382 research outputs found
Face Alignment Assisted by Head Pose Estimation
In this paper we propose a supervised initialization scheme for cascaded face
alignment based on explicit head pose estimation. We first investigate the
failure cases of most state of the art face alignment approaches and observe
that these failures often share one common global property, i.e. the head pose
variation is usually large. Inspired by this, we propose a deep convolutional
network model for reliable and accurate head pose estimation. Instead of using
a mean face shape, or randomly selected shapes for cascaded face alignment
initialisation, we propose two schemes for generating initialisation: the first
one relies on projecting a mean 3D face shape (represented by 3D facial
landmarks) onto 2D image under the estimated head pose; the second one searches
nearest neighbour shapes from the training set according to head pose distance.
By doing so, the initialisation gets closer to the actual shape, which enhances
the possibility of convergence and in turn improves the face alignment
performance. We demonstrate the proposed method on the benchmark 300W dataset
and show very competitive performance in both head pose estimation and face
alignment.Comment: Accepted by BMVC201
Answer Ranking for Product-Related Questions via Multiple Semantic Relations Modeling
Many E-commerce sites now offer product-specific question answering platforms
for users to communicate with each other by posting and answering questions
during online shopping. However, the multiple answers provided by ordinary
users usually vary diversely in their qualities and thus need to be
appropriately ranked for each question to improve user satisfaction. It can be
observed that product reviews usually provide useful information for a given
question, and thus can assist the ranking process. In this paper, we
investigate the answer ranking problem for product-related questions, with the
relevant reviews treated as auxiliary information that can be exploited for
facilitating the ranking. We propose an answer ranking model named MUSE which
carefully models multiple semantic relations among the question, answers, and
relevant reviews. Specifically, MUSE constructs a multi-semantic relation graph
with the question, each answer, and each review snippet as nodes. Then a
customized graph convolutional neural network is designed for explicitly
modeling the semantic relevance between the question and answers, the content
consistency among answers, and the textual entailment between answers and
reviews. Extensive experiments on real-world E-commerce datasets across three
product categories show that our proposed model achieves superior performance
on the concerned answer ranking task.Comment: Accepted by SIGIR 202
Product Question Answering in E-Commerce: A Survey
Product question answering (PQA), aiming to automatically provide instant
responses to customer's questions in E-Commerce platforms, has drawn increasing
attention in recent years. Compared with typical QA problems, PQA exhibits
unique challenges such as the subjectivity and reliability of user-generated
contents in E-commerce platforms. Therefore, various problem settings and novel
methods have been proposed to capture these special characteristics. In this
paper, we aim to systematically review existing research efforts on PQA.
Specifically, we categorize PQA studies into four problem settings in terms of
the form of provided answers. We analyze the pros and cons, as well as present
existing datasets and evaluation protocols for each setting. We further
summarize the most significant challenges that characterize PQA from general QA
applications and discuss their corresponding solutions. Finally, we conclude
this paper by providing the prospect on several future directions
Social Media Fashion Knowledge Extraction as Captioning
Social media plays a significant role in boosting the fashion industry, where
a massive amount of fashion-related posts are generated every day. In order to
obtain the rich fashion information from the posts, we study the task of social
media fashion knowledge extraction. Fashion knowledge, which typically consists
of the occasion, person attributes, and fashion item information, can be
effectively represented as a set of tuples. Most previous studies on fashion
knowledge extraction are based on the fashion product images without
considering the rich text information in social media posts. Existing work on
fashion knowledge extraction in social media is classification-based and
requires to manually determine a set of fashion knowledge categories in
advance. In our work, we propose to cast the task as a captioning problem to
capture the interplay of the multimodal post information. Specifically, we
transform the fashion knowledge tuples into a natural language caption with a
sentence transformation method. Our framework then aims to generate the
sentence-based fashion knowledge directly from the social media post. Inspired
by the big success of pre-trained models, we build our model based on a
multimodal pre-trained generative model and design several auxiliary tasks for
enhancing the knowledge extraction. Since there is no existing dataset which
can be directly borrowed to our task, we introduce a dataset consisting of
social media posts with manual fashion knowledge annotation. Extensive
experiments are conducted to demonstrate the effectiveness of our model.Comment: Accepted by SIGIR-AP 202
Evaluating the performance of Chinese commercial banks:A comparative analysis of different types of banks
This paper examines the cost and profit efficiency of four types of Chinese commercial banks over the period from 2002 to 2013. We find that the cost and profit efficiencies improved across all types of Chinese domestic banks in general and the banks are more profit-efficient than cost efficient. Foreign banks are the most cost efficient but the least profit efficient. The profit efficiency gap between foreign banks and domestic banks has widened after the World Trade Organization transition period (2007–2013). Ownership structure, market competition, bank size, and listing status are the main determinants of the efficiency of Chinese banks. We also find a causal relationship between efficiency and SROE by using the panel auto regression method. The evidence from the shadow return on equity (SROE) suggests that policy makers should be cautious of the adjustment costs imposed by the recapitalization process, which offsets the efficiency gains
Parameter-Efficient Tuning with Special Token Adaptation
Parameter-efficient tuning aims at updating only a small subset of parameters
when adapting a pretrained model to downstream tasks. In this work, we
introduce PASTA, in which we only modify the special token representations
(e.g., [SEP] and [CLS] in BERT) before the self-attention module at each layer
in Transformer-based models. PASTA achieves comparable performance to
fine-tuning in natural language understanding tasks including text
classification and NER with up to only 0.029% of total parameters trained. Our
work not only provides a simple yet effective way of parameter-efficient
tuning, which has a wide range of practical applications when deploying
finetuned models for multiple tasks, but also demonstrates the pivotal role of
special tokens in pretrained language models
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Bayesian Functional Data Clustering for Temporal Microarray Data
We propose a Bayesian procedure to cluster temporal gene expression microarray profiles,
based on a mixed-effect smoothing-spline model, and design a Gibbs sampler to sample from
the desired posterior distribution. Our method can determine the cluster number automatically
based on the Bayesian information criterion, and handle missing data easily. When applied
to a microarray dataset on the budding yeast, our clustering algorithm provides biologically
meaningful gene clusters according to a functional enrichment analysis
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