pwc-archive/papers-with-abstracts huggingface dataset:
DCTM: Discrete-Continuous Transformation Matching for Semantic Flow
Authors: Seungryong Kim, Dongbo Min, Stephen Lin, Kwanghoon Sohn
Date: 2017-07-18 00:00:00
Proceeding: ICCV 2017 10
Tasks: Semantic correspondence

Techniques for dense semantic correspondence have provided limited ability to
deal with the geometric variations that commonly exist between semantically
similar images. While variations due to scale and rotation have been examined,
there lack practical solutions for more complex deformations such as affine
transformations because of the tremendous size of the associated solution
space. To address this problem, we present a discrete-continuous transformation
matching (DCTM) framework where dense affine transformation fields are inferred
through a discrete label optimization in which the labels are iteratively
updated via continuous regularization. In this way, our approach draws
solutions from the continuous space of affine transformations in a manner that
can be computed efficiently through constant-time edge-aware filtering and a
proposed affine-varying CNN-based descriptor. Experimental results show that
this model outperforms the state-of-the-art methods for dense semantic
correspondence on various benchmarks.
Detecting Intentional Lexical Ambiguity in English Puns
Authors: Elena Mikhalkova, Yuri Karyakin
Date: 2017-07-18 00:00:00

The article describes a model of automatic analysis of puns, where a word is
intentionally used in two meanings at the same time (the target word). We
employ Roget's Thesaurus to discover two groups of words which, in a pun, form
around two abstract bits of meaning (semes). They become a semantic vector,
based on which an SVM classifier learns to recognize puns, reaching a score
0.73 for F-measure. We apply several rule-based methods to locate intentionally
ambiguous (target) words, based on structural and semantic criteria. It appears
that the structural criterion is more effective, although it possibly
characterizes only the tested dataset. The results we get correlate with the
results of other teams at SemEval-2017 competition (Task 7 Detection and
Interpretation of English Puns) considering effects of using supervised
learning models and word statistics.
Coresets for Triangulation
Authors: Qianggong Zhang, Tat-Jun Chin
Date: 2017-07-18 00:00:00

Multiple-view triangulation by $\ell_{\infty}$ minimisation has become
established in computer vision. State-of-the-art $\ell_{\infty}$ triangulation
algorithms exploit the quasiconvexity of the cost function to derive iterative
update rules that deliver the global minimum. Such algorithms, however, can be
computationally costly for large problem instances that contain many image
measurements, e.g., from web-based photo sharing sites or long-term video
recordings. In this paper, we prove that $\ell_{\infty}$ triangulation admits a
coreset approximation scheme, which seeks small representative subsets of the
input data called coresets. A coreset possesses the special property that the
error of the $\ell_{\infty}$ solution on the coreset is within known bounds
from the global minimum. We establish the necessary mathematical underpinnings
of the coreset algorithm, specifically, by enacting the stopping criterion of
the algorithm and proving that the resulting coreset gives the desired
approximation accuracy. On large-scale triangulation problems, our method
provides theoretically sound approximate solutions. Iterated until convergence,
our coreset algorithm is also guaranteed to reach the true optimum. On
practical datasets, we show that our technique can in fact attain the global
minimiser much faster than current methods
Pruning Convolutional Neural Networks for Image Instance Retrieval
Authors: Gaurav Manek, Jie Lin, Vijay Chandrasekhar, Ling-Yu Duan, Sateesh Giduthuri, Xiao-Li Li, Tomaso Poggio
Date: 2017-07-18 00:00:00
Tasks: Image Instance Retrieval, Retrieval, Triplet

In this work, we focus on the problem of image instance retrieval with deep
descriptors extracted from pruned Convolutional Neural Networks (CNN). The
objective is to heavily prune convolutional edges while maintaining retrieval
performance. To this end, we introduce both data-independent and data-dependent
heuristics to prune convolutional edges, and evaluate their performance across
various compression rates with different deep descriptors over several
benchmark datasets. Further, we present an end-to-end framework to fine-tune
the pruned network, with a triplet loss function specially designed for the
retrieval task. We show that the combination of heuristic pruning and
fine-tuning offers 5x compression rate without considerable loss in retrieval
performance.
Discriminative Transformation Learning for Fuzzy Sparse Subspace Clustering
Authors: Zaidao Wen, Biao Hou, Qian Wu, Licheng Jiao
Date: 2017-07-18 00:00:00
Tasks: Clustering

