pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Estimate Pose by Watching Videos
Authors: Prabuddha Chakraborty, Vinay P. Namboodiri
Date: 2017-04-13 00:00:00
Tasks: Action Recognition, Pose Estimation, Temporal Action Localization

In this paper we propose a technique for obtaining coarse pose estimation of
humans in an image that does not require any manual supervision. While a
general unsupervised technique would fail to estimate human pose, we suggest
that sufficient information about coarse pose can be obtained by observing
human motion in multiple frames. Specifically, we consider obtaining surrogate
supervision through videos as a means for obtaining motion based grouping cues.
We supplement the method using a basic object detector that detects persons.
With just these components we obtain a rough estimate of the human pose.
  With these samples for training, we train a fully convolutional neural
network (FCNN)[20] to obtain accurate dense blob based pose estimation. We show
that the results obtained are close to the ground-truth and to the results
obtained using a fully supervised convolutional pose estimation method [31] as
evaluated on a challenging dataset [15]. This is further validated by
evaluating the obtained poses using a pose based action recognition method [5].
In this setting we outperform the results as obtained using the baseline method
that uses a fully supervised pose estimation algorithm and is competitive with
a new baseline created using convolutional pose estimation with full
supervision.
DCFNet: Discriminant Correlation Filters Network for Visual Tracking
Authors: Qiang Wang, Jin Gao, Junliang Xing, Mengdan Zhang, Weiming Hu
Date: 2017-04-13 00:00:00
Tasks: image-classification, Object Tracking, Visual Tracking

Discriminant Correlation Filters (DCF) based methods now become a kind of
dominant approach to online object tracking. The features used in these
methods, however, are either based on hand-crafted features like HoGs, or
convolutional features trained independently from other tasks like image
classification. In this work, we present an end-to-end lightweight network
architecture, namely DCFNet, to learn the convolutional features and perform
the correlation tracking process simultaneously. Specifically, we treat DCF as
a special correlation filter layer added in a Siamese network, and carefully
derive the backpropagation through it by defining the network output as the
probability heatmap of object location. Since the derivation is still carried
out in Fourier frequency domain, the efficiency property of DCF is preserved.
This enables our tracker to run at more than 60 FPS during test time, while
achieving a significant accuracy gain compared with KCF using HoGs. Extensive
evaluations on OTB-2013, OTB-2015, and VOT2015 benchmarks demonstrate that the
proposed DCFNet tracker is competitive with several state-of-the-art trackers,
while being more compact and much faster.
Land Cover Classification via Multi-temporal Spatial Data by Recurrent Neural Networks
Authors: Dino Ienco, Raffaele Gaetano, Claire Dupaquier, Pierre Maurel
Date: 2017-04-13 00:00:00
Tasks: Classification, Earth Observation, General Classification, Land Cover Classification, Open-Ended Question Answering, Scene Classification, Time Series, Time Series Analysis

Nowadays, modern earth observation programs produce huge volumes of satellite
images time series (SITS) that can be useful to monitor geographical areas
through time. How to efficiently analyze such kind of information is still an
open question in the remote sensing field. Recently, deep learning methods
proved suitable to deal with remote sensing data mainly for scene
classification (i.e. Convolutional Neural Networks - CNNs - on single images)
while only very few studies exist involving temporal deep learning approaches
(i.e Recurrent Neural Networks - RNNs) to deal with remote sensing time series.
In this letter we evaluate the ability of Recurrent Neural Networks, in
particular the Long-Short Term Memory (LSTM) model, to perform land cover
classification considering multi-temporal spatial data derived from a time
series of satellite images. We carried out experiments on two different
datasets considering both pixel-based and object-based classification. The
obtained results show that Recurrent Neural Networks are competitive compared
to state-of-the-art classifiers, and may outperform classical approaches in
presence of low represented and/or highly mixed classes. We also show that
using the alternative feature representation generated by LSTM can improve the
performances of standard classifiers.
Adaptive Neighboring Selection Algorithm Based on Curvature Prediction in Manifold Learning
Authors: Lin Ma, Caifa Zhou, Xi Liu, Yubin Xu
Date: 2017-04-13 00:00:00
Tasks: Dimensionality Reduction

