pwc-archive/papers-with-abstracts huggingface dataset:
A Novel Space-Time Representation on the Positive Semidefinite Con for Facial Expression Recognition
Authors: Anis Kacem, Mohamed Daoudi, Boulbaba Ben Amor, Juan Carlos Alvarez-Paiva
Date: 2017-07-20 00:00:00
Proceeding: ICCV 2017
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

In this paper, we study the problem of facial expression recognition using a
novel space-time geometric representation. We describe the temporal evolution
of facial landmarks as parametrized trajectories on the Riemannian manifold of
positive semidefinite matrices of fixed-rank. Our representation has the
advantage to bring naturally a second desirable quantity when comparing shapes
-- the spatial covariance -- in addition to the conventional affine-shape
representation. We derive then geometric and computational tools for
rate-invariant analysis and adaptive re-sampling of trajectories, grounding on
the Riemannian geometry of the manifold. Specifically, our approach involves
three steps: 1) facial landmarks are first mapped into the Riemannian manifold
of positive semidefinite matrices of rank 2, to build time-parameterized
trajectories; 2) a temporal alignment is performed on the trajectories,
providing a geometry-aware (dis-)similarity measure between them; 3) finally,
pairwise proximity function SVM (ppfSVM) is used to classify them,
incorporating the latter (dis-)similarity measure into the kernel function. We
show the effectiveness of the proposed approach on four publicly available
benchmarks (CK+, MMI, Oulu-CASIA, and AFEW). The results of the proposed
approach are comparable to or better than the state-of-the-art methods when
involving only facial landmarks.
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
Authors: Hirokatsu Kataoka, Soma Shirakabe, Yun He, Shunya Ueta, Teppei Suzuki, Kaori Abe, Asako Kanezaki, Shin'ichiro Morita, Toshiyuki Yabe, Yoshihiro Kanehara, Hiroya Yatsuyanagi, Shinya Maruyama, Ryosuke Takasawa, Masataka Fuchida, Yudai Miyashita, Kazushige Okayasu, Yuta Matsuzaki
Date: 2017-07-20 00:00:00

The paper gives futuristic challenges disscussed in the cvpaper.challenge. In
2015 and 2016, we thoroughly study 1,600+ papers in several
conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV.
Scalable Full Flow with Learned Binary Descriptors
Authors: Gottfried Munda, Alexander Shekhovtsov, Patrick Knöbelreiter, Thomas Pock
Date: 2017-07-20 00:00:00
Tasks: Optical Flow Estimation

We propose a method for large displacement optical flow in which local
matching costs are learned by a convolutional neural network (CNN) and a
smoothness prior is imposed by a conditional random field (CRF). We tackle the
computation- and memory-intensive operations on the 4D cost volume by a
min-projection which reduces memory complexity from quadratic to linear and
binary descriptors for efficient matching. This enables evaluation of the cost
on the fly and allows to perform learning and CRF inference on high resolution
images without ever storing the 4D cost volume. To address the problem of
learning binary descriptors we propose a new hybrid learning scheme. In
contrast to current state of the art approaches for learning binary CNNs we can
compute the exact non-zero gradient within our model. We compare several
methods for training binary descriptors and show results on public available
benchmarks.
Semantic Segmentation with Reverse Attention
Authors: Qin Huang, Chunyang Xia, Chi-Hao Wu, Siyang Li, Ye Wang, Yuhang Song, C. -C. Jay Kuo
Date: 2017-07-20 00:00:00
Tasks: Segmentation, Semantic Segmentation

Recent development in fully convolutional neural network enables efficient
end-to-end learning of semantic segmentation. Traditionally, the convolutional
classifiers are taught to learn the representative semantic features of labeled
semantic objects. In this work, we propose a reverse attention network (RAN)
architecture that trains the network to capture the opposite concept (i.e.,
what are not associated with a target class) as well. The RAN is a three-branch
network that performs the direct, reverse and reverse-attention learning
processes simultaneously. Extensive experiments are conducted to show the
effectiveness of the RAN in semantic segmentation. Being built upon the
DeepLabv2-LargeFOV, the RAN achieves the state-of-the-art mIoU score (48.1%)
for the challenging PASCAL-Context dataset. Significant performance
improvements are also observed for the PASCAL-VOC, Person-Part, NYUDv2 and
ADE20K datasets.
Compressing Green's function using intermediate representation between imaginary-time and real-frequency domains
Authors: Hiroshi Shinaoka, Junya Otsuki, Masayuki Ohzeki, Kazuyoshi Yoshimi
Date: 2017-02-10 00:00:00

