pwc-archive/papers-with-abstracts huggingface dataset:
Optimizing Region Selection for Weakly Supervised Object Detection
Authors: Wenhui Jiang, Thuyen Ngo, B. S. Manjunath, Zhicheng Zhao, Fei Su
Date: 2017-08-05 00:00:00
Tasks: Diversity, Object, object-detection, Object Detection, Weakly Supervised Object Detection

Training object detectors with only image-level annotations is very
challenging because the target objects are often surrounded by a large number
of background clutters. Many existing approaches tackle this problem through
object proposal mining. However, the collected positive regions are either low
in precision or lack of diversity, and the strategy of collecting negative
regions is not carefully designed, neither. Moreover, training is often slow
because region selection and object detector training are processed separately.
In this context, the primary contribution of this work is to improve weakly
supervised detection with an optimized region selection strategy. The proposed
method collects purified positive training regions by progressively removing
easy background clutters, and selects discriminative negative regions by mining
class-specific hard samples. This region selection procedure is further
integrated into a CNN-based weakly supervised detection (WSD) framework, and
can be performed in each stochastic gradient descent mini-batch during
training. Therefore, the entire model can be trained end-to-end efficiently.
Extensive evaluation results on PASCAL VOC 2007, VOC 2010 and VOC 2012 datasets
are presented which demonstrate that the proposed method effectively improves
WSD.
Automatic Question-Answering Using A Deep Similarity Neural Network
Authors: Shervin Minaee, Zhu Liu
Date: 2017-08-05 00:00:00
Tasks: Question Answering

Automatic question-answering is a classical problem in natural language
processing, which aims at designing systems that can automatically answer a
question, in the same way as human does. In this work, we propose a deep
learning based model for automatic question-answering. First the questions and
answers are embedded using neural probabilistic modeling. Then a deep
similarity neural network is trained to find the similarity score of a pair of
answer and question. Then for each question, the best answer is found as the
one with the highest similarity score. We first train this model on a
large-scale public question-answering database, and then fine-tune it to
transfer to the customer-care chat data. We have also tested our framework on a
public question-answering database and achieved very good performance.
A causation coefficient and taxonomy of correlation/causation relationships
Authors: Joshua Brulé
Date: 2017-08-05 00:00:00

This paper introduces a causation coefficient which is defined in terms of
probabilistic causal models. This coefficient is suggested as the natural
causal analogue of the Pearson correlation coefficient and permits comparing
causation and correlation to each other in a simple, yet rigorous manner.
Together, these coefficients provide a natural way to classify the possible
correlation/causation relationships that can occur in practice and examples of
each relationship are provided. In addition, the typical relationship between
correlation and causation is analyzed to provide insight into why correlation
and causation are often conflated. Finally, example calculations of the
causation coefficient are shown on a real data set.
Video Frame Synthesis using Deep Voxel Flow
Authors: Ziwei Liu, Raymond A. Yeh, Xiaoou Tang, Yiming Liu, Aseem Agarwala
Date: 2017-02-08 00:00:00
Proceeding: ICCV 2017 10
Tasks: Optical Flow Estimation, Video Prediction

We address the problem of synthesizing new video frames in an existing video,
either in-between existing frames (interpolation), or subsequent to them
(extrapolation). This problem is challenging because video appearance and
motion can be highly complex. Traditional optical-flow-based solutions often
fail where flow estimation is challenging, while newer neural-network-based
methods that hallucinate pixel values directly often produce blurry results. We
combine the advantages of these two methods by training a deep network that
learns to synthesize video frames by flowing pixel values from existing ones,
which we call deep voxel flow. Our method requires no human supervision, and
any video can be used as training data by dropping, and then learning to
predict, existing frames. The technique is efficient, and can be applied at any
video resolution. We demonstrate that our method produces results that both
quantitatively and qualitatively improve upon the state-of-the-art.
DeepCoder: Semi-parametric Variational Autoencoders for Automatic Facial Action Coding
Authors: Dieu Linh Tran, Robert Walecki, Ognjen Rudovic, Stefanos Eleftheriadis, Bjørn Schuller, Maja Pantic
Date: 2017-04-07 00:00:00
Proceeding: ICCV 2017 10
Tasks: Gaussian Processes

