期刊
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
卷 30, 期 10, 页码 3358-3371出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2019.2941017
关键词
Feature extraction; Saliency detection; Object detection; Aggregates; Task analysis; Visualization; Benchmark testing; Saliency detection; attentional dilated features; multiple layer aggregation
资金
- CUHK Research Committee
- Research Grants Council of the Hong Kong Special Administrative Region [CUHK 14201717]
- NSFC [61772206, U1611461, 61472145, 61902275]
- Guangdong Research and Development Key Project of China [2018B010107003]
- Guangdong High-Level Personnel Program [2016TQ03X319]
- Guangdong NSF [2017A030311027]
- Guangzhou Key Project in Industrial Technology [201802010027]
- Hong Kong Innovation and Technology Commission [ITS/319/17]
This paper presents a novel deep learning model to aggregate the attentional dilated features for salient object detection by exploring the complementary information between the global and local context in a convolutional neural network. There are two technical contributions to our network design. First, we develop an attentional dense atrous (dilated) spatial pyramid pooling (AD-ASPP) module to selectively use the local saliency cues captured by dilated convolutions with a small rate and the global saliency cues captured by dilated convolutions with a large rate. Second, taking the feature pyramid network as the backbone, we develop an aggregation network to integrate the refined features by formulating two consecutive chains of residual learning based modules: one chain from deep to shallow layers while another chain from shallow to deep layers. We evaluate our network on seven widely-used saliency detection benchmarks by comparing it against 21 state-of-the-art methods. Experimental results show that our network outperforms others on all the seven benchmark datasets.
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