A saliency map in primary visual cortex.

By Zhaoping Li. Trends in Cognitive Sciences, 2002

700 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/670e0c07765761cd3d5af9a27fb1b6a9cd4fa0c8

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Citations in the corpus

Visual search: a retrospective. (2011, background): “This makes pure bottom-up saliency-type models capable of predicting saccades in simple displays with sparse elements (Li, 2002) but less successful for more complex scenes that contain task-relevant and task-irrelevant salient regions.”
Saliency, attention, and visual search: an information theoretic approach. (2009, positive): “It is also interesting to note that suppression of this type is observed for virtually all types of features (Shen, Xu, & Li, 2007). It is important to note that AIM is the sole proposal that is consistent with the entire range of psychophysical results considered and has a strong neural correlate i”
Human gaze control during real-world scene perception. (2003, background): “This issue is likely to receive increased scrutiny in the coming years [ 30 ,32,33].”
Eye guidance in natural vision: Reinterpreting salience (2011, neutral): “The neural implementations of such a priority map include the superior colliculus (McPeek & Keller, 2002), pulvinar (Robinson & Petersen, 1992), V1 (Li, 2002), V4 (Mazer & Gallant, 2003), LIP (Gottlieb, Kusunoki, & Goldberg, 1998), and the frontal eye field (Thompson & Bichot, 2005).”
Top-down and bottom-up control of visual selection. (2010, background): “Even though the dichotomy between these two stages is not as strict as originally assumed, in almost all past and present theories of visual attention this basic architecture is more or less still present (e.g., Itti & Koch, 2001; Li, 2002 ; Treue, 2003; Wolfe, 1994).”
Foundations of Vision (2018, background): “In fact, models of visual saliency propose that local differences in feature content are calculated throughout the visual field (Itti & Koch, 2000; Li, 2002), and that this information can then be used to determine what portions of the scene may contain potential objects of interest.”
How Prediction Errors Shape Perception, Attention, and Motivation (2012, background): “Under this account, salience is determined by how unexpected an input is, and not solely by bottom-up stimulus characteristics such as contrast (Li, 2002).”
Modeling Search for People in 900 Scenes: A combined source model of eye guidance (2009, neutral): “Guidance by statistically unexpected, or salient, regions of a natural image has been explored in depth in both modelling and behavioural work (e.g., Bruce & Tsotsos, 2006; Itti, Koch, & Niebur, 1998; Koch & Ullman, 1985; Li, 2002; Rosenholtz, 1999; Torralba, 2003a).”
Guided Search 6.0: An updated model of visual search (2021, background): “Bottom-up salience maps are based on something like this assumption (e.g., Bisley &Mirpour, 2019; Itti & Koch, 2000; Li, 2002) and, certainly, there are important general rules.”
State-of-the-Art in Visual Attention Modeling (2013, background): “Zhaoping Li [170], introduced a neural implementation for saliency map in V1 area that can also account for search difficulty in pop-out and conjunction search tasks.”
Space and time in visual context (2007, background): “For instance, it has been suggested that the key computation for the primary visual cortex is in fact the allocation of salience to image locations based on statistical inhomogeneities in spac”
Of Bits and Wows: A Bayesian Theory of Surprise with Applications to Attention (2009, background): “It is interesting to note that other alternatives may be more desirable, e.g., a max operation across features (Li, 2002; Zhaoping & May, 2007 ).”
Attention, Intention, and Priority in the Parietal Lobe (2010, background): “It has been suggested that earlier visual areas, such as V1 (Li 2002) and V4 (Mazer & Gallant 2003), may act as salience maps, but to be a true priority map, top-down information needs to have a strong influence on the bottom-up visual information.”
Intracranial Recordings Demonstrate Both Cortical and Medial Temporal Lobe Engagement in Visual Search in Humans (2021, background): “One model proposes that the central mechanism enabling the visual pop-out phenomenon is computation of salience in early visual cortex, most notably V1 (Zhaoping, 2002, 2019; Zhaoping & Dayan, 2006).”