1,916 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
Enhancing Perceptual Attributes with Bayesian Style Generation
Deep learning has brought an unprecedented progress in computer vision and
significant advances have been made in predicting subjective properties
inherent to visual data (e.g., memorability, aesthetic quality, evoked
emotions, etc.). Recently, some research works have even proposed deep learning
approaches to modify images such as to appropriately alter these properties.
Following this research line, this paper introduces a novel deep learning
framework for synthesizing images in order to enhance a predefined perceptual
attribute. Our approach takes as input a natural image and exploits recent
models for deep style transfer and generative adversarial networks to change
its style in order to modify a specific high-level attribute. Differently from
previous works focusing on enhancing a specific property of a visual content,
we propose a general framework and demonstrate its effectiveness in two use
cases, i.e. increasing image memorability and generating scary pictures. We
evaluate the proposed approach on publicly available benchmarks, demonstrating
its advantages over state of the art methods.Comment: ACCV-201
Defining Image Memorability using the Visual Memory Schema
Memorability of an image is a characteristic determined by the human observers’ ability to remember images they have seen. Yet recent work on image memorability defines it as an intrinsic property that can be obtained independent of the observer. The current study aims to enhance our understanding and prediction of image memorability, improving upon existing approaches by incorporating the properties of cumulative human annotations. We propose a new concept called the Visual Memory Schema (VMS) referring to an organization of image components human observers share when encoding and recognizing images. The concept of VMS is operationalised by asking human observers to define memorable regions of images they were asked to remember during an episodic memory test. We then statistically assess the consistency of VMSs across observers for either correctly or incorrectly recognised images. The associations of the VMSs with eye fixations and saliency are analysed separately as well. Lastly, we adapt various deep learning architectures for the reconstruction and prediction of memorable regions in images and analyse the results when using transfer learning at the outputs of different convolutional network layers
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