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Open AccessProceedings ArticleDOI

Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection

TLDR
Amulet is presented, a generic aggregating multi-level convolutional feature framework for salient object detection that provides accurate salient object labeling and performs favorably against state-of-the-art approaches in terms of near all compared evaluation metrics.
Abstract
Fully convolutional neural networks (FCNs) have shown outstanding performance in many dense labeling problems. One key pillar of these successes is mining relevant information from features in convolutional layers. However, how to better aggregate multi-level convolutional feature maps for salient object detection is underexplored. In this work, we present Amulet, a generic aggregating multi-level convolutional feature framework for salient object detection. Our framework first integrates multi-level feature maps into multiple resolutions, which simultaneously incorporate coarse semantics and fine details. Then it adaptively learns to combine these feature maps at each resolution and predict saliency maps with the combined features. Finally, the predicted results are efficiently fused to generate the final saliency map. In addition, to achieve accurate boundary inference and semantic enhancement, edge-aware feature maps in low-level layers and the predicted results of low resolution features are recursively embedded into the learning framework. By aggregating multi-level convolutional features in this efficient and flexible manner, the proposed saliency model provides accurate salient object labeling. Comprehensive experiments demonstrate that our method performs favorably against state-of-the-art approaches in terms of near all compared evaluation metrics.

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Citations
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Posted Content

Towards High-Resolution Salient Object Detection.

TL;DR: Experiments illustrate that the proposed novel approach outperforms existing state-of-the-art methods on high-resolution saliency datasets by a large margin, and achieves comparable or even better performance than them on some widely used saliency benchmarks.
Journal ArticleDOI

AMFB: Attention based multimodal Factorized Bilinear Pooling for multimodal Fake News Detection

TL;DR: It is empirically show that text, when combined with the image, can improve the performance of the model, and the complexity of the proposed model is significantly lower than the state-of-the-art models.
Journal ArticleDOI

Progressive Self-Guided Loss for Salient Object Detection

TL;DR: Zhang et al. as discussed by the authors proposed a progressive self-guided loss function to facilitate deep learning-based salient object detection in images, which simulates a morphological closing operation on the model predictions for progressively creating auxiliary training supervisions to stepwise guide the training process.
Journal ArticleDOI

Attention-guided RGBD saliency detection using appearance information

TL;DR: A novel RGBD saliency model, where the attention-guided bottom-up and top-down modules are powerfully combined by using multi-level deep RGB features, to utilize the deep RGB and depth features in a sufficient and appropriate way.
Posted Content

Cascaded Context Pyramid for Full-Resolution 3D Semantic Scene Completion

TL;DR: This work proposes a novel deep learning framework, named Cascaded Context Pyramid Network (CCPNet), to jointly infer the occupancy and semantic labels of a volumetric 3D scene from a single depth image, and improves the labeling coherence with a cascaded context pyramid.
References
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Proceedings ArticleDOI

Deep Residual Learning for Image Recognition

TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article

Very Deep Convolutional Networks for Large-Scale Image Recognition

TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings Article

Very Deep Convolutional Networks for Large-Scale Image Recognition

TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Book ChapterDOI

U-Net: Convolutional Networks for Biomedical Image Segmentation

TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Proceedings ArticleDOI

Fully convolutional networks for semantic segmentation

TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
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