pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of Langevin Monte Carlo via convex optimization
Authors: Alain Durmus, Szymon Majewski, Błażej Miasojedow
Date: 2018-02-26 00:00:00
Tasks: Bayesian Inference

In this paper, we provide new insights on the Unadjusted Langevin Algorithm.
We show that this method can be formulated as a first order optimization
algorithm of an objective functional defined on the Wasserstein space of order
$2$. Using this interpretation and techniques borrowed from convex
optimization, we give a non-asymptotic analysis of this method to sample from
logconcave smooth target distribution on $\mathbb{R}^d$. Based on this
interpretation, we propose two new methods for sampling from a non-smooth
target distribution, which we analyze as well. Besides, these new algorithms
are natural extensions of the Stochastic Gradient Langevin Dynamics (SGLD)
algorithm, which is a popular extension of the Unadjusted Langevin Algorithm.
Similar to SGLD, they only rely on approximations of the gradient of the target
log density and can be used for large-scale Bayesian inference.
Improvements to context based self-supervised learning
Authors: T. Nathan Mundhenk, Daniel Ho, Barry Y. Chen
Date: 2017-11-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: General Classification, Self-Supervised Learning, Transfer Learning

We develop a set of methods to improve on the results of self-supervised
learning using context. We start with a baseline of patch based arrangement
context learning and go from there. Our methods address some overt problems
such as chromatic aberration as well as other potential problems such as
spatial skew and mid-level feature neglect. We prevent problems with testing
generalization on common self-supervised benchmark tests by using different
datasets during our development. The results of our methods combined yield top
scores on all standard self-supervised benchmarks, including classification and
detection on PASCAL VOC 2007, segmentation on PASCAL VOC 2012, and "linear
tests" on the ImageNet and CSAIL Places datasets. We obtain an improvement over
our baseline method of between 4.0 to 7.1 percentage points on transfer
learning classification tests. We also show results on different standard
network architectures to demonstrate generalization as well as portability. All
data, models and programs are available at:
https://gdo-datasci.llnl.gov/selfsupervised/.
QuickEdit: Editing Text & Translations by Crossing Words Out
Authors: David Grangier, Michael Auli
Date: 2017-11-13 00:00:00
Proceeding: HLT 2018 6
Tasks: Sentence, Translation

We propose a framework for computer-assisted text editing. It applies to
translation post-editing and to paraphrasing. Our proposal relies on very
simple interactions: a human editor modifies a sentence by marking tokens they
would like the system to change. Our model then generates a new sentence which
reformulates the initial sentence by avoiding marked words. The approach builds
upon neural sequence-to-sequence modeling and introduces a neural network which
takes as input a sentence along with change markers. Our model is trained on
translation bitext by simulating post-edits. We demonstrate the advantage of
our approach for translation post-editing through simulated post-edits. We also
evaluate our model for paraphrasing through a user study.
Human Emotional Facial Expression Recognition
Authors: Chendi Wang
Date: 2018-03-28 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), feature selection

An automatic Facial Expression Recognition (FER) model with Adaboost face
detector, feature selection based on manifold learning and synergetic prototype
based classifier has been proposed. Improved feature selection method and
proposed classifier can achieve favorable effectiveness to performance FER in
reasonable processing time.
A Survey on Deep Learning Methods for Robot Vision
Authors: Javier Ruiz-del-Solar, Patricio Loncomilla, Naiomi Soto
Date: 2018-03-28 00:00:00
Tasks: Deep Learning, Survey

Deep learning has allowed a paradigm shift in pattern recognition, from using
hand-crafted features together with statistical classifiers to using
general-purpose learning procedures for learning data-driven representations,
features, and classifiers together. The application of this new paradigm has
been particularly successful in computer vision, in which the development of
deep learning methods for vision applications has become a hot research topic.
Given that deep learning has already attracted the attention of the robot
vision community, the main purpose of this survey is to address the use of deep
learning in robot vision. To achieve this, a comprehensive overview of deep
learning and its usage in computer vision is given, that includes a description
of the most frequently used neural models and their main application areas.
Then, the standard methodology and tools used for designing deep-learning based
vision systems are presented. Afterwards, a review of the principal work using
deep learning in robot vision is presented, as well as current and future
trends related to the use of deep learning in robotics. This survey is intended
to be a guide for the developers of robot vision systems.
Memory Warps for Learning Long-Term Online Video Representations
Authors: Tuan-Hung Vu, Wongun Choi, Samuel Schulter, Manmohan Chandraker
Date: 2018-03-28 00:00:00
Tasks: object-detection, Object Detection

