Results 11 to 20 of about 116,902 (337)
Backtracking Spatial Pyramid Pooling (SPP)-based Image Classifier for Weakly Supervised Top-down Salient Object Detection [PDF]
H. Cholakkal, J. Johnson, D. Rajan, "Backtracking Spatial Pyramid
Pooling (SPP)-based Image Classifier for Weakly Supervised Top-down Salient
Object Detection", in IEEE Transactions on Image processing, August 2018, 2016 Top-down saliency models produce a probability map that peaks at target locations specified by a task/goal such as object detection. They are usually trained in a fully supervised setting involving pixel-level annotations of objects. We propose a weakly supervised top-down saliency framework using only binary labels that indicate the presence/absence ...
Cholakkal, Hisham +2 more
arxiv +3 more sources
Spatio-Temporal Saliency Networks for Dynamic Saliency Prediction [PDF]
IEEE Transactions on Multimedia, 2018 Computational saliency models for still images have gained significant popularity in recent years. Saliency prediction from videos, on the other hand, has received relatively little interest from the community. Motivated by this, in this work, we study the use of deep learning for dynamic saliency prediction and propose the so-called spatio-temporal ...
Cagdas Bak +3 more
openaire +5 more sources
Quantitative Analysis of Saliency Models [PDF]
arXiv, 2016 Previous saliency detection research required the reader to evaluate performance qualitatively, based on renderings of saliency maps on a few shapes. This qualitative approach meant it was unclear which saliency models were better, or how well they compared to human perception. This paper provides a quantitative evaluation framework that addresses this
Dodgson, Neil Anthony +2 more
arxiv +3 more sources
Recurrent Attentional Networks for Saliency Detection [PDF]
arXiv, 2016 Convolutional-deconvolution networks can be adopted to perform end-to-end saliency detection. But, they do not work well with objects of multiple scales. To overcome such a limitation, in this work, we propose a recurrent attentional convolutional-deconvolution network (RACDNN).
Kuen, Jason, Wang, Gang, Wang, Zhenhua
arxiv +3 more sources
Saliency-based image correction for colorblind patients
Computational Visual Media, 2020 Improper functioning, or lack, of human cone cells leads to vision defects, making it impossible for affected persons to distinguish certain colors. Colorblind persons have color perception, but their ability to capture color information differs from ...
Jinjiang Li, Xiaomei Feng, Hui Fan
doaj +1 more source
Human reaction time in a mixed reality environment
Frontiers in Neuroscience, 2022 Over the last few years applications based on the use of immersive environments, where physical and digital objects coexist and interact, have gained widespread attention.
Syed Muhammad Umair Arif +3 more
doaj +1 more source
Deep Saliency Quality Assessment Network With Joint Metric
IEEE Access, 2018 Saliency detection aims to find the most conspicuous regions in an image, which highly catches the users' attention. High-quality saliency map plays an important role in boosting many other computer vision tasks, such as object detection and segmentation.
Liangzhi Tang +3 more
doaj +1 more source
Where Is My Mind (Looking at)? A Study of the EEG–Visual Attention Relationship
Informatics, 2022 Visual attention estimation is an active field of research at the crossroads of different disciplines: computer vision, deep learning, and medicine.
Victor Delvigne +7 more
doaj +1 more source
Open Linguistics, 2020 AbstractA commonly used concept in linguistics is salience. Oftentimes it is used without definition, and the meaning of the concept is repeatedly assumed to be self-explanatory. The definitions that are provided may vary greatly from one operationalization of salience to the next. In order to find out whether it is possible to postulate an overarching
Vincent Boswijk, Matt Coler
openaire +3 more sources
Saliency for free: Saliency prediction as a side-effect of object recognition [PDF]
2021, 2021 Saliency is the perceptual capacity of our visual system to focus our attention (i.e. gaze) on relevant objects. Neural networks for saliency estimation require ground truth saliency maps for training which are usually achieved via eyetracking experiments.
arxiv +1 more source