Memorability: An image-computable measure of information utility

[1]  Aude Oliva,et al.  Multimodal Memorability: Modeling Effects of Semantics and Decay on Video Memorability , 2020, ECCV.

[2]  Xucong Zhang,et al.  Towards End-to-end Video-based Eye-Tracking , 2020, ECCV.

[3]  Adrian G. Bors,et al.  Generating Memorable Images Based on Human Visual Memory Schemas , 2020, ArXiv.

[4]  Nicole C. Rust,et al.  Understanding Image Memorability , 2020, Trends in Cognitive Sciences.

[5]  Radomír Mech,et al.  Adaptive Photographic Composition Guidance , 2020, CHI.

[6]  Bolei Zhou,et al.  In-Domain GAN Inversion for Real Image Editing , 2020, ECCV.

[7]  Jayaraman J. Thiagarajan,et al.  MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking , 2019, International Journal of Computer Vision.

[8]  C. Madan Exploring word memorability: How well do different word properties explain item free-recall probability? , 2019, Psychonomic Bulletin & Review.

[9]  Steven Franconeri,et al.  Biased Average Position Estimates in Line and Bar Graphs: Underestimation, Overestimation, and Perceptual Pull , 2019, IEEE Transactions on Visualization and Computer Graphics.

[10]  Phillip Isola,et al.  On the "steerability" of generative adversarial networks , 2019, ICLR.

[11]  Erdem Akagunduz,et al.  Defining Image Memorability Using the Visual Memory Schema , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Zulin Wang,et al.  Understanding and Predicting the Memorability of Outdoor Natural Scenes , 2018, IEEE Transactions on Image Processing.

[13]  Bolei Zhou,et al.  Moments in Time Dataset: One Million Videos for Event Understanding , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Aude Oliva,et al.  Population response magnitude variation in inferotemporal cortex predicts image memorability , 2019, eLife.

[15]  Johan Wagemans,et al.  Get the Picture? Goodness of Image Organization Contributes to Image Memorability. , 2019, Journal of cognition.

[16]  Adrian G. Bors,et al.  Predicting Visual Memory Schemas with Variational Autoencoders , 2019, BMVC.

[17]  Bolei Zhou,et al.  Semantic photo manipulation with a generative image prior , 2019, ACM Trans. Graph..

[18]  Aude Oliva,et al.  GANalyze: Toward Visual Definitions of Cognitive Image Properties , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[19]  Fernando Fernández-Martínez,et al.  Predicting Image Aesthetics for Intelligent Tourism Information Systems , 2019, Electronics.

[20]  J. Wagemans,et al.  MemCat: a new category-based image set quantified on memorability , 2019, PeerJ.

[21]  Nicu Sebe,et al.  Increasing Image Memorability with Neural Style Transfer , 2019, ACM Trans. Multim. Comput. Commun. Appl..

[22]  Radomír Mech,et al.  SmartEye: Assisting Instant Photo Taking via Integrating User Preference with Deep View Proposal Network , 2019, CHI.

[23]  Karrie Karahalios,et al.  Trust and Recall of Information across Varying Degrees of Title-Visualization Misalignment , 2019, CHI.

[24]  P. Pérez,et al.  SoDeep: A Sorting Deep Net to Learn Ranking Loss Surrogates , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  Peter Wonka,et al.  Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space? , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[26]  Martin Engilberge,et al.  VideoMem: Constructing, Analyzing, Predicting Short-Term and Long-Term Video Memorability , 2018, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[27]  Oleksii Sidorov,et al.  Changing the Image Memorability: From Basic Photo Editing to GANs , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[28]  Anil A. Bharath,et al.  Inverting the Generator of a Generative Adversarial Network , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[29]  A. Oliva,et al.  Effects of title wording on memory of trends in line graphs , 2018, Journal of Vision.

[30]  Zulin Wang,et al.  What Makes Natural Scene Memorable? , 2018, EE-USAD'18.

[31]  Jing Qian,et al.  Remotion: A Motion-Based Capture and Replay Platform of Mobile Device Interaction for Remote Usability Testing , 2018, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[32]  Claire-Hélène Demarty,et al.  Annotating, Understanding, and Predicting Long-term Video Memorability , 2018, ICMR.

[33]  Bolei Zhou,et al.  Places: A 10 Million Image Database for Scene Recognition , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Johan Wagemans,et al.  Image memorability across longer time intervals , 2018, Memory.

