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Global contrast based salient region detection
TLDR
This work proposes a regional contrast based saliency extraction algorithm, which simultaneously evaluates global contrast differences and spatial coherence, and consistently outperformed existing saliency detection methods.
Struck: Structured Output Tracking with Kernels
TLDR
A framework for adaptive visual object tracking based on structured output prediction that is able to outperform state-of-the-art trackers on various benchmark videos and can easily incorporate additional features and kernels into the framework, which results in increased tracking performance.
Salient Object Detection: A Discriminative Regional Feature Integration Approach
TLDR
This paper presents a principled extension, supervised feature integration, which learns a random forest regressor to discriminatively integrate the saliency features for saliency computation and significantly outperforms state-of-the-art methods on seven benchmark datasets.
Deeply Supervised Salient Object Detection with Short Connections
TLDR
A new saliency method is proposed by introducing short connections to the skip-layer structures within the HED architecture, which produces state-of-the-art results on 5 widely tested salient object detection benchmarks, with advantages in terms of efficiency, effectiveness, and simplicity over the existing algorithms.
BING: Binarized Normed Gradients for Objectness Estimation at 300fps
TLDR
It is observed that generic objects with well-defined closed boundary can be discriminated by looking at the norm of gradients, with a suitable resizing of their corresponding image windows in to a small fixed size, so as to train a generic objectness measure.
BING: Binarized normed gradients for objectness estimation at 300fps
TLDR
To improve localization quality of the proposals while maintaining efficiency, a novel fast segmentation method is proposed and demonstrated its effectiveness for improving BING’s localization performance, when used in multi-thresholding straddling expansion (MTSE) post-processing.
Res2Net: A New Multi-Scale Backbone Architecture
TLDR
This paper proposes a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block that represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.
Object Region Mining with Adversarial Erasing: A Simple Classification to Semantic Segmentation Approach
TLDR
This work investigates a principle way to progressively mine discriminative object regions using classification networks to address the weakly-supervised semantic segmentation problems and proposes a new adversarial erasing approach for localizing and expanding object regions progressively.
EGNet: Edge Guidance Network for Salient Object Detection
TLDR
This paper presents an edge guidance network (EGNet) for salient object detection with three steps to simultaneously model these two kinds of complementary information in a single network to solve the complementarity between salient edge information and salient object information.
Richer Convolutional Features for Edge Detection
TLDR
The proposed network fully exploits multiscale and multilevel information of objects to perform the image-to-image prediction by combining all the meaningful convolutional features in a holistic manner and achieves state-of-the-art performance on several available datasets.
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