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Despite these strong priors, we show that deep trackers often default to tracking by saliency detection - without relying on the object instance representation.
Fully convolutional deep correlation networks are integral components of state-of- the-art approaches to single object visual tracking.
Saliency detection is one of the most challenging problems in the fields of image analysis and computer vision.
Fusing complementary information of RGB and depth has been demonstrated to be effective for image salient object detection which is known as RGB-D salient object detection problem.
Alternative unsupervised approaches rely on careful selection of multiple handcrafted saliency methods to generate noisy pseudo-ground-truth labels.
Beneficial from Fully Convolutional Neural Networks (FCNs), saliency detection methods have achieved promising results.
Object recognition in 3D point clouds is a challenging task, mainly when time is an important factor to deal with, such as in industrial applications.
As a common visual problem, co-saliency detection within a single image does not attract enough attention and yet has not been well addressed.
In this paper, we introduce the proposed methods in both saliency detection and retargeting.
It consists of two building blocks: first, the encoder network extracts low-resolution spatiotemporal features from an input clip of several consecutive frames, and then the following prediction network decodes the encoded features spatially while aggregating all the temporal information.