ReviewVision (Basel, Switzerland)2019
Salience Models: A Computational Cognitive Neuroscience Review.
Review in Vision (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Dynamic salience as a basis for behavioural meta-malleability.Royal Society open science · 2026Review
- Perception of motion salience shapes the emergence of collective motions.Nature communications · 2024Article
- The Gaze of Schizophrenia Patients Captured by Bottom-up Saliency.Schizophrenia (Heidelberg, Germany) · 2024Article
- The Oddity Detection in Diverse Scenes (ODDS) database: Validated real-world scenes for studying anomaly detection.Behavior research methods · 2023Article
- Between the Scenes.Experimental psychology · 2022Article
- Semantic object-scene inconsistencies affect eye movements, but not in the way predicted by contextualized meaning maps.Journal of vision · 2022Article
- An improved saliency model of visual attention dependent on image content.Frontiers in human neuroscience · 2022Article
- Glimpse: A Gaze-Based Measure of Temporal Salience.Sensors (Basel, Switzerland) · 2021Article
- No Advantage for Separating Overt and Covert Attention in Visual Search.Vision (Basel, Switzerland) · 2020Article
- Temporal Limitations of the Standard Leaky Integrate and Fire Model.Brain sciences · 2019Article
- What Neuroscientific Studies Tell Us about Inhibition of Return.Vision (Basel, Switzerland) · 2019Review
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Authors and funding
2 authors.
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Abstract
The seminal model by Laurent Itti and Cristoph Koch demonstrated that we can compute the entire flow of visual processing from input to resulting fixations. Despite many replications and follow-ups, few have matched the impact of the original model-so what made this model so groundbreaking? We have selected five key contributions that distinguish the original salience model by Itti and Koch; namely, its contribution to our theoretical, neural, and computational understanding of visual processing, as well as the spatial and temporal predictions for fixation distributions. During the last 20 years, advances in the field have brought up various techniques and approaches to salience modelling, many of which tried to improve or add to the initial Itti and Koch model. One of the most recent trends has been to adopt the computational power of deep learning neural networks; however, this has also shifted their primary focus to spatial classification. We present a review of recent approaches to modelling salience, starting from direct variations of the Itti and Koch salience model to sophisticated deep-learning architectures, and discuss the models from the point of view of their contribution to computational cognitive neuroscience.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.