Journal Article10.1007/S11042-020-08849-Y
A brief survey of visual saliency detection
TL;DR: A detailed overview of the recent progress of saliency detection models in terms of heuristic- based techniques and deep learning-based techniques is demonstrated.
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Abstract: Salient object detection models mimic the behavior of human beings and capture the most salient region/object from the images or scenes, this field contains many important applications in both computer vision and pattern recognition tasks. Despite hundreds of models that have been proposed in this field, but still, it requires a large room for research. This paper demonstrates a detailed overview of the recent progress of saliency detection models in terms of heuristic-based techniques and deep learning-based techniques. we have discussed and reviewed its co-related fields, such as Eye-fixation-prediction, RGBD salient-object-detection, co-saliency object detection, and video-saliency-detection models. We have reviewed the key issues of the current saliency models and discussed future trends and recommendations. The broadly utilized datasets and assessment strategies are additionally investigated in this paper.
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Citations
Colorectal polyp region extraction using saliency detection network with neutrosophic enhancement
TL;DR: Zhang et al. as mentioned in this paper used neutrosophic theory to enhance the quality of specular reflections detection in the colonoscopy images, and introduced two level short connections into the saliency detection network, aiming to take advantage of the multi-level and multi-scale features extracted from different stages of the network.
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Dif-Fusion: Towards High Color Fidelity in Infrared and Visible Image Fusion with Diffusion Models
TL;DR: Wang et al. as mentioned in this paper proposed a novel method with diffusion models, termed as Dif-Fusion, to generate the distribution of the multi-channel input data, which increases the ability of multi-source information aggregation and the fidelity of colors.
Open World Entity Segmentation
TL;DR: Zhang et al. as discussed by the authors proposed a CondInst-like fully-convolutional architecture with two novel modules specifically designed to exploit the class-agnostic and non-overlapping requirements of entity segmentation.
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Deep learning in electron microscopy
Jeffrey M. Ede
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