New model-independent compact representations of imaginary-time data are
presented in terms of the intermediate representation (IR) of analytical
continuation. This is motivated by a recent numerical finding by the authors
[J. Otsuki et al., arXiv:1702.03056]. We demonstrate the efficiency of the IR
through continuous-time quantum Monte Carlo calculations of an Anderson
impurity model. We find that the IR yields a significantly compact form of
various types of correlation functions. The present framework will provide
general ways to boost the power of cutting-edge diagrammatic/quantum Monte
Carlo treatments of many-body systems.
Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions
Authors: Sara Magliacane, Thijs van Ommen, Tom Claassen, Stephan Bongers, Philip Versteeg, Joris M. Mooij
Date: 2017-07-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Causal Inference, Domain Adaptation

An important goal common to domain adaptation and causal inference is to make
accurate predictions when the distributions for the source (or training)
domain(s) and target (or test) domain(s) differ. In many cases, these different
distributions can be modeled as different contexts of a single underlying
system, in which each distribution corresponds to a different perturbation of
the system, or in causal terms, an intervention. We focus on a class of such
causal domain adaptation problems, where data for one or more source domains
are given, and the task is to predict the distribution of a certain target
variable from measurements of other variables in one or more target domains. We
propose an approach for solving these problems that exploits causal inference
and does not rely on prior knowledge of the causal graph, the type of
interventions or the intervention targets. We demonstrate our approach by
evaluating a possible implementation on simulated and real world data.
The Predictron: End-To-End Learning and Planning
Authors: David Silver, Hado van Hasselt, Matteo Hessel, Tom Schaul, Arthur Guez, Tim Harley, Gabriel Dulac-Arnold, David Reichert, Neil Rabinowitz, Andre Barreto, Thomas Degris
Date: 2016-12-28 00:00:00
Proceeding: ICML 2017 8

One of the key challenges of artificial intelligence is to learn models that
are effective in the context of planning. In this document we introduce the
predictron architecture. The predictron consists of a fully abstract model,
represented by a Markov reward process, that can be rolled forward multiple
"imagined" planning steps. Each forward pass of the predictron accumulates
internal rewards and values over multiple planning depths. The predictron is
trained end-to-end so as to make these accumulated values accurately
approximate the true value function. We applied the predictron to procedurally
generated random mazes and a simulator for the game of pool. The predictron
yielded significantly more accurate predictions than conventional deep neural
network architectures.
Deep CNN Framework for Audio Event Recognition using Weakly Labeled Web Data
Authors: Anurag Kumar, Bhiksha Raj
Date: 2017-07-09 00:00:00

The development of audio event recognition systems require labeled training data, which are generally hard to obtain. One promising source of recordings of audio events is the large amount of multimedia data on the web. In particular, if the audio content analysis must itself be performed on web audio, it is important to train the recognizers themselves from such data. Training from these web data, however, poses several challenges, the most important being the availability of labels: labels, if any, that may be obtained for the data are generally weak, and not of the kind conventionally required for training detectors or classifiers. We propose that learning algorithms that can exploit weak labels offer an effective method to learn from web data. We then propose a robust and efficient deep convolutional neural network (CNN) based framework to learn audio event recognizers from weakly labeled data. The proposed method can train from and analyze recordings of variable length in an efficient manner and outperforms a network trained with strongly labeled web data by a considerable margin. Moreover, even though we learn from weakly labeled data, where event time stamps within the recording are not available during training, our proposed framework is able to localize events during the inference stage.
Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors
Authors: Hong-Yu Zhou, Bin-Bin Gao, Jianxin Wu
Date: 2017-07-20 00:00:00
Proceeding: ICCV 2017 10
Tasks: object-detection, Object Detection

