Results 1 to 10 of about 10,158 (162)
Characteristics of Visual Saliency Caused by Character Feature for Reconstruction of Saliency Map Model [PDF]
Visual saliency maps have been developed to estimate the bottom-up visual attention of humans. A conventional saliency map represents a bottom-up visual attention using image features such as the intensity, orientation, and color.
Hironobu Takano +2 more
doaj +2 more sources
Rethinking Gradient Weight’s Influence over Saliency Map Estimation [PDF]
Class activation map (CAM) helps to formulate saliency maps that aid in interpreting the deep neural network’s prediction. Gradient-based methods are generally faster than other branches of vision interpretability and independent of human guidance.
Masud An Nur Islam Fahim +3 more
doaj +2 more sources
Influence of image classification accuracy on saliency map estimation
Saliency map estimation in computer vision aims to estimate the locations where people gaze in images. Since people tend to look at objects in images, the parameters of the model pre-trained on ImageNet for image classification are useful for the ...
Taiki Oyama, Takao Yamanaka
doaj +3 more sources
Spatial competition on the master-saliency map. [PDF]
The saliency map model (Itti and Koch, 2000) is a hierarchically structured computational model, simulating visual saliency processing. Iso-feature processing on feature maps and conspicuity maps precedes cross-dimensional signal processing on the master map, where the most salient location of the visual field is selected.
Schade U, Meinecke C.
europepmc +6 more sources
An Enhanced Insect Pest Counter Based on Saliency Map and Improved Non-Maximum Suppression [PDF]
Chuntao Wang +2 more
exaly +2 more sources
PointCloud Saliency Maps [PDF]
Accepted to ICCV19 (oral)
Tianhang Zheng +4 more
openaire +2 more sources
Olfaction spontaneously highlights visual saliency map [PDF]
Sheng He
exaly +2 more sources
Why are Saliency Maps Noisy? Cause of and Solution to Noisy Saliency Maps [PDF]
Accepted at the 2019 ICCV Workshop on Interpreting and Explaining Visual AI Models (VXAI 2019)
Beomsu Kim +5 more
openaire +2 more sources
On Saliency Maps and Adversarial Robustness [PDF]
A Very recent trend has emerged to couple the notion of interpretability and adversarial robustness, unlike earlier efforts which solely focused on good interpretations or robustness against adversaries. Works have shown that adversarially trained models exhibit more interpretable saliency maps than their non-robust counterparts, and that this behavior
Puneet Mangla +2 more
openaire +3 more sources
Aircraft Detection in High-Resolution SAR Images Based on a Gradient Textural Saliency Map [PDF]
Yansheng Li, Yihua Tan, Jinwen Tian
exaly +2 more sources