This paper proposes a novel memory-based online video representation that is
efficient, accurate and predictive. This is in contrast to prior works that
often rely on computationally heavy 3D convolutions, ignore actual motion when
aligning features over time, or operate in an off-line mode to utilize future
frames. In particular, our memory (i) holds the feature representation, (ii) is
spatially warped over time to compensate for observer and scene motions, (iii)
can carry long-term information, and (iv) enables predicting feature
representations in future frames. By exploring a variant that operates at
multiple temporal scales, we efficiently learn across even longer time
horizons. We apply our online framework to object detection in videos,
obtaining a large 2.3 times speed-up and losing only 0.9% mAP on ImageNet-VID
dataset, compared to prior works that even use future frames. Finally, we
demonstrate the predictive property of our representation in two novel
detection setups, where features are propagated over time to (i) significantly
enhance a real-time detector by more than 10% mAP in a multi-threaded online
setup and to (ii) anticipate objects in future frames.
Features for Multi-Target Multi-Camera Tracking and Re-Identification
Authors: Ergys Ristani, Carlo Tomasi
Date: 2018-03-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Person Re-Identification, Triplet

Multi-Target Multi-Camera Tracking (MTMCT) tracks many people through video
taken from several cameras. Person Re-Identification (Re-ID) retrieves from a
gallery images of people similar to a person query image. We learn good
features for both MTMCT and Re-ID with a convolutional neural network. Our
contributions include an adaptive weighted triplet loss for training and a new
technique for hard-identity mining. Our method outperforms the state of the art
both on the DukeMTMC benchmarks for tracking, and on the Market-1501 and
DukeMTMC-ReID benchmarks for Re-ID. We examine the correlation between good
Re-ID and good MTMCT scores, and perform ablation studies to elucidate the
contributions of the main components of our system. Code is available.
Entropy-based closure for probabilistic learning on manifolds
Authors: C. Soizea, R. Ghanem, C. Safta, X. Huan, Z. P. Vane, J. Oefelein, G. Lacaz, H. N. Najm, Q. Tang, X. Chen
Date: 2018-03-21 00:00:00

In a recent paper, the authors proposed a general methodology for
probabilistic learning on manifolds. The method was used to generate numerical
samples that are statistically consistent with an existing dataset construed as
a realization from a non-Gaussian random vector. The manifold structure is
learned using diffusion manifolds and the statistical sample generation is
accomplished using a projected Ito stochastic differential equation. This
probabilistic learning approach has been extended to polynomial chaos
representation of databases on manifolds and to probabilistic nonconvex
constrained optimization with a fixed budget of function evaluations. The
methodology introduces an isotropic-diffusion kernel with hyperparameter
{\epsilon}. Currently, {\epsilon} is more or less arbitrarily chosen. In this
paper, we propose a selection criterion for identifying an optimal value of
{\epsilon}, based on a maximum entropy argument. The result is a comprehensive,
closed, probabilistic model for characterizing data sets with hidden
constraints. This entropy argument ensures that out of all possible models,
this is the one that is the most uncertain beyond any specified constraints,
which is selected. Applications are presented for several databases.
Importance Weighted Adversarial Nets for Partial Domain Adaptation
Authors: Jing Zhang, Zewei Ding, Wanqing Li, Philip Ogunbona
Date: 2018-03-25 00:00:00
Proceeding: CVPR 2018 6
Tasks: Domain Adaptation, Partial Domain Adaptation, Transfer Learning, Unsupervised Domain Adaptation, valid

