Saliency Detection is a preprocessing step in computer vision which aims at finding salient objects in an image.
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Early fusion and the result fusion schemes fuse RGB and depth information at the input and output stages, respectively, hence incur the problem of distribution gap or information loss.
Secondly, we benchmark seven representative light field SOD models together with several cutting-edge RGB-D SOD models on four widely used light field datasets, from which insightful discussions and analyses including the comparison between light field SOD and RGB-D SOD models are achieved.
In this work, we propose a 3D fully convolutional architecture for video saliency detection that employs multi-head supervision on intermediate maps (referred to as conspicuity maps) generated using features extracted at different abstraction level.
In recent years, co-saliency object detection (CoSOD) has achieved significant progress and played a key role in the retrieval-related tasks, e. g., image retrieval and video foreground detection.
Co-saliency detection aims to detect common salient objects from a group of relevant images.
We present a cyclic global context salient instance segmentation network (CGCNet), which is supervised by the combination of the binary salient regions and bounding boxes from the existing saliency detection datasets.
In this paper, we show that existing recognition and localization deep architectures, that have not been exposed to eye tracking data or any saliency datasets, are capable of predicting the human visual saliency.
With the rapid development of deep learning techniques, image saliency deep models trained solely by spatial information have occasionally achieved detection performance for video data comparable to that of the models trained by both spatial and temporal information.
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.