Recently manifold learning algorithm for dimensionality reduction attracts
more and more interests, and various linear and nonlinear, global and local
algorithms are proposed. The key step of manifold learning algorithm is the
neighboring region selection. However, so far for the references we know, few
of which propose a generally accepted algorithm to well select the neighboring
region. So in this paper, we propose an adaptive neighboring selection
algorithm, which successfully applies the LLE and ISOMAP algorithms in the
test. It is an algorithm that can find the optimal K nearest neighbors of the
data points on the manifold. And the theoretical basis of the algorithm is the
approximated curvature of the data point on the manifold. Based on Riemann
Geometry, Jacob matrix is a proper mathematical concept to predict the
approximated curvature. By verifying the proposed algorithm on embedding Swiss
roll from R3 to R2 based on LLE and ISOMAP algorithm, the simulation results
show that the proposed adaptive neighboring selection algorithm is feasible and
able to find the optimal value of K, making the residual variance relatively
small and better visualization of the results. By quantitative analysis, the
embedding quality measured by residual variance is increased 45.45% after using
the proposed algorithm in LLE.
3D Deep Learning for Biological Function Prediction from Physical Fields
Authors: Vladimir Golkov, Marcin J. Skwark, Atanas Mirchev, Georgi Dikov, Alexander R. Geanes, Jeffrey Mendenhall, Jens Meiler, Daniel Cremers
Date: 2017-04-13 00:00:00
Tasks: Deep Learning, GPU

Predicting the biological function of molecules, be it proteins or drug-like
compounds, from their atomic structure is an important and long-standing
problem. Function is dictated by structure, since it is by spatial interactions
that molecules interact with each other, both in terms of steric
complementarity, as well as intermolecular forces. Thus, the electron density
field and electrostatic potential field of a molecule contain the "raw
fingerprint" of how this molecule can fit to binding partners. In this paper,
we show that deep learning can predict biological function of molecules
directly from their raw 3D approximated electron density and electrostatic
potential fields. Protein function based on EC numbers is predicted from the
approximated electron density field. In another experiment, the activity of
small molecules is predicted with quality comparable to state-of-the-art
descriptor-based methods. We propose several alternative computational models
for the GPU with different memory and runtime requirements for different sizes
of molecules and of databases. We also propose application-specific
multi-channel data representations. With future improvements of training
datasets and neural network settings in combination with complementary
information sources (sequence, genomic context, expression level), deep
learning can be expected to show its generalization power and revolutionize the
field of molecular function prediction.
Zero-order Reverse Filtering
Authors: Xin Tao, Chao Zhou, Xiaoyong Shen, Jue Wang, Jiaya Jia
Date: 2017-04-13 00:00:00
Proceeding: ICCV 2017 10

In this paper, we study an unconventional but practically meaningful
reversibility problem of commonly used image filters. We broadly define filters
as operations to smooth images or to produce layers via global or local
algorithms. And we raise the intriguingly problem if they are reservable to the
status before filtering. To answer it, we present a novel strategy to
understand general filter via contraction mappings on a metric space. A very
simple yet effective zero-order algorithm is proposed. It is able to
practically reverse most filters with low computational cost. We present quite
a few experiments in the paper and supplementary file to thoroughly verify its
performance. This method can also be generalized to solve other inverse
problems and enables new applications.
Infinite Sparse Structured Factor Analysis
Authors: Matthew C. Pearce, Simon R. White
Date: 2017-04-13 00:00:00
Tasks: Denoising