This paper proposes an importance weighted adversarial nets-based method for
unsupervised domain adaptation, specific for partial domain adaptation where
the target domain has less number of classes compared to the source domain.
Previous domain adaptation methods generally assume the identical label spaces,
such that reducing the distribution divergence leads to feasible knowledge
transfer. However, such an assumption is no longer valid in a more realistic
scenario that requires adaptation from a larger and more diverse source domain
to a smaller target domain with less number of classes. This paper extends the
adversarial nets-based domain adaptation and proposes a novel adversarial
nets-based partial domain adaptation method to identify the source samples that
are potentially from the outlier classes and, at the same time, reduce the
shift of shared classes between domains.
SemRe-Rank: Improving Automatic Term Extraction By Incorporating Semantic Relatedness With Personalised PageRank
Authors: Ziqi Zhang, Jie Gao, Fabio Ciravegna
Date: 2017-11-09 00:00:00
Tasks: Term Extraction, Word Embeddings

Automatic Term Extraction deals with the extraction of terminology from a
domain specific corpus, and has long been an established research area in data
and knowledge acquisition. ATE remains a challenging task as it is known that
there is no existing ATE methods that can consistently outperform others in any
domain. This work adopts a refreshed perspective to this problem: instead of
searching for such a 'one-size-fit-all' solution that may never exist, we
propose to develop generic methods to 'enhance' existing ATE methods. We
introduce SemRe-Rank, the first method based on this principle, to incorporate
semantic relatedness - an often overlooked venue - into an existing ATE method
to further improve its performance. SemRe-Rank incorporates word embeddings
into a personalised PageRank process to compute 'semantic importance' scores
for candidate terms from a graph of semantically related words (nodes), which
are then used to revise the scores of candidate terms computed by a base ATE
algorithm. Extensively evaluated with 13 state-of-the-art base ATE methods on
four datasets of diverse nature, it is shown to have achieved widespread
improvement over all base methods and across all datasets, with up to 15
percentage points when measured by the Precision in the top ranked K candidate
terms (the average for a set of K's), or up to 28 percentage points in F1
measured at a K that equals to the expected real terms in the candidates (F1 in
short). Compared to an alternative approach built on the well-known TextRank
algorithm, SemRe-Rank can potentially outperform by up to 8 points in Precision
at top K, or up to 17 points in F1.
Non-Convex Matrix Completion Against a Semi-Random Adversary
Authors: Yu Cheng, Rong Ge
Date: 2018-03-28 00:00:00
Tasks: Matrix Completion

Matrix completion is a well-studied problem with many machine learning
applications. In practice, the problem is often solved by non-convex
optimization algorithms. However, the current theoretical analysis for
non-convex algorithms relies heavily on the assumption that every entry is
observed with exactly the same probability $p$, which is not realistic in
practice.
  In this paper, we investigate a more realistic semi-random model, where the
probability of observing each entry is at least $p$. Even with this mild
semi-random perturbation, we can construct counter-examples where existing
non-convex algorithms get stuck in bad local optima.
  In light of the negative results, we propose a pre-processing step that tries
to re-weight the semi-random input, so that it becomes "similar" to a random
input. We give a nearly-linear time algorithm for this problem, and show that
after our pre-processing, all the local minima of the non-convex objective can
be used to approximately recover the underlying ground-truth matrix.
Deep Learning Object Detection Methods for Ecological Camera Trap Data
Authors: Stefan Schneider, Graham W. Taylor, Stefan C. Kremer
Date: 2018-03-28 00:00:00
Tasks: Deep Learning, Object, object-detection, Object Detection, Object Recognition, Transfer Learning

