Book ChapterDOI
Visual Saliency Detection via Convolutional Gated Recurrent Units
Sayanti Bardhan,Sukhendu Das,Shibu Jacob +2 more
- pp 162-174
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TLDR
This work proposes a proposed novel end-to-end framework with a Contextual Unit (CTU) module that models the scene contextual information to give efficient saliency maps with the help of Convolutional GRU (Conv-GRU).Abstract:
Context is an important aspect for accurate saliency detection. However, the question of how to formally model image context within saliency detection frameworks is still an open problem. Recent saliency detection models designed using complex Deep Neural Networks to extract robust features, however often fail to select the right contextual features. These methods generally utilize physical attributes of objects for generating final saliency maps, but ignores scene contextual information. In this paper, we overcome such limitation using (i) a proposed novel end-to-end framework with a Contextual Unit (CTU) module that models the scene contextual information to give efficient saliency maps with the help of Convolutional GRU (Conv-GRU). This is the first work reported so far that utilizes Conv-GRU to generate image saliency maps. In addition, (ii) we propose a novel way of using the Conv-GRU that helps to refine saliency maps based on input image context. The proposed model has been evaluated on challenging benchmark saliency datasets, where it outperforms prominent state-of-the-art methods.read more
Citations
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Proceedings ArticleDOI
Salient Object Detection by Contextual Refinement
TL;DR: A novel saliency detection framework with a Contextual Refinement Module (CRM) which consists of two sub-networks, Object Relation Unit (ORU) and Scene Context Unit (SCU) which captures complementary contextual information to give a holistic estimation of salient regions.
Journal ArticleDOI
BGRDNet: RGB-D salient object detection with a bidirectional gated recurrent decoding network
Journal ArticleDOI
An Image Saliency Detection Method Based on Combining Global and Local Information
TL;DR: Experimental results show that the proposed saliency target detection algorithm can not only accurately and comprehensively extract significant target regions but also retain more texture information and complete edge information while satisfying the human visual experience.
Journal ArticleDOI
AGRFNet: Two-stage cross-modal and multi-level attention gated recurrent fusion network for RGB-D saliency detection
TL;DR: Zhang et al. as discussed by the authors proposed an Attention Gated Recurrent Unit (AGRU) for RGB-D saliency detection, which can reduce the influence of low-quality depth image, and retain more semantic features in the progressive fusion process.
References
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Proceedings ArticleDOI
Learning to Promote Saliency Detectors
TL;DR: This work forms a zero-shot learning problem to promote existing saliency detectors and significantly improves accuracy of existing methods and compares favorably against state-of-the-art approaches.
Posted Content
Salient Object Detection by Lossless Feature Reflection.
TL;DR: Zhang et al. as discussed by the authors proposed a symmetrical fully convolutional network (SFCN) to learn complementary saliency features under the guidance of lossless feature reflection, where location information and contextual and semantic information of salient objects are jointly utilized to supervise the proposed network for more accurate saliency predictions.
Proceedings ArticleDOI
Multi-criteria Energy Minimization with Boundedness, Edge-density and Rarity, for Object Saliency in Natural Images
Sudeshna Roy,Sukhendu Das +1 more
TL;DR: A novel multi-criteria objective function which captures many dependencies and the scene structure for correct spatial propagation of low-level priors to perform salient object segmentation, in such cases as the PASCAL-VOC challenge dataset.
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