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Humans, but Not Deep Neural Networks, Often Miss Giant Targets in Scenes
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-319-10602-1_48openalex-snapshot
Microsoft COCO: Common Objects in Context
inferredcites →https://doi.org/10.1007/bf03181556openalex-snapshot
A change of the leading player in flow Visualization technique
inferredcites →https://doi.org/10.1007/bf03181786openalex-snapshot
The 10th anniversary of journal of visualization
inferredcites →https://doi.org/10.1016/0010-0285(82)90007-xopenalex-snapshot
Scene perception: Detecting and judging objects undergoing relational violations
inferredcites →https://doi.org/10.1016/j.beproc.2014.01.021openalex-snapshot
Pigeons exhibit contextual cueing to both simple and complex backgrounds
inferredcites →https://doi.org/10.1016/j.cogpsych.2008.06.001openalex-snapshot
Recognition of natural scenes from global properties: Seeing the forest without representing the trees
inferredcites →https://doi.org/10.1016/j.neuron.2012.04.036openalex-snapshot
A Real-World Size Organization of Object Responses in Occipitotemporal Cortex
inferredcites →https://doi.org/10.1016/j.tics.2010.12.001openalex-snapshot
Visual search in scenes involves selective and nonselective pathways
inferredcites →https://doi.org/10.1016/j.tics.2014.02.004openalex-snapshot
Attention in the real world: toward understanding its neural basis
inferredcites →https://doi.org/10.1016/j.visres.2005.08.025openalex-snapshot
Scene context guides eye movements during visual search
inferredcites →https://doi.org/10.1016/j.visres.2008.12.008openalex-snapshot
Statistical decision theory to relate neurons to behavior in the study of covert visual attention
inferredcites →https://doi.org/10.1016/j.visres.2012.12.011openalex-snapshot
Rethinking human visual attention: Spatial cueing effects and optimality of decisions by honeybees, monkeys and humans
inferredcites →https://doi.org/10.1016/j.visres.2015.05.016openalex-snapshot
Optimal and human eye movements to clustered low value cues to increase decision rewards during search
inferredcites →https://doi.org/10.1016/s0042-6989(96)00218-0openalex-snapshot
Saccade Target Selection During Visual Search
inferredcites →https://doi.org/10.1023/a:1011139631724openalex-snapshot
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
inferredcites →https://doi.org/10.1037/0033-295x.113.4.766openalex-snapshot
Contextual guidance of eye movements and attention in real-world scenes: The role of global features in object search.
inferredcites →https://doi.org/10.1037/0096-1523.31.6.1416openalex-snapshot
Task Demands Control Acquisition and Storage of Visual Information.
inferredcites →https://doi.org/10.1037/0096-1523.33.4.753openalex-snapshot
Initial scene representations facilitate eye movement guidance in visual search.
inferredcites →https://doi.org/10.1037/0096-1523.33.4.816openalex-snapshot
Does contextual cuing guide the deployment of attention?
inferredcites →https://doi.org/10.1037/a0037524openalex-snapshot
Peripheral guidance in scenes: The interaction of scene context and object content.
inferredcites →https://doi.org/10.1037/xhp0000363openalex-snapshot
Beyond scene gist: Objects guide search more than scene background.
inferredcites →https://doi.org/10.1038/nature03390openalex-snapshot
Optimal eye movement strategies in visual search
inferredcites →https://doi.org/10.1038/nrn1411openalex-snapshot
What attributes guide the deployment of visual attention and how do they do it?