Object detection aims at high speed and accuracy simultaneously. However,
fast models are usually less accurate, while accurate models cannot satisfy our
need for speed. A fast model can be 10 times faster but 50\% less accurate than
an accurate model. In this paper, we propose Adaptive Feeding (AF) to combine a
fast (but less accurate) detector and an accurate (but slow) detector, by
adaptively determining whether an image is easy or hard and choosing an
appropriate detector for it. In practice, we build a cascade of detectors,
including the AF classifier which make the easy vs. hard decision and the two
detectors. The AF classifier can be tuned to obtain different tradeoff between
speed and accuracy, which has negligible training time and requires no
additional training data. Experimental results on the PASCAL VOC, MS COCO and
Caltech Pedestrian datasets confirm that AF has the ability to achieve
comparable speed as the fast detector and comparable accuracy as the accurate
one at the same time. As an example, by combining the fast SSD300 with the
accurate SSD500 detector, AF leads to 50\% speedup over SSD500 with the same
precision on the VOC2007 test set.
Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transforming
Authors: Xiu-Shen Wei, Chen-Lin Zhang, Jianxin Wu, Chunhua Shen, Zhi-Hua Zhou
Date: 2017-07-20 00:00:00
Tasks: Object, object-detection, Object Detection, Object Discovery, Single-object discovery, valid

Reusable model design becomes desirable with the rapid expansion of computer
vision and machine learning applications. In this paper, we focus on the
reusability of pre-trained deep convolutional models. Specifically, different
from treating pre-trained models as feature extractors, we reveal more
treasures beneath convolutional layers, i.e., the convolutional activations
could act as a detector for the common object in the image co-localization
problem. We propose a simple yet effective method, termed Deep Descriptor
Transforming (DDT), for evaluating the correlations of descriptors and then
obtaining the category-consistent regions, which can accurately locate the
common object in a set of unlabeled images, i.e., unsupervised object
discovery. Empirical studies validate the effectiveness of the proposed DDT
method. On benchmark image co-localization datasets, DDT consistently
outperforms existing state-of-the-art methods by a large margin. Moreover, DDT
also demonstrates good generalization ability for unseen categories and
robustness for dealing with noisy data. Beyond those, DDT can be also employed
for harvesting web images into valid external data sources for improving
performance of both image recognition and object detection.
Robust Cost-Sensitive Learning for Recommendation with Implicit Feedback
Authors: Peng Yang, Peilin Zhao, Xin Gao, Yong liu
Date: 2017-07-03 00:00:00

Recommendation is the task of improving customer experience through
personalized recommendation based on users' past feedback. In this paper, we
investigate the most common scenario: the user-item (U-I) matrix of implicit
feedback. Even though many recommendation approaches are designed based on
implicit feedback, they attempt to project the U-I matrix into a low-rank
latent space, which is a strict restriction that rarely holds in practice. In
addition, although misclassification costs from imbalanced classes are
significantly different, few methods take the cost of classification error into
account. To address aforementioned issues, we propose a robust framework by
decomposing the U-I matrix into two components: (1) a low-rank matrix that
captures the common preference, and (2) a sparse matrix that detects the
user-specific preference of individuals. A cost-sensitive learning model is
embedded into the framework. Specifically, this model exploits different costs
in the loss function for the observed and unobserved instances. We show that
the resulting non-smooth convex objective can be optimized efficiently by an
accelerated projected gradient method with closed-form solutions. Morever, the
proposed algorithm can be scaled up to large-sized datasets after a relaxation.
The theoretical result shows that even with a small fraction of 1's in the U-I
matrix $M\in\mathbb{R}^{n\times m}$, the cost-sensitive error of the proposed
model is upper bounded by $O(\frac{\alpha}{\sqrt{mn}})$, where $\alpha$ is a
bias over imbalanced classes. Finally, empirical experiments are extensively
carried out to evaluate the effectiveness of our proposed algorithm.
Encouraging experimental results show that our algorithm outperforms several
state-of-the-art algorithms on benchmark recommendation datasets.
Large-Scale Goodness Polarity Lexicons for Community Question Answering
Authors: Todor Mihaylov, Daniel Belchev, Yasen Kiprov, Ivan Koychev, Preslav Nakov
Date: 2017-07-20 00:00:00
Tasks: Community Question Answering, Question Answering, Sentiment Analysis