This paper develops a novel iterative framework for subspace clustering in a
learned discriminative feature domain. This framework consists of two modules
of fuzzy sparse subspace clustering and discriminative transformation learning.
In the first module, fuzzy latent labels containing discriminative information
and latent representations capturing the subspace structure will be
simultaneously evaluated in a feature domain. Then the linear transforming
operator with respect to the feature domain will be successively updated in the
second module with the advantages of more discrimination, subspace structure
preservation and robustness to outliers. These two modules will be
alternatively carried out and both theoretical analysis and empirical
evaluations will demonstrate its effectiveness and superiorities. In
particular, experimental results on three benchmark databases for subspace
clustering clearly illustrate that the proposed framework can achieve
significant improvements than other state-of-the-art approaches in terms of
clustering accuracy.
Unconstrained Still/Video-Based Face Verification with Deep Convolutional Neural Networks
Authors: Jun-Cheng Chen, Rajeev Ranjan, Swami Sankaranarayanan, Amit Kumar, Ching-Hui Chen, Vishal M. Patel, Carlos D. Castillo, Rama Chellappa
Date: 2016-05-09 00:00:00
Tasks: Face Detection, Face Recognition, Face Verification, object-detection, Object Detection

Over the last five years, methods based on Deep Convolutional Neural Networks
(DCNNs) have shown impressive performance improvements for object detection and
recognition problems. This has been made possible due to the availability of
large annotated datasets, a better understanding of the non-linear mapping
between input images and class labels as well as the affordability of GPUs. In
this paper, we present the design details of a deep learning system for
unconstrained face recognition, including modules for face detection,
association, alignment and face verification. The quantitative performance
evaluation is conducted using the IARPA Janus Benchmark A (IJB-A), the JANUS
Challenge Set 2 (JANUS CS2), and the LFW dataset. The IJB-A dataset includes
real-world unconstrained faces of 500 subjects with significant pose and
illumination variations which are much harder than the Labeled Faces in the
Wild (LFW) and Youtube Face (YTF) datasets. JANUS CS2 is the extended version
of IJB-A which contains not only all the images/frames of IJB-A but also
includes the original videos for evaluating the video-based face verification
system. Some open issues regarding DCNNs for face verification problems are
then discussed.
BiSeg: Simultaneous Instance Segmentation and Semantic Segmentation with Fully Convolutional Networks
Authors: Viet-Quoc Pham, Satoshi Ito, Tatsuo Kozakaya
Date: 2017-06-07 00:00:00
Tasks: Bayesian Inference, Instance Segmentation, Position, Segmentation, Semantic Segmentation

We present a simple and effective framework for simultaneous semantic
segmentation and instance segmentation with Fully Convolutional Networks
(FCNs). The method, called BiSeg, predicts instance segmentation as a posterior
in Bayesian inference, where semantic segmentation is used as a prior. We
extend the idea of position-sensitive score maps used in recent methods to a
fusion of multiple score maps at different scales and partition modes, and
adopt it as a robust likelihood for instance segmentation inference. As both
Bayesian inference and map fusion are performed per pixel, BiSeg is a fully
convolutional end-to-end solution that inherits all the advantages of FCNs. We
demonstrate state-of-the-art instance segmentation accuracy on PASCAL VOC.
Learning to Compose with Professional Photographs on the Web
Authors: Yi-Ling Chen, Jan Klopp, Min Sun, Shao-Yi Chien, Kwan-Liu Ma
Date: 2017-02-01 00:00:00
Tasks: Image Cropping

Photo composition is an important factor affecting the aesthetics in
photography. However, it is a highly challenging task to model the aesthetic
properties of good compositions due to the lack of globally applicable rules to
the wide variety of photographic styles. Inspired by the thinking process of
photo taking, we formulate the photo composition problem as a view finding
process which successively examines pairs of views and determines their
aesthetic preferences. We further exploit the rich professional photographs on
the web to mine unlimited high-quality ranking samples and demonstrate that an
aesthetics-aware deep ranking network can be trained without explicitly
modeling any photographic rules. The resulting model is simple and effective in
terms of its architectural design and data sampling method. It is also generic
since it naturally learns any photographic rules implicitly encoded in
professional photographs. The experiments show that the proposed view finding
network achieves state-of-the-art performance with sliding window search
strategy on two image cropping datasets.
Top-Rank Enhanced Listwise Optimization for Statistical Machine Translation
Authors: Huadong Chen, Shu-Jian Huang, David Chiang, Xin-yu Dai, Jia-Jun Chen
Date: 2017-07-18 00:00:00
Proceeding: CONLL 2017 8
Tasks: Machine Translation, Translation