Human face exhibits an inherent hierarchy in its representations (i.e.,
holistic facial expressions can be encoded via a set of facial action units
(AUs) and their intensity). Variational (deep) auto-encoders (VAE) have shown
great results in unsupervised extraction of hierarchical latent representations
from large amounts of image data, while being robust to noise and other
undesired artifacts. Potentially, this makes VAEs a suitable approach for
learning facial features for AU intensity estimation. Yet, most existing
VAE-based methods apply classifiers learned separately from the encoded
features. By contrast, the non-parametric (probabilistic) approaches, such as
Gaussian Processes (GPs), typically outperform their parametric counterparts,
but cannot deal easily with large amounts of data. To this end, we propose a
novel VAE semi-parametric modeling framework, named DeepCoder, which combines
the modeling power of parametric (convolutional) and nonparametric (ordinal
GPs) VAEs, for joint learning of (1) latent representations at multiple levels
in a task hierarchy1, and (2) classification of multiple ordinal outputs. We
show on benchmark datasets for AU intensity estimation that the proposed
DeepCoder outperforms the state-of-the-art approaches, and related VAEs and
deep learning models.
Cost-Sensitive Label Embedding for Multi-Label Classification
Authors: Kuan-Hao Huang, Hsuan-Tien Lin
Date: 2016-03-30 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Label embedding (LE) is an important family of multi-label classification
algorithms that digest the label information jointly for better performance.
Different real-world applications evaluate performance by different cost
functions of interest. Current LE algorithms often aim to optimize one specific
cost function, but they can suffer from bad performance with respect to other
cost functions. In this paper, we resolve the performance issue by proposing a
novel cost-sensitive LE algorithm that takes the cost function of interest into
account. The proposed algorithm, cost-sensitive label embedding with
multidimensional scaling (CLEMS), approximates the cost information with the
distances of the embedded vectors by using the classic multidimensional scaling
approach for manifold learning. CLEMS is able to deal with both symmetric and
asymmetric cost functions, and effectively makes cost-sensitive decisions by
nearest-neighbor decoding within the embedded vectors. We derive theoretical
results that justify how CLEMS achieves the desired cost-sensitivity.
Furthermore, extensive experimental results demonstrate that CLEMS is
significantly better than a wide spectrum of existing LE algorithms and
state-of-the-art cost-sensitive algorithms across different cost functions.
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
Authors: Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, Dimitris Metaxas
Date: 2016-12-10 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Generation, Text-to-Image Generation

Synthesizing high-quality images from text descriptions is a challenging
problem in computer vision and has many practical applications. Samples
generated by existing text-to-image approaches can roughly reflect the meaning
of the given descriptions, but they fail to contain necessary details and vivid
object parts. In this paper, we propose Stacked Generative Adversarial Networks
(StackGAN) to generate 256x256 photo-realistic images conditioned on text
descriptions. We decompose the hard problem into more manageable sub-problems
through a sketch-refinement process. The Stage-I GAN sketches the primitive
shape and colors of the object based on the given text description, yielding
Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text
descriptions as inputs, and generates high-resolution images with
photo-realistic details. It is able to rectify defects in Stage-I results and
add compelling details with the refinement process. To improve the diversity of
the synthesized images and stabilize the training of the conditional-GAN, we
introduce a novel Conditioning Augmentation technique that encourages
smoothness in the latent conditioning manifold. Extensive experiments and
comparisons with state-of-the-arts on benchmark datasets demonstrate that the
proposed method achieves significant improvements on generating photo-realistic
images conditioned on text descriptions.
Adversarial Robustness: Softmax versus Openmax
Authors: Andras Rozsa, Manuel Günther, Terrance E. Boult
Date: 2017-08-05 00:00:00
Tasks: Adversarial Robustness, Open Set Learning