inferredcites →https://doi.org/10.1038/s41562-017-0058openalex-snapshot
Five factors that guide attention in visual search
inferredcites →https://doi.org/10.1068/p3128openalex-snapshot
Quantifying the Performance Limits of Human Saccadic Targeting during Visual Search
inferredcites →https://doi.org/10.1073/pnas.0911972107openalex-snapshot
Optimal rewarding harvesting in complex visual environments
inferredcites →https://doi.org/10.1073/pnas.1101042108openalex-snapshot
A neural basis for real-world visual search in human occipitotemporal cortex
inferredcites →https://doi.org/10.1080/13506280902771138openalex-snapshot
SUN: Top-down saliency using natural statistics
inferredcites →https://doi.org/10.1109/cvpr.2017.351openalex-snapshot
Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors
inferredcites →https://doi.org/10.1109/cvpr.2017.690openalex-snapshot
YOLO9000: Better, Faster, Stronger
inferredcites →https://doi.org/10.1109/tpami.2011.119openalex-snapshot
A Tree-Based Context Model for Object Recognition
inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
inferredcites →https://doi.org/10.1111/j.1467-9280.1997.tb00427.xopenalex-snapshot
To See or not to See: The Need for Attention to Perceive Changes in Scenes
inferredcites →https://doi.org/10.1111/j.1467-9280.2006.01815.xopenalex-snapshot
Attentional Cues in Real Scenes, Saccadic Targeting, and Bayesian Priors
inferredcites →https://doi.org/10.1146/annurev-vision-102016-061220openalex-snapshot
Probabilistic Computations for Attention, Eye Movements, and Search
inferredcites →https://doi.org/10.1146/annurev.ento.010908.164537openalex-snapshot
Honey Bees as a Model for Vision, Perception, and Cognition
inferredcites →https://doi.org/10.1167/10.2.4openalex-snapshot
Combining top-down processes to guide eye movements during real-world scene search
inferredcites →https://doi.org/10.1167/10.3.14openalex-snapshot
The time course of initial scene processing for eye movement guidance in natural scene search
inferredcites →https://doi.org/10.1167/11.11.1334openalex-snapshot
Depth and Size Information Reduce Effective Set Size for Visual Search in Real-World Scenes
inferredcites →https://doi.org/10.1167/11.9.9openalex-snapshot
Object co-occurrence serves as a contextual cue to guide and facilitate visual search in a natural viewing environment
inferredcites →https://doi.org/10.1167/12.13.19openalex-snapshot
The role of uncertainty and reward on eye movements in a virtual driving task
inferredcites →https://doi.org/10.1167/13.3.27openalex-snapshot
Choice of saccade endpoint under risk
inferredcites →https://doi.org/10.1167/17.2.16openalex-snapshot
Temporal and peripheral extraction of contextual cues from scenes during visual search
inferredcites →https://doi.org/10.1167/9.1.34openalex-snapshot
The specificity of the search template
inferredcites →https://doi.org/10.1167/9.10.6openalex-snapshot
The contributions of central versus peripheral vision to scene gist recognition
inferredcites →https://doi.org/10.1167/9.11.8openalex-snapshot
The effects of target template specificity on visual search in real-world scenes: Evidence from eye movements
inferredcites →https://doi.org/10.1167/9.2.18openalex-snapshot
Learning cue validity through performance feedback
inferredcites →https://doi.org/10.1523/jneurosci.5840-12.2013openalex-snapshot
Neural Representations of Contextual Guidance in Visual Search of Real-World Scenes
inferredcites →https://doi.org/10.3758/app.72.5.1283openalex-snapshot
The relative contribution of scene context and target features to visual search in scenes
inferredcites →https://doi.org/10.3758/bf03194447openalex-snapshot
Driving attention with the top down: The relative contribution of target templates to the linear separability effect in the size dimension
inferredcites →https://doi.org/10.3758/bf03194537openalex-snapshot
Attentional guidance of the eyes by contextual information and abrupt onsets.
inferredcites →https://doi.org/10.3758/bf03197524openalex-snapshot
The effects of contextual scenes on the identification of objects
inferredcites →https://doi.org/10.3758/s13414-011-0153-3openalex-snapshot
Visual search for arbitrary objects in real scenes
inferredcites →https://doi.org/10.3758/s13423-011-0107-8openalex-snapshot
Scene context influences without scene gist: Eye movements guided by spatial associations in visual search
inferredcites →https://doi.org/10.48550/arxiv.1605.06409openalex-snapshot
R-FCN: Object Detection via Region-based Fully Convolutional Networks
inferredcites →https://doi.org/10.7490/f1000research.1270.1openalex-snapshot
Depth and size information reduce effective set size for visual search in real-world scenes
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Manipulating and Measuring Variation In Deep Neural Network (DNN) Representations of Objects
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The neural time course of size constancy in natural scenes
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Visual Attention: Size Matters
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Under-exploration of Three-Dimensional Images Leads to Search Errors for Small Salient Targets
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Local features and global shape information in object classification by deep convolutional neural networks
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The meaning and structure of scenes
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Searching near and far: The attentional template incorporates viewing distance.
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Qualitative similarities and differences in visual object representations between brains and deep networks
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Eye movements during free viewing to maximize scene understanding
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Attention scales according to inferred real-world object size
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The transverse occipital sulcus and intraparietal sulcus show neural selectivity to object-scene size relationships
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Predictive processing of scenes and objects
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Event knowledge and object-scene knowledge jointly influence fixations in scenes
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Noise-robust recognition of objects by humans and deep neural networks
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Solving Bongard Problems With a Visual Language and Pragmatic Constraints
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An image-computable model of speeded decision-making
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An image-computable model of speeded decision-making
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