Weakly supervised visual saliency prediction
The success of current deep saliency models heavily depends on large amounts of
annotated human fixation data to fit the highly non-linear mapping between the stimuli and …
annotated human fixation data to fit the highly non-linear mapping between the stimuli and …
Saliency prediction in the deep learning era: Successes and limitations
A Borji - IEEE transactions on pattern analysis and machine …, 2019 - ieeexplore.ieee.org
Visual saliency models have enjoyed a big leap in performance in recent years, thanks to
advances in deep learning and large scale annotated data. Despite enormous effort and …
advances in deep learning and large scale annotated data. Despite enormous effort and …
What do deep saliency models learn about visual attention?
In recent years, deep saliency models have made significant progress in predicting human
visual attention. However, the mechanisms behind their success remain largely unexplained …
visual attention. However, the mechanisms behind their success remain largely unexplained …
Saliency prediction in the deep learning era: Successes, limitations, and future challenges
A Borji - arXiv preprint arXiv:1810.03716, 2018 - arxiv.org
Visual saliency models have enjoyed a big leap in performance in recent years, thanks to
advances in deep learning and large scale annotated data. Despite enormous effort and …
advances in deep learning and large scale annotated data. Despite enormous effort and …
Visual saliency prediction using a mixture of deep neural networks
Visual saliency models have recently begun to incorporate deep learning to achieve
predictive capacity much greater than previous unsupervised methods. However, most …
predictive capacity much greater than previous unsupervised methods. However, most …
[HTML][HTML] Contextual encoder–decoder network for visual saliency prediction
Predicting salient regions in natural images requires the detection of objects that are present
in a scene. To develop robust representations for this challenging task, high-level visual …
in a scene. To develop robust representations for this challenging task, high-level visual …
[HTML][HTML] TranSalNet: Towards perceptually relevant visual saliency prediction
Convolutional neural networks (CNNs) have significantly advanced computational
modelling for saliency prediction. However, accurately simulating the mechanisms of visual …
modelling for saliency prediction. However, accurately simulating the mechanisms of visual …
Deep salience: Visual salience modeling via deep belief propagation
Visual salience is an intriguing phenomenon observed in biological neural systems.
Numerous attempts have been made to model visual salience mathematically using various …
Numerous attempts have been made to model visual salience mathematically using various …
Understanding and visualizing deep visual saliency models
Recently, data-driven deep saliency models have achieved high performance and have
outperformed classical saliency models, as demonstrated by results on datasets such as the …
outperformed classical saliency models, as demonstrated by results on datasets such as the …
Satsal: A multi-level self-attention based architecture for visual saliency prediction
Human visual Attention modelling is a persistent interdisciplinary research challenge,
gaining new interest in recent years mainly due to the latest developments in deep learning …
gaining new interest in recent years mainly due to the latest developments in deep learning …