Deep neural networks (DNNs) provide state-of-the-art results on various tasks
and are widely used in real world applications. However, it was discovered that
machine learning models, including the best performing DNNs, suffer from a
fundamental problem: they can unexpectedly and confidently misclassify examples
formed by slightly perturbing otherwise correctly recognized inputs. Various
approaches have been developed for efficiently generating these so-called
adversarial examples, but those mostly rely on ascending the gradient of loss.
In this paper, we introduce the novel logits optimized targeting system (LOTS)
to directly manipulate deep features captured at the penultimate layer. Using
LOTS, we analyze and compare the adversarial robustness of DNNs using the
traditional Softmax layer with Openmax, which was designed to provide open set
recognition by defining classes derived from deep representations, and is
claimed to be more robust to adversarial perturbations. We demonstrate that
Openmax provides less vulnerable systems than Softmax to traditional attacks,
however, we show that it can be equally susceptible to more sophisticated
adversarial generation techniques that directly work on deep representations.
Benchmarking and Error Diagnosis in Multi-Instance Pose Estimation
Authors: Matteo Ruggero Ronchi, Pietro Perona
Date: 2017-07-17 00:00:00
Proceeding: ICCV 2017 10
Tasks: Benchmarking, Pose Estimation

We propose a new method to analyze the impact of errors in algorithms for
multi-instance pose estimation and a principled benchmark that can be used to
compare them. We define and characterize three classes of errors -
localization, scoring, and background - study how they are influenced by
instance attributes and their impact on an algorithm's performance. Our
technique is applied to compare the two leading methods for human pose
estimation on the COCO Dataset, measure the sensitivity of pose estimation with
respect to instance size, type and number of visible keypoints, clutter due to
multiple instances, and the relative score of instances. The performance of
algorithms, and the types of error they make, are highly dependent on all these
variables, but mostly on the number of keypoints and the clutter. The analysis
and software tools we propose offer a novel and insightful approach for
understanding the behavior of pose estimation algorithms and an effective
method for measuring their strengths and weaknesses.
Video Frame Interpolation via Adaptive Separable Convolution
Authors: Simon Niklaus, Long Mai, Feng Liu
Date: 2017-08-05 00:00:00
Proceeding: ICCV 2017 10
Tasks: Optical Flow Estimation, Video Frame Interpolation

Standard video frame interpolation methods first estimate optical flow
between input frames and then synthesize an intermediate frame guided by
motion. Recent approaches merge these two steps into a single convolution
process by convolving input frames with spatially adaptive kernels that account
for motion and re-sampling simultaneously. These methods require large kernels
to handle large motion, which limits the number of pixels whose kernels can be
estimated at once due to the large memory demand. To address this problem, this
paper formulates frame interpolation as local separable convolution over input
frames using pairs of 1D kernels. Compared to regular 2D kernels, the 1D
kernels require significantly fewer parameters to be estimated. Our method
develops a deep fully convolutional neural network that takes two input frames
and estimates pairs of 1D kernels for all pixels simultaneously. Since our
method is able to estimate kernels and synthesizes the whole video frame at
once, it allows for the incorporation of perceptual loss to train the neural
network to produce visually pleasing frames. This deep neural network is
trained end-to-end using widely available video data without any human
annotation. Both qualitative and quantitative experiments show that our method
provides a practical solution to high-quality video frame interpolation.
AFFACT - Alignment-Free Facial Attribute Classification Technique
Authors: Manuel Günther, Andras Rozsa, Terrance E. Boult
Date: 2016-11-18 00:00:00
Tasks: Attribute, Classification, Data Augmentation, Facial Attribute Classification, General Classification