Deep learning methods for computer vision tasks show promise for automating
the data analysis of camera trap images. Ecological camera traps are a common
approach for monitoring an ecosystem's animal population, as they provide
continual insight into an environment without being intrusive. However, the
analysis of camera trap images is expensive, labour intensive, and time
consuming. Recent advances in the field of deep learning for object detection
show promise towards automating the analysis of camera trap images. Here, we
demonstrate their capabilities by training and comparing two deep learning
object detection classifiers, Faster R-CNN and YOLO v2.0, to identify,
quantify, and localize animal species within camera trap images using the
Reconyx Camera Trap and the self-labeled Gold Standard Snapshot Serengeti data
sets. When trained on large labeled datasets, object recognition methods have
shown success. We demonstrate their use, in the context of realistically sized
ecological data sets, by testing if object detection methods are applicable for
ecological research scenarios when utilizing transfer learning. Faster R-CNN
outperformed YOLO v2.0 with average accuracies of 93.0\% and 76.7\% on the two
data sets, respectively. Our findings show promising steps towards the
automation of the labourious task of labeling camera trap images, which can be
used to improve our understanding of the population dynamics of ecosystems
across the planet.
Learning Deep Representations with Probabilistic Knowledge Transfer
Authors: Nikolaos Passalis, Anastasios Tefas
Date: 2018-03-28 00:00:00
Proceeding: ECCV 2018 9
Tasks: General Classification, Representation Learning, Transfer Learning

Knowledge Transfer (KT) techniques tackle the problem of transferring the
knowledge from a large and complex neural network into a smaller and faster
one. However, existing KT methods are tailored towards classification tasks and
they cannot be used efficiently for other representation learning tasks. In
this paper a novel knowledge transfer technique, that is capable of training a
student model that maintains the same amount of mutual information between the
learned representation and a set of (possible unknown) labels as the teacher
model, is proposed. Apart from outperforming existing KT techniques, the
proposed method allows for overcoming several limitations of existing methods
providing new insight into KT as well as novel KT applications, ranging from
knowledge transfer from handcrafted feature extractors to {cross-modal} KT from
the textual modality into the representation extracted from the visual modality
of the data.
Analysis of Hand Segmentation in the Wild
Authors: Aisha Urooj Khan, Ali Borji
Date: 2018-03-08 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Recognition, Activity Recognition, Fine-grained Action Recognition, Hand Segmentation, Segmentation, Semantic Segmentation, Temporal Action Localization

A large number of works in egocentric vision have concentrated on action and
object recognition. Detection and segmentation of hands in first-person videos,
however, has less been explored. For many applications in this domain, it is
necessary to accurately segment not only hands of the camera wearer but also
the hands of others with whom he is interacting. Here, we take an in-depth look
at the hand segmentation problem. In the quest for robust hand segmentation
methods, we evaluated the performance of the state of the art semantic
segmentation methods, off the shelf and fine-tuned, on existing datasets. We
fine-tune RefineNet, a leading semantic segmentation method, for hand
segmentation and find that it does much better than the best contenders.
Existing hand segmentation datasets are collected in the laboratory settings.
To overcome this limitation, we contribute by collecting two new datasets: a)
EgoYouTubeHands including egocentric videos containing hands in the wild, and
b) HandOverFace to analyze the performance of our models in presence of similar
appearance occlusions. We further explore whether conditional random fields can
help refine generated hand segmentations. To demonstrate the benefit of
accurate hand maps, we train a CNN for hand-based activity recognition and
achieve higher accuracy when a CNN was trained using hand maps produced by the
fine-tuned RefineNet. Finally, we annotate a subset of the EgoHands dataset for
fine-grained action recognition and show that an accuracy of 58.6% can be
achieved by just looking at a single hand pose which is much better than the
chance level (12.5%).
Mobile Video Object Detection with Temporally-Aware Feature Maps
Authors: Mason Liu, Menglong Zhu
Date: 2017-11-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: CPU, Object, object-detection, Object Detection, Video Object Detection

This paper introduces an online model for object detection in videos designed
to run in real-time on low-powered mobile and embedded devices. Our approach
combines fast single-image object detection with convolutional long short term
memory (LSTM) layers to create an interweaved recurrent-convolutional
architecture. Additionally, we propose an efficient Bottleneck-LSTM layer that
significantly reduces computational cost compared to regular LSTMs. Our network
achieves temporal awareness by using Bottleneck-LSTMs to refine and propagate
feature maps across frames. This approach is substantially faster than existing
detection methods in video, outperforming the fastest single-frame models in
model size and computational cost while attaining accuracy comparable to much
more expensive single-frame models on the Imagenet VID 2015 dataset. Our model
reaches a real-time inference speed of up to 15 FPS on a mobile CPU.
Achieving non-discrimination in prediction
Authors: Lu Zhang, Yongkai Wu, Xintao Wu
Date: 2017-02-28 00:00:00
Tasks: Prediction

