient Object Detection
TL;DR: Experimental results on six public datasets show that the proposed predict-refine architecture, BASNet, outperforms the state-of-the-art methods both in terms of regional and boundary evaluation measures.
Proceedings ArticleDOI
EGNet: Edge Guidance Network for Salient Object Detection
TL;DR: In this article, an edge guidance network (EGNet) is proposed for salient object detection with three steps to simultaneously model these two kinds of complementary information in a single network, which can help locate salient objects especially their boundaries more accurately.
Proceedings ArticleDOI
A Simple Pooling-Based Design for Real-Time Salient Object Detection
TL;DR: This work solves the problem of salient object detection by investigating how to expand the role of pooling in convolutional neural networks by building a global guidance module (GGM) and designing a feature aggregation module (FAM) to make the coarse-level semantic information well fused with the fine-level features from the top-down path- way.
Proceedings ArticleDOI
Cascaded Partial Decoder for Fast and Accurate Salient Object Detection
Zhe Wu,Li Su,Qingming Huang +2 more
TL;DR: A novel Cascaded Partial Decoder (CPD) framework for fast and accurate salient object detection and applies the proposed framework to optimize existing multi-level feature aggregation models and significantly improve their efficiency and accuracy.
Journal ArticleDOI
U2-Net: Going deeper with nested U-structure for salient object detection
Xuebin Qin,Zichen Vincent Zhang,Chenyang Huang,Masood Dehghan,Osmar R. Zaïane,Martin Jagersand +5 more
TL;DR: A simple yet powerful deep network architecture, U2-Net, for salient object detection (SOD), a two-level nested U-structure that enables us to train a deep network from scratch without using backbones from image classification tasks.
References
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Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia,Evan Shelhamer,Jeff Donahue,Sergey Karayev,Jonathan Long,Ross Girshick,Sergio Guadarrama,Trevor Darrell +7 more
TL;DR: Caffe as discussed by the authors is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures.
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