29 research outputs found
Fashion Conversation Data on Instagram
The fashion industry is establishing its presence on a number of
visual-centric social media like Instagram. This creates an interesting clash
as fashion brands that have traditionally practiced highly creative and
editorialized image marketing now have to engage with people on the platform
that epitomizes impromptu, realtime conversation. What kinds of fashion images
do brands and individuals share and what are the types of visual features that
attract likes and comments? In this research, we take both quantitative and
qualitative approaches to answer these questions. We analyze visual features of
fashion posts first via manual tagging and then via training on convolutional
neural networks. The classified images were examined across four types of
fashion brands: mega couture, small couture, designers, and high street. We
find that while product-only images make up the majority of fashion
conversation in terms of volume, body snaps and face images that portray
fashion items more naturally tend to receive a larger number of likes and
comments by the audience. Our findings bring insights into building an
automated tool for classifying or generating influential fashion information.
We make our novel dataset of {24,752} labeled images on fashion conversations,
containing visual and textual cues, available for the research community.Comment: 10 pages, 6 figures, This paper will be presented at ICWSM'1
FairGRAPE: Fairness-aware GRAdient Pruning mEthod for Face Attribute Classification
Existing pruning techniques preserve deep neural networks' overall ability to
make correct predictions but may also amplify hidden biases during the
compression process. We propose a novel pruning method, Fairness-aware GRAdient
Pruning mEthod (FairGRAPE), that minimizes the disproportionate impacts of
pruning on different sub-groups. Our method calculates the per-group importance
of each model weight and selects a subset of weights that maintain the relative
between-group total importance in pruning. The proposed method then prunes
network edges with small importance values and repeats the procedure by
updating importance values. We demonstrate the effectiveness of our method on
four different datasets, FairFace, UTKFace, CelebA, and ImageNet, for the tasks
of face attribute classification where our method reduces the disparity in
performance degradation by up to 90% compared to the state-of-the-art pruning
algorithms. Our method is substantially more effective in a setting with a high
pruning rate (99%). The code and dataset used in the experiments are available
at https://github.com/Bernardo1998/FairGRAPEComment: To appear in ECCV 202
Cultural Diffusion and Trends in Facebook Photographs
Online social media is a social vehicle in which people share various moments
of their lives with their friends, such as playing sports, cooking dinner or
just taking a selfie for fun, via visual means, that is, photographs. Our study
takes a closer look at the popular visual concepts illustrating various
cultural lifestyles from aggregated, de-identified photographs. We perform
analysis both at macroscopic and microscopic levels, to gain novel insights
about global and local visual trends as well as the dynamics of interpersonal
cultural exchange and diffusion among Facebook friends. We processed images by
automatically classifying the visual content by a convolutional neural network
(CNN). Through various statistical tests, we find that socially tied
individuals more likely post images showing similar cultural lifestyles. To
further identify the main cause of the observed social correlation, we use the
Shuffle test and the Preference-based Matched Estimation (PME) test to
distinguish the effects of influence and homophily. The results indicate that
the visual content of each user's photographs are temporally, although not
necessarily causally, correlated with the photographs of their friends, which
may suggest the effect of influence. Our paper demonstrates that Facebook
photographs exhibit diverse cultural lifestyles and preferences and that the
social interaction mediated through the visual channel in social media can be
an effective mechanism for cultural diffusion.Comment: 10 pages, To appear in ICWSM 2017 (Full Paper
A Fully Implicit Method for Robust Frictional Contact Handling in Elastic Rods
Accurate frictional contact is critical in simulating the assembly of
rod-like structures in the practical world, such as knots, hairs, flagella, and
more. Due to their high geometric nonlinearity and elasticity, rod-on-rod
contact remains a challenging problem tackled by researchers in both
computational mechanics and computer graphics. Typically, frictional contact is
regarded as constraints for the equations of motions of a system. Such
constraints are often computed independently at every time step in a dynamic
simulation, thus slowing down the simulation and possibly introducing numerical
convergence issues. This paper proposes a fully implicit penalty-based
frictional contact method, Implicit Contact Model (IMC), that efficiently and
robustly captures accurate frictional contact responses. We showcase our
algorithm's performance in achieving visually realistic results for the
challenging and novel contact scenario of flagella bundling in fluid medium, a
significant phenomenon in biology that motivates novel engineering applications
in soft robotics. In addition to this, we offer a side-by-side comparison with
Incremental Potential Contact (IPC), a state-of-the-art contact handling
algorithm. We show that IMC possesses comparable performance to IPC while
converging at a faster rate.Comment: * Equal contribution. A video summarizing this work is available on
YouTube: https://youtu.be/g0rlCFfWJ8
Visual Persuasion: Inferring Communicative Intents of Images
In this paper we introduce the novel problem of under-standing visual persuasion. Modern mass media make ex-tensive use of images to persuade people to make commer-cial and political decisions. These effects and techniques are widely studied in the social sciences, but behavioral studies do not scale to massive datasets. Computer vision has made great strides in building syntactical representa-tions of images, such as detection and identification of ob-jects. However, the pervasive use of images for commu-nicative purposes has been largely ignored. We extend the significant advances in syntactic analysis in computer vi-sion to the higher-level challenge of understanding the un-derlying communicative intent implied in images. We be-gin by identifying nine dimensions of persuasive intent la-tent in images of politicians, such as “socially dominant,” “energetic, ” and “trustworthy, ” and propose a hierarchical model that builds on the layer of syntactical attributes, such as “smile ” and “waving hand, ” to predict the intents pre-sented in the images. To facilitate progress, we introduce a new dataset of 1,124 images of politicians labeled with ground-truth intents in the form of rankings. This study demonstrates that a systematic focus on visual persuasion opens up the field of computer vision to a new class of inves-tigations around mediated images, intersecting with media analysis, psychology, and political communication. 1