Attentional Selection for Object Recognition - A Gentle Way

By Dirk Bernhardt-Walther, Laurent Itti, Maximilian Riesenhuber, Tomaso A. Poggio, Christof Koch. 2002

266 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Residual Attention Network for Image Classification (2017, background): “The mixed nature of attention has been studied extensively in the previous literatures [34, 16, 23, 40].”
Modeling attention to salient proto-objects (2006, unknown): “S1, which correspond approximately to areas V4 and V1, by gain modulation with variable modulation strength ( Walther, Itti,Riesenhuber,Poggio,&Koch,2002 ).”
Components of bottom-up gaze allocation in natural images. (2005, background): “Indeed, models of bottom-up attention have been shown to improve the performance of traditional computer vision object recognition systems, both in the visual learning phase as well as in the subsequent recognition phase (Miau and Itti, 2001; Walther et al., 2002; Rutishauser et al., 2004).”
SalGAN: Visual Saliency Prediction with Generative Adversarial Networks (2017, background): “ere a human observer explores the image for a few seconds with no particular task in mind. Therefore, saliency prediction is indispensable for various machine vision tasks such as object recognition (Walther et al., 2002). Visual saliency data are traditionally collected by eyetrackers (Judd et al.,”
Saliency Detection: A Spectral Residual Approach (2007, methodology): “These images are taken from [11], [10], and [26].”
A Novel Multiresolution Spatiotemporal Saliency Detection Model and Its Applications in Image and Video Compression (2010, methodology): “He also applied the model to accomplish object recognition tasks [13].”
Spatio-temporal Saliency detection using phase spectrum of quaternion fourier transform (2008, methodology): “He also applied it to the object recognition tasks [9].”
Automatic foveation for video compression using a neurobiological model of visual attention (2004, methodology): “Additional details on this technique have been previously described [67]”
Is bottom-up attention useful for object recognition? (2004, background): “While several computational implementations of models of visual attention have been published [29] [4] [14], little work has been done in investigating its benefits for object learning and recognition in a machine vision context (but see [6] [20] [31]).”
Predicting Human Eye Fixations via an LSTM-Based Saliency Attentive Model (2016, background): “tensive research effort aimed to emulate such selective visual mechanisms, as computational saliency can be applied to a wide range of applications like image retargeting [2], [3], object recognition [4], video compression [5], tracking [6] and other data-dependent tasks such as image captioning [7]”
DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations (2015, methodology): “This modeling has applications in vision-related tasks such as video compression [8], object and action recognition [9], [10], image retargetting [11] and surveillance systems [12].”
Hierarchical Image Saliency Detection on Extended CSSD (2016, unknown): “Index Terms—Saliency detection, region scale Ç”
Learning to Detect A Salient Object (2007, background): “Recent studies [18, 22, 26] demonstrated that visual attention helps object recognition, tracking, and detection as well.”
Saliency Detection by Multitask Sparsity Pursuit (2012, background): “V ISUAL attention is crucial in determining visual experience, leading to the challenging problem of saliency detection that is an important function for image processing and understanding [1]–[4].”
Modeling the influence of task on attention. (2005, unknown): “Priedman-Hill, Stewart, & OConnell, 1992), size (Treisman & Gelade, 1980), closure (Enns, 1986; Triesman & Souther, 1986 ), color (hue) (Bauer, Jolicoeur, & Cowan,”
40 years of cognitive architectures: core cognitive abilities and practical applications (2018, background): “1995), extensions to HMAX (Riesenhuber 2005; Walther et al. 2002; Walther and Koch 2007) and others (Frintrop et al.”