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How Hard Can It Be? Estimating the Difficulty of Visual Search in an Image
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Edge Boxes: Locating Object Proposals from Edges
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Some Objects Are More Equal Than Others: Measuring and Predicting Importance
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Photo and Video Quality Evaluation: Focusing on the Subject
inferredcites →https://doi.org/10.1007/978-3-642-15561-1_33openalex-snapshot
Localizing Objects While Learning Their Appearance
inferredcites →https://doi.org/10.1007/978-3-642-23199-5_36openalex-snapshot
Estimating Image Segmentation Difficulty
inferredcites →https://doi.org/10.1007/s11263-012-0538-3openalex-snapshot
Weakly Supervised Localization and Learning with Generic Knowledge
inferredcites →https://doi.org/10.1007/s11263-014-0733-5openalex-snapshot
The Pascal Visual Object Classes Challenge: A Retrospective
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1016/j.visres.2009.11.002openalex-snapshot
Reaction time distributions constrain models of visual search
inferredcites →https://doi.org/10.1016/j.visres.2012.04.005openalex-snapshot
Turning visual search time on its head
inferredcites →https://doi.org/10.1016/s0004-3702(96)00034-3openalex-snapshot
Solving the multiple instance problem with axis-parallel rectangles
inferredcites →https://doi.org/10.1016/s0885-2014(98)90016-8openalex-snapshot
Lifespan changes in attention: The visual search task
inferredcites →https://doi.org/10.1017/cbo9780511809682openalex-snapshot
Kernel Methods for Pattern Analysis
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/10.1023/b:visi.0000022288.19776.77openalex-snapshot
Efficient Graph-Based Image Segmentation
inferredcites →https://doi.org/10.1093/acref/9780199541454.001.0001openalex-snapshot
A Dictionary of Statistics
inferredcites →https://doi.org/10.1109/34.730558openalex-snapshot
A model of saliency-based visual attention for rapid scene analysis
inferredcites →https://doi.org/10.1109/cvpr.2007.383267openalex-snapshot
Saliency Detection: A Spectral Residual Approach
inferredcites →https://doi.org/10.1109/cvpr.2009.5206705openalex-snapshot
What's it going to cost you?: Predicting effort vs. informativeness for multi-label image annotations
inferredcites →https://doi.org/10.1109/cvpr.2011.5995721openalex-snapshot
What makes an image memorable?
inferredcites →https://doi.org/10.1109/cvpr.2014.309openalex-snapshot
Multi-fold MIL Training for Weakly Supervised Object Localization
inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
inferredcites →https://doi.org/10.1109/iccv.2007.4408851openalex-snapshot
Bottom-up saliency is a discriminant process
inferredcites →https://doi.org/10.1109/iccv.2009.5459467openalex-snapshot
A framework for visual saliency detection with applications to image thumbnailing
inferredcites →https://doi.org/10.1109/iccv.2011.6126261openalex-snapshot
Weakly supervised object detector learning with model drift detection
inferredcites →https://doi.org/10.1109/iccv.2013.166openalex-snapshot
Predicting Sufficient Annotation Strength for Interactive Foreground Segmentation
inferredcites →https://doi.org/10.1109/tip.2015.2396361openalex-snapshot
Large-Scale Weakly Supervised Object Localization via Latent Category Learning
inferredcites →https://doi.org/10.1109/tpami.2009.96openalex-snapshot
TurboPixels: Fast Superpixels Using Geometric Flows
inferredcites →https://doi.org/10.1109/tpami.2012.28openalex-snapshot
Measuring the Objectness of Image Windows
inferredcites →https://doi.org/10.1109/tpami.2013.200openalex-snapshot
What Makes a Photograph Memorable?
inferredcites →https://doi.org/10.1109/tpami.2014.2377715openalex-snapshot
Fast Edge Detection Using Structured Forests
inferredcites →https://doi.org/10.1113/jphysiol.1973.sp010133openalex-snapshot
Edge detectors in human vision
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.48550/arxiv.1406.6507openalex-snapshot
Weakly-supervised Discovery of Visual Pattern Configurations
inferredcites →https://doi.org/10.48550/arxiv.1408.5093openalex-snapshot
Caffe: Convolutional Architecture for Fast Feature Embedding
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.5244/c.26.78openalex-snapshot
Transfer Learning by Ranking for Weakly Supervised Object Annotation
inferredcites →https://doi.org/10.5244/c.28.6openalex-snapshot
Return of the Devil in the Details: Delving Deep into Convolutional Nets
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Frustratingly Easy Trade-off Optimization Between Single-Stage and Two-Stage Deep Object Detectors
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Content based images retrieval based on implicit gaze annotations
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