4,021 research outputs found
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
Facial beauty prediction (FBP) is a significant visual recognition problem to
make assessment of facial attractiveness that is consistent to human
perception. To tackle this problem, various data-driven models, especially
state-of-the-art deep learning techniques, were introduced, and benchmark
dataset become one of the essential elements to achieve FBP. Previous works
have formulated the recognition of facial beauty as a specific supervised
learning problem of classification, regression or ranking, which indicates that
FBP is intrinsically a computation problem with multiple paradigms. However,
most of FBP benchmark datasets were built under specific computation
constrains, which limits the performance and flexibility of the computational
model trained on the dataset. In this paper, we argue that FBP is a
multi-paradigm computation problem, and propose a new diverse benchmark
dataset, called SCUT-FBP5500, to achieve multi-paradigm facial beauty
prediction. The SCUT-FBP5500 dataset has totally 5500 frontal faces with
diverse properties (male/female, Asian/Caucasian, ages) and diverse labels
(face landmarks, beauty scores within [1,~5], beauty score distribution), which
allows different computational models with different FBP paradigms, such as
appearance-based/shape-based facial beauty classification/regression model for
male/female of Asian/Caucasian. We evaluated the SCUT-FBP5500 dataset for FBP
using different combinations of feature and predictor, and various deep
learning methods. The results indicates the improvement of FBP and the
potential applications based on the SCUT-FBP5500.Comment: 6 pages, 14 figures, conference pape
Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs
To make machines better understand sentiments, research needs to move from
polarity identification to understanding the reasons that underlie the
expression of sentiment. Categorizing the goals or needs of humans is one way
to explain the expression of sentiment in text. Humans are good at
understanding situations described in natural language and can easily connect
them to the character's psychological needs using commonsense knowledge. We
present a novel method to extract, rank, filter and select multi-hop relation
paths from a commonsense knowledge resource to interpret the expression of
sentiment in terms of their underlying human needs. We efficiently integrate
the acquired knowledge paths in a neural model that interfaces context
representations with knowledge using a gated attention mechanism. We assess the
model's performance on a recently published dataset for categorizing human
needs. Selectively integrating knowledge paths boosts performance and
establishes a new state-of-the-art. Our model offers interpretability through
the learned attention map over commonsense knowledge paths. Human evaluation
highlights the relevance of the encoded knowledge
Listening between the Lines: Learning Personal Attributes from Conversations
Open-domain dialogue agents must be able to converse about many topics while
incorporating knowledge about the user into the conversation. In this work we
address the acquisition of such knowledge, for personalization in downstream
Web applications, by extracting personal attributes from conversations. This
problem is more challenging than the established task of information extraction
from scientific publications or Wikipedia articles, because dialogues often
give merely implicit cues about the speaker. We propose methods for inferring
personal attributes, such as profession, age or family status, from
conversations using deep learning. Specifically, we propose several Hidden
Attribute Models, which are neural networks leveraging attention mechanisms and
embeddings. Our methods are trained on a per-predicate basis to output rankings
of object values for a given subject-predicate combination (e.g., ranking the
doctor and nurse professions high when speakers talk about patients, emergency
rooms, etc). Experiments with various conversational texts including Reddit
discussions, movie scripts and a collection of crowdsourced personal dialogues
demonstrate the viability of our methods and their superior performance
compared to state-of-the-art baselines.Comment: published in WWW'1
Capturing the Visitor Profile for a Personalized Mobile Museum Experience: an Indirect Approach
An increasing number of museums and cultural institutions
around the world use personalized, mostly mobile, museum
guides to enhance visitor experiences. However since a typical
museum visit may last a few minutes and visitors might only visit
once, the personalization processes need to be quick and efficient,
ensuring the engagement of the visitor. In this paper we
investigate the use of indirect profiling methods through a visitor
quiz, in order to provide the visitor with specific museum content.
Building on our experience of a first study aimed at the design,
implementation and user testing of a short quiz version at the
Acropolis Museum, a second parallel study was devised. This
paper introduces this research, which collected and analyzed data
from two environments: the Acropolis Museum and social media
(i.e. Facebook). Key profiling issues are identified, results are
presented, and guidelines towards a generalized approach for the
profiling needs of cultural institutions are discussed
Measuring Online Social Bubbles
Social media have quickly become a prevalent channel to access information,
spread ideas, and influence opinions. However, it has been suggested that
social and algorithmic filtering may cause exposure to less diverse points of
view, and even foster polarization and misinformation. Here we explore and
validate this hypothesis quantitatively for the first time, at the collective
and individual levels, by mining three massive datasets of web traffic, search
logs, and Twitter posts. Our analysis shows that collectively, people access
information from a significantly narrower spectrum of sources through social
media and email, compared to search. The significance of this finding for
individual exposure is revealed by investigating the relationship between the
diversity of information sources experienced by users at the collective and
individual level. There is a strong correlation between collective and
individual diversity, supporting the notion that when we use social media we
find ourselves inside "social bubbles". Our results could lead to a deeper
understanding of how technology biases our exposure to new information
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