Discrimination-aware classification is receiving an increasing attention in
data science fields. The pre-process methods for constructing a
discrimination-free classifier first remove discrimination from the training
data, and then learn the classifier from the cleaned data. However, they lack a
theoretical guarantee for the potential discrimination when the classifier is
deployed for prediction. In this paper, we fill this gap by mathematically
bounding the probability of the discrimination in prediction being within a
given interval in terms of the training data and classifier. We adopt the
causal model for modeling the data generation mechanism, and formally defining
discrimination in population, in a dataset, and in prediction. We obtain two
important theoretical results: (1) the discrimination in prediction can still
exist even if the discrimination in the training data is completely removed;
and (2) not all pre-process methods can ensure non-discrimination in prediction
even though they can achieve non-discrimination in the modified training data.
Based on the results, we develop a two-phase framework for constructing a
discrimination-free classifier with a theoretical guarantee. The experiments
demonstrate the theoretical results and show the effectiveness of our two-phase
framework.
Structured Weight Matrices-Based Hardware Accelerators in Deep Neural Networks: FPGAs and ASICs
Authors: Caiwen Ding, Ao Ren, Geng Yuan, Xiaolong Ma, Jiayu Li, Ning Liu, Bo Yuan, Yanzhi Wang
Date: 2018-03-28 00:00:00

Both industry and academia have extensively investigated hardware
accelerations. In this work, to address the increasing demands in computational
capability and memory requirement, we propose structured weight matrices
(SWM)-based compression techniques for both \emph{field programmable gate
array} (FPGA) and \emph{application-specific integrated circuit} (ASIC)
implementations. In algorithm part, SWM-based framework adopts block-circulant
matrices to achieve a fine-grained tradeoff between accuracy and compression
ratio. The SWM-based technique can reduce computational complexity from
O($n^2$) to O($n\log n$) and storage complexity from O($n^2$) to O($n$) for
each layer and both training and inference phases. For FPGA implementations on
deep convolutional neural networks (DCNNs), we achieve at least 152X and 72X
improvement in performance and energy efficiency, respectively using the
SWM-based framework, compared with the baseline of IBM TrueNorth processor
under same accuracy constraints using the data set of MNIST, SVHN, and
CIFAR-10. For FPGA implementations on long short term memory (LSTM) networks,
the proposed SWM-based LSTM can achieve up to 21X enhancement in performance
and 33.5X gains in energy efficiency compared with the baseline accelerator.
For ASIC implementations, the SWM-based ASIC design exhibits impressive
advantages in terms of power, throughput, and energy efficiency. Experimental
results indicate that this method is greatly suitable for applying DNNs onto
both FPGAs and mobile/IoT devices.
Information Design in Crowdfunding under Thresholding Policies
Authors: Wen Shen, Jacob W. Crandall, Ke Yan, Cristina V. Lopes
Date: 2017-09-12 00:00:00

Crowdfunding has emerged as a prominent way for entrepreneurs to secure
funding without sophisticated intermediation. In crowdfunding, an entrepreneur
often has to decide how to disclose the campaign status in order to collect as
many contributions as possible. Such decisions are difficult to make primarily
due to incomplete information. We propose information design as a tool to help
the entrepreneur to improve revenue by influencing backers' beliefs. We
introduce a heuristic algorithm to dynamically compute information-disclosure
policies for the entrepreneur, followed by an empirical evaluation to
demonstrate its competitiveness over the widely-adopted immediate-disclosure
policy. Our results demonstrate that the immediate-disclosure policy is not
optimal when backers follow thresholding policies despite its ease of
implementation. With appropriate heuristics, an entrepreneur can benefit from
dynamic information disclosure. Our work sheds light on information design in a
dynamic setting where agents make decisions using thresholding policies.