26,926 research outputs found
Augmented semi-supervised learning for salient object detection with edge computing
[EN] Salient object detection (SOD) from raw sensor images in the edge networks can effectively speed up the decision-making process in the complex environments, because it simulates the mechanism of human attention to identify salient objects from images. The success of supervised deep learning approaches have been widely proved SOD field. However, the imbalanced and limited training data at each edge device pose a huge challenge for us to deploy deep learning methods in the edge computing environments. In this article, we propose a cloud-edge distributed augmented semi-supervised learning architecture for SOD over the edge networks. The framework consists of two components: the base classification networks are employed in different edge nodes, and the reverse augmented network is employed in cloud. First, the base classification networks are trained with data from edge nodes while the reverse augmented network is trained with the whole data. Then, we concatenate each base classification network with reverse augmented network, thus the latter network can help the training of former network. Finally, we integrate the outputs of all base classification network to generate the pseudo-labels, which are used for semi-supervised learning of the augment network. We demonstrated a convincing performance of our semi-supervised learning framework on four bench-marked data-sets. These results show that our augmented semi-supervised learning framework can outperform other optimization strategies on deep learning for the edge computing.Yu, C.; Zhang, Y.; Mukherjee, M.; Lloret, J. (2022). Augmented semi-supervised learning for salient object detection with edge computing. IEEE Wireless Communications. 29(3):109-114. https://doi.org/10.1109/MWC.2020.200035110911429
A Reverse Hierarchy Model for Predicting Eye Fixations
A number of psychological and physiological evidences suggest that early
visual attention works in a coarse-to-fine way, which lays a basis for the
reverse hierarchy theory (RHT). This theory states that attention propagates
from the top level of the visual hierarchy that processes gist and abstract
information of input, to the bottom level that processes local details.
Inspired by the theory, we develop a computational model for saliency detection
in images. First, the original image is downsampled to different scales to
constitute a pyramid. Then, saliency on each layer is obtained by image
super-resolution reconstruction from the layer above, which is defined as
unpredictability from this coarse-to-fine reconstruction. Finally, saliency on
each layer of the pyramid is fused into stochastic fixations through a
probabilistic model, where attention initiates from the top layer and
propagates downward through the pyramid. Extensive experiments on two standard
eye-tracking datasets show that the proposed method can achieve competitive
results with state-of-the-art models.Comment: CVPR 2014, 27th IEEE Conference on Computer Vision and Pattern
Recognition (CVPR). CVPR 201
Improvised Salient Object Detection and Manipulation
In case of salient subject recognition, computer algorithms have been heavily
relied on scanning of images from top-left to bottom-right systematically and
apply brute-force when attempting to locate objects of interest. Thus, the
process turns out to be quite time consuming. Here a novel approach and a
simple solution to the above problem is discussed. In this paper, we implement
an approach to object manipulation and detection through segmentation map,
which would help to desaturate or, in other words, wash out the background of
the image. Evaluation for the performance is carried out using the Jaccard
index against the well-known Ground-truth target box technique.Comment: 7 page
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