[35]  Karrie Karahalios,et al.  Frames and Slants in Titles of Visualizations on Controversial Topics , 2018, CHI.

[36]  Paolo Remagnino,et al.  AMNet: Memorability Estimation with Attention , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[37]  Vincent Aleven,et al.  The classroom as a dashboard: co-designing wearable cognitive augmentation for K-12 teachers , 2018, LAK.

[38]  A. Oliva,et al.  Memorable words are monogamous: The role of synonymy and homonymy in word recognition memory , 2018 .

[39]  Wen Yi,et al.  A critical review of virtual and augmented reality (VR/AR) applications in construction safety , 2018 .

[40]  Filip Děchtěrenko,et al.  Visual properties and memorising scenes: Effects of image-space sparseness and uniformity , 2017, Attention, perception & psychophysics.

[41]  Sumit Shekhar,et al.  Show and Recall: Learning What Makes Videos Memorable , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).

[42]  Andrew Zisserman,et al.  Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[43]  Mehdi Rezaeian,et al.  Image memorability prediction using deep features , 2017, 2017 Iranian Conference on Electrical Engineering (ICEE).

[44]  Liqiang Nie,et al.  Predicting Image Memorability Through Adaptive Transfer Learning From External Sources , 2017, IEEE Transactions on Multimedia.

[45]  Bolei Zhou,et al.  Network Dissection: Quantifying Interpretability of Deep Visual Representations , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[46]  Nicu Sebe,et al.  How to Make an Image More Memorable?: A Deep Style Transfer Approach , 2017, ICMR.

[47]  Aude Oliva,et al.  Image Memorability in the Eye of the Beholder: Tracking the Decay of Visual Scene Representations , 2013, bioRxiv.

[48]  Patrick Le Callet,et al.  Deep Learning for Image Memorability Prediction: the Emotional Bias , 2016, ACM Multimedia.

[49]  James Hays,et al.  WebGazer: Scalable Webcam Eye Tracking Using User Interactions , 2016, IJCAI.

[50]  Yi-Ju Lee,et al.  Creative experiences, memorability and revisit intention in creative tourism , 2016 .

[51]  Wojciech Matusik,et al.  Eye Tracking for Everyone , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[52]  M. Schijven,et al.  Systematic review on the effectiveness of augmented reality applications in medical training , 2016, Surgical Endoscopy.

[53]  Hanspeter Pfister,et al.  Beyond Memorability: Visualization Recognition and Recall , 2016, IEEE Transactions on Visualization and Computer Graphics.

[54]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[55]  Antonio Torralba,et al.  Understanding and Predicting Image Memorability at a Large Scale , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[56]  A. Torralba,et al.  Intrinsic and extrinsic effects on image memorability , 2015, Vision Research.

[57]  ErdemAykut,et al.  Predicting memorability of images using attention-driven spatial pooling and image semantics , 2015 .

[58]  Aykut Erdem,et al.  Predicting memorability of images using attention-driven spatial pooling and image semantics , 2015, Image Vis. Comput..

[59]  Ling Shao,et al.  Learning Computational Models of Video Memorability from fMRI Brain Imaging , 2015, IEEE Transactions on Cybernetics.

[60]  Alexandra Papoutsaki,et al.  Scalable Webcam Eye Tracking by Learning from User Interactions , 2015, CHI Extended Abstracts.

[61]  Mario Fritz,et al.  Appearance-based gaze estimation in the wild , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[62]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[63]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[64]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[65]  Bolei Zhou,et al.  Learning Deep Features for Scene Recognition using Places Database , 2014, NIPS.

[66]  Jianxiong Xiao,et al.  What Makes a Photograph Memorable? , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[67]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.

[68]  James M. Rehg,et al.  The Secrets of Salient Object Segmentation , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[69]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[70]  Raffay Hamid,et al.  What makes an image popular? , 2014, WWW.

[71]  Stefan Carlsson,et al.  CNN Features Off-the-Shelf: An Astounding Baseline for Recognition , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[72]  Joseph L. Gabbard,et al.  Behind the Glass: Driver Challenges and Opportunities for AR Automotive Applications , 2014, Proceedings of the IEEE.

[73]  Hanspeter Pfister,et al.  What Makes a Visualization Memorable? , 2013, IEEE Transactions on Visualization and Computer Graphics.