Matrix factorisation methods decompose multivariate observations as linear
combinations of latent feature vectors. The Indian Buffet Process (IBP)
provides a way to model the number of latent features required for a good
approximation in terms of regularised reconstruction error. Previous work has
focussed on latent feature vectors with independent entries. We extend the
model to include nondiagonal latent covariance structures representing
characteristics such as smoothness. This is done by . Using simulations we
demonstrate that under appropriate conditions a smoothness prior helps to
recover the true latent features, while denoising more accurately. We
demonstrate our method on a real neuroimaging dataset, where computational
tractability is a sufficient challenge that the efficient strategy presented
here is essential.
Italy goes to Stanford: a collection of CoreNLP modules for Italian
Authors: Alessio Palmero Aprosio, Giovanni Moretti
Date: 2016-09-20 00:00:00

In this we paper present Tint, an easy-to-use set of fast, accurate and
extendable Natural Language Processing modules for Italian. It is based on
Stanford CoreNLP and is freely available as a standalone software or a library
that can be integrated in an existing project.
Close Yet Distinctive Domain Adaptation
Authors: Lingkun Luo, Xiaofang Wang, Shiqiang Hu, Chao Wang, Yu-Xing Tang, Liming Chen
Date: 2017-04-13 00:00:00
Tasks: Domain Adaptation, image-classification, Image Classification, Transfer Learning

Domain adaptation is transfer learning which aims to generalize a learning
model across training and testing data with different distributions. Most
previous research tackle this problem in seeking a shared feature
representation between source and target domains while reducing the mismatch of
their data distributions. In this paper, we propose a close yet discriminative
domain adaptation method, namely CDDA, which generates a latent feature
representation with two interesting properties. First, the discrepancy between
the source and target domain, measured in terms of both marginal and
conditional probability distribution via Maximum Mean Discrepancy is minimized
so as to attract two domains close to each other. More importantly, we also
design a repulsive force term, which maximizes the distances between each label
dependent sub-domain to all others so as to drag different class dependent
sub-domains far away from each other and thereby increase the discriminative
power of the adapted domain. Moreover, given the fact that the underlying data
manifold could have complex geometric structure, we further propose the
constraints of label smoothness and geometric structure consistency for label
propagation. Extensive experiments are conducted on 36 cross-domain image
classification tasks over four public datasets. The comprehensive results show
that the proposed method consistently outperforms the state-of-the-art methods
with significant margins.
Oracle-Efficient Online Learning and Auction Design
Authors: Miroslav Dudík, Nika Haghtalab, Haipeng Luo, Robert E. Schapire, Vasilis Syrgkanis, Jennifer Wortman Vaughan
Date: 2016-11-05 00:00:00

We consider the design of computationally efficient online learning algorithms in an adversarial setting in which the learner has access to an offline optimization oracle. We present an algorithm called Generalized Follow-the-Perturbed-Leader and provide conditions under which it is oracle-efficient while achieving vanishing regret. Our results make significant progress on an open problem raised by Hazan and Koren, who showed that oracle-efficient algorithms do not exist in general and asked whether one can identify properties under which oracle-efficient online learning may be possible. Our auction-design framework considers an auctioneer learning an optimal auction for a sequence of adversarially selected valuations with the goal of achieving revenue that is almost as good as the optimal auction in hindsight, among a class of auctions. We give oracle-efficient learning results for: (1) VCG auctions with bidder-specific reserves in single-parameter settings, (2) envy-free item pricing in multi-item auctions, and (3) s-level auctions of Morgenstern and Roughgarden for single-item settings. The last result leads to an approximation of the overall optimal Myerson auction when bidders' valuations are drawn according to a fast-mixing Markov process, extending prior work that only gave such guarantees for the i.i.d. setting. Finally, we derive various extensions, including: (1) oracle-efficient algorithms for the contextual learning setting in which the learner has access to side information (such as bidder demographics), (2) learning with approximate oracles such as those based on Maximal-in-Range algorithms, and (3) no-regret bidding in simultaneous auctions, resolving an open problem of Daskalakis and Syrgkanis.
One-Shot Video Object Segmentation
Authors: Sergi Caelles, Kevis-Kokitsi Maninis, Jordi Pont-Tuset, Laura Leal-Taixé, Daniel Cremers, Luc van Gool
Date: 2016-11-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Foreground Segmentation, Object, Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Segmentation, Visual Object Tracking

