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Global contrast based salient region detection
Reliable estimation of visual saliency allows appropriate processing of images without prior knowledge of their contents, and thus remains an important step in many computer vision tasks includingExpand
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Salient Object Detection: A Discriminative Regional Feature Integration Approach
Feature integration provides a computational framework for saliency detection, and a lot of hand-crafted integration rules have been developed. In this paper, we present a principled extension,Expand
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Struck: Structured Output Tracking with Kernels
Adaptive tracking-by-detection methods are widely used in computer vision for tracking arbitrary objects. Current approaches treat the tracking problem as a classification task and use onlineExpand
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Deeply Supervised Salient Object Detection with Short Connections
Recent progress on saliency detection is substantial, benefiting mostly from the explosive development of Convolutional Neural Networks (CNNs). Semantic segmentation and saliency detection algorithmsExpand
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BING: Binarized Normed Gradients for Objectness Estimation at 300fps
Training a generic objectness measure to produce a small set of candidate object windows, has been shown to speed up the classical sliding window object detection paradigm. We observe that genericExpand
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BING: Binarized normed gradients for objectness estimation at 300fps
Training a generic objectness measure to produce object proposals has recently become of significant interest. We observe that generic objects with well-defined closed boundaries can be detected byExpand
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Efficient Salient Region Detection with Soft Image Abstraction
Detecting visually salient regions in images is one of the fundamental problems in computer vision. We propose a novel method to decompose an image into large scale perceptually homogeneous elementsExpand
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Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach
We investigate a principle way to progressively mine discriminative object regions using classification networks to address the weakly-supervised semantic segmentation problems. ClassificationExpand
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GMS: Grid-Based Motion Statistics for Fast, Ultra-Robust Feature Correspondence
Incorporating smoothness constraints into feature matching is known to enable ultra-robust matching. However, such formulations are both complex and slow, making them unsuitable for videoExpand
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Res2Net: A New Multi-scale Backbone Architecture
Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate strongerExpand
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