Beyond Universal Saliency: Personalized Saliency Prediction with Multi-task CNN

Xu, Yanyu; Li, Nianyi; Wu, Junru; Yu, Jingyi; Gao, Shenghua

doi:10.24963/ijcai.2017/543

Cited by 17 publications

(13 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, current mood and emotions may greatly influence the interior state of an individual and the individual's attention while viewing emotionally neutral stimuli. Such a personalized attention prediction is a current open topic in attention modelling [50,51]. However, there is no evidence of such a publicly available, thorough research.…”

Section: Attention Models Incorporating Emotionsmentioning

confidence: 99%

Effects of individual’s emotions on saliency and visual search

et al. 2020

View full text Add to dashboard Cite

Psychological studies have already proven a connection between emotional stimuli and visual attention. However, there is a lack of evidence of how much an individual's mood influences visual information processing of emotionally neutral stimuli. In contrast to prior studies, we explored the effect of positive mood on bottom-up low-level visual saliency. In our extensive experimental studies, we induced positive or neutral emotions in 106 subjects using a psychological method of autobiographical memories and listening to positive music and then performed an eye-tracking study with three task typesfree viewing, memorizing the image content and visual search tasks. We explored differences in the human gaze behaviour between both emotions and relate them to the bottom-up attention correlation between emotionally aroused and non-aroused subjects. We observed that positive emotions induce a strong broadening and engagement effect on the bottom-up attention while performing all task types. On the other hand, we found out that certain tasks are solved less efficiently when experiencing a positive mood. Therefore, we claim that positive mood may also act as a distractor while performing certain types of tasks.

show abstract

Section: Attention Models Incorporating Emotionsmentioning

confidence: 99%

Effects of individual’s emotions on saliency and visual search

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Personalized Saliency Detection [59]. From the psychological point of view, different people have different focuses of attention (FOV) on the same scene.…”

Section: Future Directionsmentioning

confidence: 99%

Exploiting the Value of the Center-dark Channel Prior for Salient Object Detection

Zhu¹,

Zhang²,

Li³

et al. 2018

Preprint

View full text Add to dashboard Cite

Saliency detection aims to detect the most attractive objects in images and is widely used as a foundation for various applications. In this paper, we propose a novel salient object detection algorithm for RGB-D images using center-dark channel priors. First, we generate an initial saliency map based on a color saliency map and a depth saliency map of a given RGB-D image. Then, we generate a center-dark channel map based on center saliency and dark channel priors. Finally, we fuse the initial saliency map with the center dark channel map to generate the final saliency map. Extensive evaluations over four benchmark datasets demonstrate that our proposed method performs favorably against most of the state-of-the-art approaches. Besides, we further discuss the application of the proposed algorithm in small target detection and demonstrate the universal value of center-dark channel priors in the field of object detection. CCS Concepts: • General and reference → Cross-computing tools and techniques; • Computer systems organization → Applications-Computer vision; • Image Processing and Computer → Scene Analysis-Object recognition;

show abstract

“…It is difficult to realize the PSM prediction without acquiring individual gaze data for analyzing their gaze tendencies. Then Xu et al collected many diverse images and corresponding gaze data of 30 observers and tried to predict the PSMs [11,16]. To the best of our knowledge, this study is the first attempt at construction of the This work was partly supported by the KAKENHI Grant Number JP17H01744.…”

Section: Introductionmentioning

confidence: 99%