This paper tackles the task of semi-supervised video object segmentation,
i.e., the separation of an object from the background in a video, given the
mask of the first frame. We present One-Shot Video Object Segmentation (OSVOS),
based on a fully-convolutional neural network architecture that is able to
successively transfer generic semantic information, learned on ImageNet, to the
task of foreground segmentation, and finally to learning the appearance of a
single annotated object of the test sequence (hence one-shot). Although all
frames are processed independently, the results are temporally coherent and
stable. We perform experiments on two annotated video segmentation databases,
which show that OSVOS is fast and improves the state of the art by a
significant margin (79.8% vs 68.0%).
Interspecies Knowledge Transfer for Facial Keypoint Detection
Authors: Maheen Rashid, Xiuye Gu, Yong Jae Lee
Date: 2017-04-13 00:00:00
Proceeding: CVPR 2017 7
Tasks: Human Detection, Keypoint Detection, Transfer Learning

We present a method for localizing facial keypoints on animals by
transferring knowledge gained from human faces. Instead of directly finetuning
a network trained to detect keypoints on human faces to animal faces (which is
sub-optimal since human and animal faces can look quite different), we propose
to first adapt the animal images to the pre-trained human detection network by
correcting for the differences in animal and human face shape. We first find
the nearest human neighbors for each animal image using an unsupervised shape
matching method. We use these matches to train a thin plate spline warping
network to warp each animal face to look more human-like. The warping network
is then jointly finetuned with a pre-trained human facial keypoint detection
network using an animal dataset. We demonstrate state-of-the-art results on
both horse and sheep facial keypoint detection, and significant improvement
over simple finetuning, especially when training data is scarce. Additionally,
we present a new dataset with 3717 images with horse face and facial keypoint
annotations.
Conjugate-Computation Variational Inference : Converting Variational Inference in Non-Conjugate Models to Inferences in Conjugate Models
Authors: Mohammad Emtiyaz Khan, Wu Lin
Date: 2017-03-13 00:00:00
Tasks: Variational Inference

Variational inference is computationally challenging in models that contain
both conjugate and non-conjugate terms. Methods specifically designed for
conjugate models, even though computationally efficient, find it difficult to
deal with non-conjugate terms. On the other hand, stochastic-gradient methods
can handle the non-conjugate terms but they usually ignore the conjugate
structure of the model which might result in slow convergence. In this paper,
we propose a new algorithm called Conjugate-computation Variational Inference
(CVI) which brings the best of the two worlds together -- it uses conjugate
computations for the conjugate terms and employs stochastic gradients for the
rest. We derive this algorithm by using a stochastic mirror-descent method in
the mean-parameter space, and then expressing each gradient step as a
variational inference in a conjugate model. We demonstrate our algorithm's
applicability to a large class of models and establish its convergence. Our
experimental results show that our method converges much faster than the
methods that ignore the conjugate structure of the model.
Deep representation learning for human motion prediction and classification
Authors: Judith Bütepage, Michael Black, Danica Kragic, Hedvig Kjellström
Date: 2017-02-24 00:00:00
Proceeding: CVPR 2017 7
Tasks: Action Classification, Classification, General Classification, Human motion prediction, motion prediction, Prediction, Representation Learning