Pairwise ranking methods are the basis of many widely used discriminative
training approaches for structure prediction problems in natural language
processing(NLP). Decomposing the problem of ranking hypotheses into pairwise
comparisons enables simple and efficient solutions. However, neglecting the
global ordering of the hypothesis list may hinder learning. We propose a
listwise learning framework for structure prediction problems such as machine
translation. Our framework directly models the entire translation list's
ordering to learn parameters which may better fit the given listwise samples.
Furthermore, we propose top-rank enhanced loss functions, which are more
sensitive to ranking errors at higher positions. Experiments on a large-scale
Chinese-English translation task show that both our listwise learning framework
and top-rank enhanced listwise losses lead to significant improvements in
translation quality.
Improved Neural Machine Translation with a Syntax-Aware Encoder and Decoder
Authors: Huadong Chen, Shu-Jian Huang, David Chiang, Jia-Jun Chen
Date: 2017-07-18 00:00:00
Proceeding: ACL 2017 7
Tasks: Decoder, Machine Translation, NMT, Translation

Most neural machine translation (NMT) models are based on the sequential
encoder-decoder framework, which makes no use of syntactic information. In this
paper, we improve this model by explicitly incorporating source-side syntactic
trees. More specifically, we propose (1) a bidirectional tree encoder which
learns both sequential and tree structured representations; (2) a tree-coverage
model that lets the attention depend on the source-side syntax. Experiments on
Chinese-English translation demonstrate that our proposed models outperform the
sequential attentional model as well as a stronger baseline with a bottom-up
tree encoder and word coverage.
Improving Deep Pancreas Segmentation in CT and MRI Images via Recurrent Neural Contextual Learning and Direct Loss Function
Authors: Jinzheng Cai, Le Lu, Yuanpu Xie, Fuyong Xing, Lin Yang
Date: 2017-07-16 00:00:00
Tasks: Pancreas Segmentation, Segmentation

Deep neural networks have demonstrated very promising performance on accurate
segmentation of challenging organs (e.g., pancreas) in abdominal CT and MRI
scans. The current deep learning approaches conduct pancreas segmentation by
processing sequences of 2D image slices independently through deep, dense
per-pixel masking for each image, without explicitly enforcing spatial
consistency constraint on segmentation of successive slices. We propose a new
convolutional/recurrent neural network architecture to address the contextual
learning and segmentation consistency problem. A deep convolutional sub-network
is first designed and pre-trained from scratch. The output layer of this
network module is then connected to recurrent layers and can be fine-tuned for
contextual learning, in an end-to-end manner. Our recurrent sub-network is a
type of Long short-term memory (LSTM) network that performs segmentation on an
image by integrating its neighboring slice segmentation predictions, in the
form of a dependent sequence processing. Additionally, a novel
segmentation-direct loss function (named Jaccard Loss) is proposed and deep
networks are trained to optimize Jaccard Index (JI) directly. Extensive
experiments are conducted to validate our proposed deep models, on quantitative
pancreas segmentation using both CT and MRI scans. Our method outperforms the
state-of-the-art work on CT [11] and MRI pancreas segmentation [1],
respectively.
Visually Aligned Word Embeddings for Improving Zero-shot Learning
Authors: Ruizhi Qiao, Lingqiao Liu, Chunhua Shen, Anton Van Den Hengel
Date: 2017-07-18 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity, Word Embeddings, Zero-Shot Learning

