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ABSNet: Aesthetics-Based Saliency Network Using Multi-Task Convolutional Network
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DOI：10.1109/LSP.2020.3035065.png)
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摘要
En 中文
As a smart visual attention mechanism to analyze visual scenes, visual saliency has been shown to closely correlate with semantic information such as faces. Although many semantic-information-guided saliency models have been proposed, to the best of our knowledge, no semantic information in affective domain has been employed for saliency detection. Aesthetic, the affective perceptual quality that integrates factors like scene composition and contrast, can certainly benefit visual attention that highly depends on these visual factors. In this letter, we propose an end-to-end multi-task framework called aesthetics-based saliency network (ABSNet). We use three commonly-used shared backbones and design two distinct branches for each task. Mean square error (MSE) loss and Earth Mover's Distance (EMD) loss are jointly adopted to alternately train the shared network and individual branch for different tasks, facilitating the proposed model to extract more effective features for visual perception. Moreover, our model is resolution-friendly to predict saliency for images of arbitrary size. It has been shown that the proposed multi-task method is superior over single-task version and outperforms state-of-the-art saliency methods.
Keyword:
Visualization
Task analysis
Feature extraction
Saliency detection
Signal processing algorithms
Prediction algorithms
Semantics
Aesthetics assessment
multi-task learning
visual saliency detection
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期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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Caroli Linnaei ... Species plantarum :exhibentes plantas rite cognitas, ad genera relatas, cum differentiis specificis, nominibus trivialibus, synonymis selectis, locis natalibus, secundum systema sexuale digestas卡罗利·林奈乌斯……《植物种志》：展示正确认识的植物，按属归类，带有种级差异、常用名、精选同义词、原产地，并按两性系统分类整理。.png)
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