Generative models of 3D human motion are often restricted to a small number
of activities and can therefore not generalize well to novel movements or
applications. In this work we propose a deep learning framework for human
motion capture data that learns a generic representation from a large corpus of
motion capture data and generalizes well to new, unseen, motions. Using an
encoding-decoding network that learns to predict future 3D poses from the most
recent past, we extract a feature representation of human motion. Most work on
deep learning for sequence prediction focuses on video and speech. Since
skeletal data has a different structure, we present and evaluate different
network architectures that make different assumptions about time dependencies
and limb correlations. To quantify the learned features, we use the output of
different layers for action classification and visualize the receptive fields
of the network units. Our method outperforms the recent state of the art in
skeletal motion prediction even though these use action specific training data.
Our results show that deep feedforward networks, trained from a generic mocap
database, can successfully be used for feature extraction from human motion
data and that this representation can be used as a foundation for
classification and prediction.
High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis
Authors: Chao Yang, Xin Lu, Zhe Lin, Eli Shechtman, Oliver Wang, Hao Li
Date: 2016-11-30 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Inpainting, Image Manipulation, Vocal Bursts Intensity Prediction

Recent advances in deep learning have shown exciting promise in filling large
holes in natural images with semantically plausible and context aware details,
impacting fundamental image manipulation tasks such as object removal. While
these learning-based methods are significantly more effective in capturing
high-level features than prior techniques, they can only handle very
low-resolution inputs due to memory limitations and difficulty in training.
Even for slightly larger images, the inpainted regions would appear blurry and
unpleasant boundaries become visible. We propose a multi-scale neural patch
synthesis approach based on joint optimization of image content and texture
constraints, which not only preserves contextual structures but also produces
high-frequency details by matching and adapting patches with the most similar
mid-layer feature correlations of a deep classification network. We evaluate
our method on the ImageNet and Paris Streetview datasets and achieved
state-of-the-art inpainting accuracy. We show our approach produces sharper and
more coherent results than prior methods, especially for high-resolution
images.
ZigZag: A new approach to adaptive online learning
Authors: Dylan J. Foster, Alexander Rakhlin, Karthik Sridharan
Date: 2017-04-13 00:00:00

We develop a novel family of algorithms for the online learning setting with
regret against any data sequence bounded by the empirical Rademacher complexity
of that sequence. To develop a general theory of when this type of adaptive
regret bound is achievable we establish a connection to the theory of
decoupling inequalities for martingales in Banach spaces. When the hypothesis
class is a set of linear functions bounded in some norm, such a regret bound is
achievable if and only if the norm satisfies certain decoupling inequalities
for martingales. Donald Burkholder's celebrated geometric characterization of
decoupling inequalities (1984) states that such an inequality holds if and only
if there exists a special function called a Burkholder function satisfying
certain restricted concavity properties. Our online learning algorithms are
efficient in terms of queries to this function.
  We realize our general theory by giving novel efficient algorithms for
classes including lp norms, Schatten p-norms, group norms, and reproducing
kernel Hilbert spaces. The empirical Rademacher complexity regret bound implies
--- when used in the i.i.d. setting --- a data-dependent complexity bound for
excess risk after online-to-batch conversion. To showcase the power of the
empirical Rademacher complexity regret bound, we derive improved rates for a
supervised learning generalization of the online learning with low rank experts
task and for the online matrix prediction task.
  In addition to obtaining tight data-dependent regret bounds, our algorithms
enjoy improved efficiency over previous techniques based on Rademacher
complexity, automatically work in the infinite horizon setting, and are
scale-free. To obtain such adaptive methods, we introduce novel machinery, and
the resulting algorithms are not based on the standard tools of online convex
optimization.
Dempster-Shafer Belief Function - A New Interpretation
Authors: Mieczysław Kłopotek
Date: 2017-04-13 00:00:00

We develop our interpretation of the joint belief distribution and of
evidential updating that matches the following basic requirements:
  * there must exist an efficient method for reasoning within this framework
  * there must exist a clear correspondence between the contents of the
knowledge base and the real world
  * there must be a clear correspondence between the reasoning method and some
real world process
  * there must exist a clear correspondence between the results of the
reasoning process and the results of the real world process corresponding to
the reasoning process.