Zero-shot learning (ZSL) highly depends on a good semantic embedding to
connect the seen and unseen classes. Recently, distributed word embeddings
(DWE) pre-trained from large text corpus have become a popular choice to draw
such a connection. Compared with human defined attributes, DWEs are more
scalable and easier to obtain. However, they are designed to reflect semantic
similarity rather than visual similarity and thus using them in ZSL often leads
to inferior performance. To overcome this visual-semantic discrepancy, this
work proposes an objective function to re-align the distributed word embeddings
with visual information by learning a neural network to map it into a new
representation called visually aligned word embedding (VAWE). Thus the
neighbourhood structure of VAWEs becomes similar to that in the visual domain.
Note that in this work we do not design a ZSL method that projects the visual
features and semantic embeddings onto a shared space but just impose a
requirement on the structure of the mapped word embeddings. This strategy
allows the learned VAWE to generalize to various ZSL methods and visual
features. As evaluated via four state-of-the-art ZSL methods on four benchmark
datasets, the VAWE exhibit consistent performance improvement.
Don't relax: early stopping for convex regularization
Authors: Simon Matet, Lorenzo Rosasco, Silvia Villa, Bang Long Vu
Date: 2017-07-18 00:00:00

We consider the problem of designing efficient regularization algorithms when
regularization is encoded by a (strongly) convex functional. Unlike classical
penalization methods based on a relaxation approach, we propose an iterative
method where regularization is achieved via early stopping. Our results show
that the proposed procedure achieves the same recovery accuracy as penalization
methods, while naturally integrating computational considerations. An empirical
analysis on a number of problems provides promising results with respect to the
state of the art.
Cooperative Hierarchical Dirichlet Processes: Superposition vs. Maximization
Authors: Junyu Xuan, Jie Lu, Guangquan Zhang, Richard Yi Da Xu
Date: 2017-07-18 00:00:00
Tasks: Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Topic Models

The cooperative hierarchical structure is a common and significant data
structure observed in, or adopted by, many research areas, such as: text mining
(author-paper-word) and multi-label classification (label-instance-feature).
Renowned Bayesian approaches for cooperative hierarchical structure modeling
are mostly based on topic models. However, these approaches suffer from a
serious issue in that the number of hidden topics/factors needs to be fixed in
advance and an inappropriate number may lead to overfitting or underfitting.
One elegant way to resolve this issue is Bayesian nonparametric learning, but
existing work in this area still cannot be applied to cooperative hierarchical
structure modeling.
  In this paper, we propose a cooperative hierarchical Dirichlet process (CHDP)
to fill this gap. Each node in a cooperative hierarchical structure is assigned
a Dirichlet process to model its weights on the infinite hidden factors/topics.
Together with measure inheritance from hierarchical Dirichlet process, two
kinds of measure cooperation, i.e., superposition and maximization, are defined
to capture the many-to-many relationships in the cooperative hierarchical
structure. Furthermore, two constructive representations for CHDP, i.e.,
stick-breaking and international restaurant process, are designed to facilitate
the model inference. Experiments on synthetic and real-world data with
cooperative hierarchical structures demonstrate the properties and the ability
of CHDP for cooperative hierarchical structure modeling and its potential for
practical application scenarios.
Dynamic Word Embeddings
Authors: Robert Bamler, Stephan Mandt
Date: 2017-02-27 00:00:00
Proceeding: ICML 2017 8
Tasks: Language Modeling, Language Modelling, Variational Inference, Word Embeddings

We present a probabilistic language model for time-stamped text data which
tracks the semantic evolution of individual words over time. The model
represents words and contexts by latent trajectories in an embedding space. At
each moment in time, the embedding vectors are inferred from a probabilistic
version of word2vec [Mikolov et al., 2013]. These embedding vectors are
connected in time through a latent diffusion process. We describe two scalable
variational inference algorithms--skip-gram smoothing and skip-gram
filtering--that allow us to train the model jointly over all times; thus
learning on all data while simultaneously allowing word and context vectors to
drift. Experimental results on three different corpora demonstrate that our
dynamic model infers word embedding trajectories that are more interpretable
and lead to higher predictive likelihoods than competing methods that are based
on static models trained separately on time slices.
On Automating the Doctrine of Double Effect
Authors: Naveen Sundar Govindarajulu, Selmer Bringsjord
Date: 2017-03-27 00:00:00

