Intrinsic and extrinsic effects on image memorability
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TLDR
This work finds that intrinsic differences in memorability exist at a finer-grained scale than previously documented and proposes an information-theoretic model of image distinctiveness that can automatically predict how changes in context change the memorability of natural images.About:
This article is published in Vision Research.The article was published on 2015-11-01 and is currently open access. It has received 187 citations till now.read more
Citations
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Journal ArticleDOI
DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations
TL;DR: DeepFix as mentioned in this paper proposes a fully convolutional neural network (FCN) which models the bottom-up mechanism of visual attention via saliency prediction and predicts the saliency map in an end-to-end manner.
Journal ArticleDOI
Beyond Memorability: Visualization Recognition and Recall
Michelle A. Borkin,Zoya Bylinskii,Nam Wook Kim,Constance M. Bainbridge,Chelsea S. Yeh,Daniel Borkin,Hanspeter Pfister,Aude Oliva +7 more
TL;DR: It is shown that visualizations memorable “at-a-glance” are also capable of effectively conveying the message of the visualization, and thus, a memorable visualization is often also an effective one.
Lore Goetschalckx, Alex Andonian, Aude Oliva, Phillip Isola: GANalyze: Toward Visual Definitions of Cognitive Image Properties.
TL;DR: In this article, a framework that uses Generative Adversarial Networks (GANs) to study cognitive properties like memorability is introduced, where GANs allow to generate a manifold of natural-looking images with fine-grained differences in their visual attributes.
Proceedings ArticleDOI
GANalyze: Toward Visual Definitions of Cognitive Image Properties
TL;DR: A framework that uses Generative Adversarial Networks (GANs) to study cognitive properties like memorability is introduced and it is demonstrated that the same framework can be used to analyze image aesthetics and emotional valence.
Proceedings ArticleDOI
What Makes an Object Memorable
TL;DR: This paper augments both the images and object segmentations from the PASCAL-S dataset with ground truth memorability scores and shed light on the various factors and properties that make an object memorable (or forgettable) to humans.
References
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Proceedings Article
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Journal ArticleDOI
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Aude Oliva,Antonio Torralba +1 more
TL;DR: The performance of the spatial envelope model shows that specific information about object shape or identity is not a requirement for scene categorization and that modeling a holistic representation of the scene informs about its probable semantic category.
Posted Content
CNN Features off-the-shelf: an Astounding Baseline for Recognition
TL;DR: A series of experiments conducted for different recognition tasks using the publicly available code and model of the OverFeat network which was trained to perform object classification on ILSVRC13 suggest that features obtained from deep learning with convolutional nets should be the primary candidate in most visual recognition tasks.
Journal ArticleDOI
A process dissociation framework: Separating automatic from intentional uses of memory
TL;DR: In this article, a process dissociation procedure is proposed to separate the contributions of different types of processes to performance of a task, rather than equating processes with tasks, by separating automatic from intentional forms of processing.