Facial attributes are soft-biometrics that allow limiting the search space,
e.g., by rejecting identities with non-matching facial characteristics such as
nose sizes or eyebrow shapes. In this paper, we investigate how the latest
versions of deep convolutional neural networks, ResNets, perform on the facial
attribute classification task. We test two loss functions: the sigmoid
cross-entropy loss and the Euclidean loss, and find that for classification
performance there is little difference between these two. Using an ensemble of
three ResNets, we obtain the new state-of-the-art facial attribute
classification error of 8.00% on the aligned images of the CelebA dataset. More
significantly, we introduce the Alignment-Free Facial Attribute Classification
Technique (AFFACT), a data augmentation technique that allows a network to
classify facial attributes without requiring alignment beyond detected face
bounding boxes. To our best knowledge, we are the first to report similar
accuracy when using only the detected bounding boxes -- rather than requiring
alignment based on automatically detected facial landmarks -- and who can
improve classification accuracy with rotating and scaling test images. We show
that this approach outperforms the CelebA baseline on unaligned images with a
relative improvement of 36.8%.
Deep Metric Learning with Angular Loss
Authors: Jian Wang, Feng Zhou, Shilei Wen, Xiao Liu, Yuanqing Lin
Date: 2017-08-04 00:00:00
Proceeding: ICCV 2017 10
Tasks: Image Retrieval, Metric Learning, Triplet

The modern image search system requires semantic understanding of image, and
a key yet under-addressed problem is to learn a good metric for measuring the
similarity between images. While deep metric learning has yielded impressive
performance gains by extracting high level abstractions from image data, a
proper objective loss function becomes the central issue to boost the
performance. In this paper, we propose a novel angular loss, which takes angle
relationship into account, for learning better similarity metric. Whereas
previous metric learning methods focus on optimizing the similarity
(contrastive loss) or relative similarity (triplet loss) of image pairs, our
proposed method aims at constraining the angle at the negative point of triplet
triangles. Several favorable properties are observed when compared with
conventional methods. First, scale invariance is introduced, improving the
robustness of objective against feature variance. Second, a third-order
geometric constraint is inherently imposed, capturing additional local
structure of triplet triangles than contrastive loss or triplet loss. Third,
better convergence has been demonstrated by experiments on three publicly
available datasets.
Predicting the Law Area and Decisions of French Supreme Court Cases
Authors: Octavia-Maria Sulea, Marcos Zampieri, Mihaela Vela, Josef van Genabith
Date: 2017-08-04 00:00:00
Proceeding: RANLP 2017 9
Tasks: General Classification, text-classification, Text Classification

In this paper, we investigate the application of text classification methods
to predict the law area and the decision of cases judged by the French Supreme
Court. We also investigate the influence of the time period in which a ruling
was made over the textual form of the case description and the extent to which
it is necessary to mask the judge's motivation for a ruling to emulate a
real-world test scenario. We report results of 96% f1 score in predicting a
case ruling, 90% f1 score in predicting the law area of a case, and 75.9% f1
score in estimating the time span when a ruling has been issued using a linear
Support Vector Machine (SVM) classifier trained on lexical features.
Click Here: Human-Localized Keypoints as Guidance for Viewpoint Estimation
Authors: Ryan Szeto, Jason J. Corso
Date: 2017-03-29 00:00:00
Proceeding: ICCV 2017 10
Tasks: Viewpoint Estimation

We motivate and address a human-in-the-loop variant of the monocular
viewpoint estimation task in which the location and class of one semantic
object keypoint is available at test time. In order to leverage the keypoint
information, we devise a Convolutional Neural Network called Click-Here CNN
(CH-CNN) that integrates the keypoint information with activations from the
layers that process the image. It transforms the keypoint information into a 2D
map that can be used to weigh features from certain parts of the image more
heavily. The weighted sum of these spatial features is combined with global
image features to provide relevant information to the prediction layers. To
train our network, we collect a novel dataset of 3D keypoint annotations on
thousands of CAD models, and synthetically render millions of images with 2D
keypoint information. On test instances from PASCAL 3D+, our model achieves a
mean class accuracy of 90.7%, whereas the state-of-the-art baseline only
obtains 85.7% mean class accuracy, justifying our argument for
human-in-the-loop inference.
R-C3D: Region Convolutional 3D Network for Temporal Activity Detection
Authors: Huijuan Xu, Abir Das, Kate Saenko
Date: 2017-03-22 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Detection, Action Recognition In Videos, Activity Detection, General Classification, GPU