We transfer a key idea from the field of sentiment analysis to a new domain:
community question answering (cQA). The cQA task we are interested in is the
following: given a question and a thread of comments, we want to re-rank the
comments so that the ones that are good answers to the question would be ranked
higher than the bad ones. We notice that good vs. bad comments use specific
vocabulary and that one can often predict the goodness/badness of a comment
even ignoring the question, based on the comment contents only. This leads us
to the idea to build a good/bad polarity lexicon as an analogy to the
positive/negative sentiment polarity lexicons, commonly used in sentiment
analysis. In particular, we use pointwise mutual information in order to build
large-scale goodness polarity lexicons in a semi-supervised manner starting
with a small number of initial seeds. The evaluation results show an
improvement of 0.7 MAP points absolute over a very strong baseline and
state-of-the art performance on SemEval-2016 Task 3.
Google's Cloud Vision API Is Not Robust To Noise
Authors: Hossein Hosseini, Baicen Xiao, Radha Poovendran
Date: 2017-04-16 00:00:00

Google has recently introduced the Cloud Vision API for image analysis.
According to the demonstration website, the API "quickly classifies images into
thousands of categories, detects individual objects and faces within images,
and finds and reads printed words contained within images." It can be also used
to "detect different types of inappropriate content from adult to violent
content."
  In this paper, we evaluate the robustness of Google Cloud Vision API to input
perturbation. In particular, we show that by adding sufficient noise to the
image, the API generates completely different outputs for the noisy image,
while a human observer would perceive its original content. We show that the
attack is consistently successful, by performing extensive experiments on
different image types, including natural images, images containing faces and
images with texts. For instance, using images from ImageNet dataset, we found
that adding an average of 14.25% impulse noise is enough to deceive the API.
Our findings indicate the vulnerability of the API in adversarial environments.
For example, an adversary can bypass an image filtering system by adding noise
to inappropriate images. We then show that when a noise filter is applied on
input images, the API generates mostly the same outputs for restored images as
for original images. This observation suggests that cloud vision API can
readily benefit from noise filtering, without the need for updating image
analysis algorithms.
Modeling the Intra-class Variability for Liver Lesion Detection using a Multi-class Patch-based CNN
Authors: Maayan Frid-Adar, Idit Diamant, Eyal Klang, Michal Amitai, Jacob Goldberger, Hayit Greenspan
Date: 2017-07-19 00:00:00
Tasks: Lesion Detection

Automatic detection of liver lesions in CT images poses a great challenge for
researchers. In this work we present a deep learning approach that models
explicitly the variability within the non-lesion class, based on prior
knowledge of the data, to support an automated lesion detection system. A
multi-class convolutional neural network (CNN) is proposed to categorize input
image patches into sub-categories of boundary and interior patches, the
decisions of which are fused to reach a binary lesion vs non-lesion decision.
For validation of our system, we use CT images of 132 livers and 498 lesions.
Our approach shows highly improved detection results that outperform the
state-of-the-art fully convolutional network. Automated computerized tools, as
shown in this work, have the potential in the future to support the
radiologists towards improved detection.
Bioinformatics and Classical Literary Study
Authors: Pramit Chaudhuri, Joseph P. Dexter
Date: 2016-02-29 00:00:00

This paper describes the Quantitative Criticism Lab, a collaborative
initiative between classicists, quantitative biologists, and computer
scientists to apply ideas and methods drawn from the sciences to the study of
literature. A core goal of the project is the use of computational biology,
natural language processing, and machine learning techniques to investigate
authorial style, intertextuality, and related phenomena of literary
significance. As a case study in our approach, here we review the use of
sequence alignment, a common technique in genomics and computational
linguistics, to detect intertextuality in Latin literature. Sequence alignment
is distinguished by its ability to find inexact verbal similarities, which
makes it ideal for identifying phonetic echoes in large corpora of Latin texts.
Although especially suited to Latin, sequence alignment in principle can be
extended to many other languages.
RKL: a general, invariant Bayes solution for Neyman-Scott
Authors: Michael Brand
Date: 2017-07-20 00:00:00

