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
AWANet: Attentive-Aware Wide-Kernels Asymmetrical Network with Blended Contour Information for Salient Object Detection
TL;DR: Zhang et al. as mentioned in this paper proposed a saliency detection network based on three novel contributions: dense feature extraction unit (DFU), cross-feature integration unit (CFIU), and contour-aware saliency refinement unit (CSRU).
Pre-processing of Retinal Images for Removal of Outliers
Niharika Thakur,Mamta Juneja +1 more
TL;DR: The performance of the proposed approach for pre-processing the retinal fundus image is found to be better than the state of the art based on the analysis using metrics such as peak signal to ratio, mean square error and structural similarity index.
5
Unsupervised Saliency Detection via kNN Mechanism and Object-Biased Prior
TL;DR: This work discovered that the k-nearest neighbour (kNN) mechanism assumes the presence of similar objects in close proximity and introduced an unfixed k value and combined it with the clustering idea of k-means to develop a novel algorithm called kNN clustering.
4
Visual Saliency Modeling with Deep Learning: A Comprehensive Review
TL;DR: A comprehensive review of the recent advances in eye fixation prediction and salient object detection, harnessing deep learning and an overview on multi-modal saliency prediction that considers audio in dynamic scenes is provided.
4
Temporal Saliency Detection Towards Explainable Transformer-Based Timeseries Forecasting
Nghia Duong‐Trung,Du Nguyen,Danh Le-Phuoc +2 more
TL;DR: Temporal Saliency Detection (TSD) introduces an attention-based architecture for multi-horizon time series forecasting that effectively encodes saliency-related temporal patterns. TSD utilizes a series of information contracting and expanding blocks inspired by the U-Net style architecture to facilitate multiresolution analysis of saliency patterns.
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