We address the problem of activity detection in continuous, untrimmed video
streams. This is a difficult task that requires extracting meaningful
spatio-temporal features to capture activities, accurately localizing the start
and end times of each activity. We introduce a new model, Region Convolutional
3D Network (R-C3D), which encodes the video streams using a three-dimensional
fully convolutional network, then generates candidate temporal regions
containing activities, and finally classifies selected regions into specific
activities. Computation is saved due to the sharing of convolutional features
between the proposal and the classification pipelines. The entire model is
trained end-to-end with jointly optimized localization and classification
losses. R-C3D is faster than existing methods (569 frames per second on a
single Titan X Maxwell GPU) and achieves state-of-the-art results on THUMOS'14.
We further demonstrate that our model is a general activity detection framework
that does not rely on assumptions about particular dataset properties by
evaluating our approach on ActivityNet and Charades. Our code is available at
http://ai.bu.edu/r-c3d/.
A network approach to topic models
Authors: Martin Gerlach, Tiago P. Peixoto, Eduardo G. Altmann
Date: 2017-08-04 00:00:00
Tasks: Community Detection, Model Selection, Sociology, Stochastic Block Model, Topic Models

One of the main computational and scientific challenges in the modern age is
to extract useful information from unstructured texts. Topic models are one
popular machine-learning approach which infers the latent topical structure of
a collection of documents. Despite their success --- in particular of its most
widely used variant called Latent Dirichlet Allocation (LDA) --- and numerous
applications in sociology, history, and linguistics, topic models are known to
suffer from severe conceptual and practical problems, e.g. a lack of
justification for the Bayesian priors, discrepancies with statistical
properties of real texts, and the inability to properly choose the number of
topics. Here we obtain a fresh view on the problem of identifying topical
structures by relating it to the problem of finding communities in complex
networks. This is achieved by representing text corpora as bipartite networks
of documents and words. By adapting existing community-detection methods --
using a stochastic block model (SBM) with non-parametric priors -- we obtain a
more versatile and principled framework for topic modeling (e.g., it
automatically detects the number of topics and hierarchically clusters both the
words and documents). The analysis of artificial and real corpora demonstrates
that our SBM approach leads to better topic models than LDA in terms of
statistical model selection. More importantly, our work shows how to formally
relate methods from community detection and topic modeling, opening the
possibility of cross-fertilization between these two fields.
Query-guided Regression Network with Context Policy for Phrase Grounding
Authors: Kan Chen, Rama Kovvuri, Ram Nevatia
Date: 2017-08-04 00:00:00
Proceeding: ICCV 2017 10
Tasks: Phrase Grounding, regression, Reinforcement Learning

Given a textual description of an image, phrase grounding localizes objects
in the image referred by query phrases in the description. State-of-the-art
methods address the problem by ranking a set of proposals based on the
relevance to each query, which are limited by the performance of independent
proposal generation systems and ignore useful cues from context in the
description. In this paper, we adopt a spatial regression method to break the
performance limit, and introduce reinforcement learning techniques to further
leverage semantic context information. We propose a novel Query-guided
Regression network with Context policy (QRC Net) which jointly learns a
Proposal Generation Network (PGN), a Query-guided Regression Network (QRN) and
a Context Policy Network (CPN). Experiments show QRC Net provides a significant
improvement in accuracy on two popular datasets: Flickr30K Entities and Referit
Game, with 14.25% and 17.14% increase over the state-of-the-arts respectively.