Saliency Detection is a preprocessing step in computer vision which aims at finding salient objects in an image.
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In this way, even though the overall video saliency quality is heavily dependent on its spatial branch, however, the performance of the temporal branch still matter.
As an essential component of visual simultaneous localization and mapping (SLAM), place recognition is crucial for robot navigation and autonomous driving.
The proposed model consists of two sub-models parameterized by neural networks: (1) a saliency predictor that maps input images to clean saliency maps, and (2) a noise generator, which is a latent variable model that produces noises from Gaussian latent vectors.
Evaluation on seven RGBD datasets demonstrates that, without using hand-labelled saliency ground truth for RGB-D datasets and using only the RGB channels of these datasets at inference, our system achieves performance that is comparable to state-of-the-art methods that use hand-labelled saliency maps for RGB-D data at training and use the depth channels of these datasets at inference.
Depth data containing a preponderance of discriminative power in location have been proven beneficial for accurate saliency prediction.
In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process.
For this task, we present a novel adaptive graph convolutional network with attention graph clustering (GCAGC).
The original image and different versions of smoothed images are blended to generate Bokeh effect with the help of a monocular depth estimation network.
Traditional 3D mesh saliency detection algorithms and corresponding databases were proposed under several constraints such as providing limited viewing directions and not taking the subject's movement into consideration.
The deployment of automated systems to diagnose diseases from medical images is challenged by the requirement to localise the diagnosed diseases to justify or explain the classification decision.