The doctrine of double effect ($\mathcal{DDE}$) is a long-studied ethical
principle that governs when actions that have both positive and negative
effects are to be allowed. The goal in this paper is to automate
$\mathcal{DDE}$. We briefly present $\mathcal{DDE}$, and use a first-order
modal logic, the deontic cognitive event calculus, as our framework to
formalize the doctrine. We present formalizations of increasingly stronger
versions of the principle, including what is known as the doctrine of triple
effect. We then use our framework to simulate successfully scenarios that have
been used to test for the presence of the principle in human subjects. Our
framework can be used in two different modes: One can use it to build
$\mathcal{DDE}$-compliant autonomous systems from scratch, or one can use it to
verify that a given AI system is $\mathcal{DDE}$-compliant, by applying a
$\mathcal{DDE}$ layer on an existing system or model. For the latter mode, the
underlying AI system can be built using any architecture (planners, deep neural
networks, bayesian networks, knowledge-representation systems, or a hybrid); as
long as the system exposes a few parameters in its model, such verification is
possible. The role of the $\mathcal{DDE}$ layer here is akin to a (dynamic or
static) software verifier that examines existing software modules. Finally, we
end by presenting initial work on how one can apply our $\mathcal{DDE}$ layer
to the STRIPS-style planning model, and to a modified POMDP model.This is
preliminary work to illustrate the feasibility of the second mode, and we hope
that our initial sketches can be useful for other researchers in incorporating
DDE in their own frameworks.
Text Summarization using Abstract Meaning Representation
Authors: Shibhansh Dohare, Harish Karnick, Vivek Gupta
Date: 2017-06-06 00:00:00
Tasks: Abstract Meaning Representation, Natural Language Understanding, Text Summarization

With an ever increasing size of text present on the Internet, automatic
summary generation remains an important problem for natural language
understanding. In this work we explore a novel full-fledged pipeline for text
summarization with an intermediate step of Abstract Meaning Representation
(AMR). The pipeline proposed by us first generates an AMR graph of an input
story, through which it extracts a summary graph and finally, generate summary
sentences from this summary graph. Our proposed method achieves
state-of-the-art results compared to the other text summarization routines
based on AMR. We also point out some significant problems in the existing
evaluation methods, which make them unsuitable for evaluating summary quality.
Slanted Stixels: Representing San Francisco's Steepest Streets
Authors: Daniel Hernandez-Juarez, Lukas Schneider, Antonio Espinosa, David Vázquez, Antonio M. López, Uwe Franke, Marc Pollefeys, Juan C. Moure
Date: 2017-07-17 00:00:00

In this work we present a novel compact scene representation based on Stixels
that infers geometric and semantic information. Our approach overcomes the
previous rather restrictive geometric assumptions for Stixels by introducing a
novel depth model to account for non-flat roads and slanted objects. Both
semantic and depth cues are used jointly to infer the scene representation in a
sound global energy minimization formulation. Furthermore, a novel
approximation scheme is introduced that uses an extremely efficient
over-segmentation. In doing so, the computational complexity of the Stixel
inference algorithm is reduced significantly, achieving real-time computation
capabilities with only a slight drop in accuracy. We evaluate the proposed
approach in terms of semantic and geometric accuracy as well as run-time on
four publicly available benchmark datasets. Our approach maintains accuracy on
flat road scene datasets while improving substantially on a novel non-flat road
dataset.
Sparse Probit Linear Mixed Model
Authors: Stephan Mandt, Florian Wenzel, Shinichi Nakajima, John P. Cunningham, Christoph Lippert, Marius Kloft
Date: 2015-07-16 00:00:00
Tasks: feature selection, model

Linear Mixed Models (LMMs) are important tools in statistical genetics. When
used for feature selection, they allow to find a sparse set of genetic traits
that best predict a continuous phenotype of interest, while simultaneously
correcting for various confounding factors such as age, ethnicity and
population structure. Formulated as models for linear regression, LMMs have
been restricted to continuous phenotypes. We introduce the Sparse Probit Linear
Mixed Model (Probit-LMM), where we generalize the LMM modeling paradigm to
binary phenotypes. As a technical challenge, the model no longer possesses a
closed-form likelihood function. In this paper, we present a scalable
approximate inference algorithm that lets us fit the model to high-dimensional
data sets. We show on three real-world examples from different domains that in
the setup of binary labels, our algorithm leads to better prediction accuracies
and also selects features which show less correlation with the confounding
factors.