[74]  Luc Van Gool,et al.  The Interestingness of Images , 2013, 2013 IEEE International Conference on Computer Vision.

[75]  Antonio Torralba,et al.  Modifying the Memorability of Face Photographs , 2013, 2013 IEEE International Conference on Computer Vision.

[76]  Wilma A. Bainbridge,et al.  The intrinsic memorability of face photographs. , 2013, Journal of experimental psychology. General.

[77]  Vladimir Pavlovic,et al.  Relative spatial features for image memorability , 2013, ACM Multimedia.

[78]  Matei Mancas,et al.  Memorability of natural scenes: The role of attention , 2013, 2013 IEEE International Conference on Image Processing.

[79]  Ali Farhadi,et al.  Object-Centric Anomaly Detection by Attribute-Based Reasoning , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[80]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[81]  Jianxiong Xiao,et al.  Memorability of Image Regions , 2012, NIPS.

[82]  Jianxiong Xiao,et al.  Image memorability and visual inception , 2012, SIGGRAPH Asia Technical Briefs.

[83]  Naila Murray,et al.  AVA: A large-scale database for aesthetic visual analysis , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[84]  Aude Oliva,et al.  Establishing a Database for Studying Human Face Photograph Memory , 2012, CogSci.

[85]  Antonio Torralba,et al.  Understanding the Intrinsic Memorability of Images , 2011, NIPS.

[86]  George A. Alvarez,et al.  Are real-world objects represented as bound units? Independent decay of object details from short-term to long-term memory , 2011 .

[87]  Jianxiong Xiao,et al.  What makes an image memorable? , 2011, CVPR 2011.

[88]  Allan Hanbury,et al.  Affective image classification using features inspired by psychology and art theory , 2010, ACM Multimedia.

[89]  Timothy F. Brady,et al.  Scene Memory Is More Detailed Than You Think : The Role of Categories in Visual Long-Term Memory , 2010 .

[90]  Harish Katti,et al.  An Eye Fixation Database for Saliency Detection in Images , 2010, ECCV.

[91]  Timothy F. Brady,et al.  Conceptual Distinctiveness Supports Detailed Visual Long-term Memory for Real-world Objects the Fidelity of Long-term Memory for Visual Information , 2022 .

[92]  Krista A. Ehinger,et al.  SUN database: Large-scale scene recognition from abbey to zoo , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[93]  Yihong Gong,et al.  Locality-constrained Linear Coding for image classification , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[94]  Frédo Durand,et al.  Learning to predict where humans look , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[95]  Fei-Fei Li,et al.  ImageNet: A large-scale hierarchical image database , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[96]  Ali Farhadi,et al.  Describing objects by their attributes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[97]  Mark J. Huiskes,et al.  The MIR flickr retrieval evaluation , 2008, MIR '08.

[98]  Aude Oliva,et al.  Visual long-term memory has a massive storage capacity for object details , 2008, Proceedings of the National Academy of Sciences.

[99]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[100]  Antonio Torralba,et al.  LabelMe: A Database and Web-Based Tool for Image Annotation , 2008, International Journal of Computer Vision.

[101]  J. Wolfe,et al.  Is visual attention required for robust picture memory? , 2007, Vision Research.

[102]  Cordelia Schmid,et al.  Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[103]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[104]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[105]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[106]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[107]  R. Adolphs,et al.  2. The role of the human amygdala in emotional modulation of long-term declarative memory , 2002 .

[108]  J. Henderson,et al.  Accurate visual memory for previously attended objects in natural scenes , 2002 .

[109]  Carrick C. Williams,et al.  To see and remember: Visually specific information is retained in memory from previously attended objects in natural scenes , 2001, Psychonomic bulletin & review.

[110]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[111]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[112]  J. D. McGaugh,et al.  A Novel Demonstration of Enhanced Memory Associated with Emotional Arousal , 1995, Consciousness and Cognition.

[113]  George A. Miller,et al.  WordNet: A Lexical Database for English , 1995, HLT.

[114]  M. Bradley,et al.  Remembering pictures: pleasure and arousal in memory. , 1992, Journal of experimental psychology. Learning, memory, and cognition.

[115]  L. Standing Learning 10000 pictures , 1973 .

[116]  C. Spearman CORRELATION CALCULATED FROM FAULTY DATA , 1910 .

[117]  W. Brown SOME EXPERIMENTAL RESULTS IN THE CORRELATION OF MENTAL ABILITIES1 , 1910 .