462 research outputs found
How to Make an Image More Memorable? A Deep Style Transfer Approach
Recent works have shown that it is possible to automatically predict
intrinsic image properties like memorability. In this paper, we take a step
forward addressing the question: "Can we make an image more memorable?".
Methods for automatically increasing image memorability would have an impact in
many application fields like education, gaming or advertising. Our work is
inspired by the popular editing-by-applying-filters paradigm adopted in photo
editing applications, like Instagram and Prisma. In this context, the problem
of increasing image memorability maps to that of retrieving "memorabilizing"
filters or style "seeds". Still, users generally have to go through most of the
available filters before finding the desired solution, thus turning the editing
process into a resource and time consuming task. In this work, we show that it
is possible to automatically retrieve the best style seeds for a given image,
thus remarkably reducing the number of human attempts needed to find a good
match. Our approach leverages from recent advances in the field of image
synthesis and adopts a deep architecture for generating a memorable picture
from a given input image and a style seed. Importantly, to automatically select
the best style a novel learning-based solution, also relying on deep models, is
proposed. Our experimental evaluation, conducted on publicly available
benchmarks, demonstrates the effectiveness of the proposed approach for
generating memorable images through automatic style seed selectionComment: Accepted at ACM ICMR 201
Understanding and Predicting Image Memorability at a Large Scale
Progress in estimating visual memorability has been limited by the small scale and lack of variety of benchmark data. Here, we introduce a novel experimental procedure to objectively measure human memory, allowing us to build LaMem, the largest annotated image memorability dataset to date (containing 60,000 images from diverse sources). Using Convolutional Neural Networks (CNNs), we show that fine-tuned deep features outperform all other features by a large margin, reaching a rank correlation of 0.64, near human consistency (0.68). Analysis of the responses of the high-level CNN layers shows which objects and regions are positively, and negatively, correlated with memorability, allowing us to create memorability maps for each image and provide a concrete method to perform image memorability manipulation. This work demonstrates that one can now robustly estimate the memorability of images from many different classes, positioning memorability and deep memorability features as prime candidates to estimate the utility of information for cognitive systems. Our model and data are available at: http://memorability.csail.mit.edu.National Science Foundation (U.S.) (Grant 1532591)McGovern Institute for Brain Research at MIT. Neurotechnology (MINT) ProgramMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory. MIT Big Data InitiativeGoogle (Firm)Xerox Corporatio
Viraliency: Pooling Local Virality
In our overly-connected world, the automatic recognition of virality - the
quality of an image or video to be rapidly and widely spread in social networks
- is of crucial importance, and has recently awaken the interest of the
computer vision community. Concurrently, recent progress in deep learning
architectures showed that global pooling strategies allow the extraction of
activation maps, which highlight the parts of the image most likely to contain
instances of a certain class. We extend this concept by introducing a pooling
layer that learns the size of the support area to be averaged: the learned
top-N average (LENA) pooling. We hypothesize that the latent concepts (feature
maps) describing virality may require such a rich pooling strategy. We assess
the effectiveness of the LENA layer by appending it on top of a convolutional
siamese architecture and evaluate its performance on the task of predicting and
localizing virality. We report experiments on two publicly available datasets
annotated for virality and show that our method outperforms state-of-the-art
approaches.Comment: Accepted at IEEE CVPR 201
Evaluating Content-centric vs User-centric Ad Affect Recognition
Despite the fact that advertisements (ads) often include strongly emotional
content, very little work has been devoted to affect recognition (AR) from ads.
This work explicitly compares content-centric and user-centric ad AR
methodologies, and evaluates the impact of enhanced AR on computational
advertising via a user study. Specifically, we (1) compile an affective ad
dataset capable of evoking coherent emotions across users; (2) explore the
efficacy of content-centric convolutional neural network (CNN) features for
encoding emotions, and show that CNN features outperform low-level emotion
descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram
(EEG) responses acquired from eleven viewers, and find that EEG signals encode
emotional information better than content descriptors; (4) investigate the
relationship between objective AR and subjective viewer experience while
watching an ad-embedded online video stream based on a study involving 12
users. To our knowledge, this is the first work to (a) expressly compare user
vs content-centered AR for ads, and (b) study the relationship between modeling
of ad emotions and its impact on a real-life advertising application.Comment: Accepted at the ACM International Conference on Multimodal Interation
(ICMI) 201
Is Image Memorability Prediction Solved?
This paper deals with the prediction of the memorability of a given image. We
start by proposing an algorithm that reaches human-level performance on the
LaMem dataset - the only large scale benchmark for memorability prediction. The
suggested algorithm is based on three observations we make regarding
convolutional neural networks (CNNs) that affect memorability prediction.
Having reached human-level performance we were humbled, and asked ourselves
whether indeed we have resolved memorability prediction - and answered this
question in the negative. We studied a few factors and made some
recommendations that should be taken into account when designing the next
benchmark
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