Neyman-Scott is a classic example of an estimation problem with a
partially-consistent posterior, for which standard estimation methods tend to
produce inconsistent results. Past attempts to create consistent estimators for
Neyman-Scott have led to ad-hoc solutions, to estimators that do not satisfy
representation invariance, to restrictions over the choice of prior and more.
We present a simple construction for a general-purpose Bayes estimator,
invariant to representation, which satisfies consistency on Neyman-Scott over
any non-degenerate prior. We argue that the good attributes of the estimator
are due to its intrinsic properties, and generalise beyond Neyman-Scott as
well.
Improving Discourse Relation Projection to Build Discourse Annotated Corpora
Authors: Majid Laali, Leila Kosseim
Date: 2017-07-20 00:00:00
Proceeding: RANLP 2017 9
Tasks: Relation, Translation, Word Alignment

The naive approach to annotation projection is not effective to project
discourse annotations from one language to another because implicit discourse
relations are often changed to explicit ones and vice-versa in the translation.
In this paper, we propose a novel approach based on the intersection between
statistical word-alignment models to identify unsupported discourse
annotations. This approach identified 65% of the unsupported annotations in the
English-French parallel sentences from Europarl. By filtering out these
unsupported annotations, we induced the first PDTB-style discourse annotated
corpus for French from Europarl. We then used this corpus to train a classifier
to identify the discourse-usage of French discourse connectives and show a 15%
improvement of F1-score compared to the classifier trained on the non-filtered
annotations.
Video Question Answering via Attribute-Augmented Attention Network Learning
Authors: Yunan Ye, Zhou Zhao, Yimeng Li, Long Chen, Jun Xiao, Yueting Zhuang
Date: 2017-07-20 00:00:00
Tasks: Attribute, Information Retrieval, Multiple-choice, Question Answering, Representation Learning, Retrieval, Video Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Video Question Answering is a challenging problem in visual information
retrieval, which provides the answer to the referenced video content according
to the question. However, the existing visual question answering approaches
mainly tackle the problem of static image question, which may be ineffectively
for video question answering due to the insufficiency of modeling the temporal
dynamics of video contents. In this paper, we study the problem of video
question answering by modeling its temporal dynamics with frame-level attention
mechanism. We propose the attribute-augmented attention network learning
framework that enables the joint frame-level attribute detection and unified
video representation learning for video question answering. We then incorporate
the multi-step reasoning process for our proposed attention network to further
improve the performance. We construct a large-scale video question answering
dataset. We conduct the experiments on both multiple-choice and open-ended
video question answering tasks to show the effectiveness of the proposed
method.
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
Authors: Jian-Hao Luo, Jianxin Wu, Weiyao Lin
Date: 2017-07-20 00:00:00
Proceeding: ICCV 2017 10
Tasks: Neural Network Compression

We propose an efficient and unified framework, namely ThiNet, to
simultaneously accelerate and compress CNN models in both training and
inference stages. We focus on the filter level pruning, i.e., the whole filter
would be discarded if it is less important. Our method does not change the
original network structure, thus it can be perfectly supported by any
off-the-shelf deep learning libraries. We formally establish filter pruning as
an optimization problem, and reveal that we need to prune filters based on
statistics information computed from its next layer, not the current layer,
which differentiates ThiNet from existing methods. Experimental results
demonstrate the effectiveness of this strategy, which has advanced the
state-of-the-art. We also show the performance of ThiNet on ILSVRC-12
benchmark. ThiNet achieves 3.31$\times$ FLOPs reduction and 16.63$\times$
compression on VGG-16, with only 0.52$\%$ top-5 accuracy drop. Similar
experiments with ResNet-50 reveal that even for a compact network, ThiNet can
also reduce more than half of the parameters and FLOPs, at the cost of roughly
1$\%$ top-5 accuracy drop. Moreover, the original VGG-16 model can be further
pruned into a very small model with only 5.05MB model size, preserving AlexNet
level accuracy but showing much stronger generalization ability.