pwc-archive/papers-with-abstracts huggingface dataset:
Video Event Recognition and Anomaly Detection by Combining Gaussian Process and Hierarchical Dirichlet Process Models
Authors: Michael Ying Yang, Wentong Liao, Yanpeng Cao, Bodo Rosenhahn
Date: 2018-02-09 00:00:00
Tasks: Anomaly Detection, General Classification

In this paper, we present an unsupervised learning framework for analyzing
activities and interactions in surveillance videos. In our framework, three
levels of video events are connected by Hierarchical Dirichlet Process (HDP)
model: low-level visual features, simple atomic activities, and multi-agent
interactions. Atomic activities are represented as distribution of low-level
features, while complicated interactions are represented as distribution of
atomic activities. This learning process is unsupervised. Given a training
video sequence, low-level visual features are extracted based on optic flow and
then clustered into different atomic activities and video clips are clustered
into different interactions. The HDP model automatically decide the number of
clusters, i.e. the categories of atomic activities and interactions. Based on
the learned atomic activities and interactions, a training dataset is generated
to train the Gaussian Process (GP) classifier. Then the trained GP models work
in newly captured video to classify interactions and detect abnormal events in
real time. Furthermore, the temporal dependencies between video events learned
by HDP-Hidden Markov Models (HMM) are effectively integrated into GP classifier
to enhance the accuracy of the classification in newly captured videos. Our
framework couples the benefits of the generative model (HDP) with the
discriminant model (GP). We provide detailed experiments showing that our
framework enjoys favorable performance in video event classification in
real-time in a crowded traffic scene.
Triplet-based Deep Similarity Learning for Person Re-Identification
Authors: Wentong Liao, Michael Ying Yang, Ni Zhan, Bodo Rosenhahn
Date: 2018-02-09 00:00:00
Tasks: Person Re-Identification, Triplet

In recent years, person re-identification (re-id) catches great attention in
both computer vision community and industry. In this paper, we propose a new
framework for person re-identification with a triplet-based deep similarity
learning using convolutional neural networks (CNNs). The network is trained
with triplet input: two of them have the same class labels and the other one is
different. It aims to learn the deep feature representation, with which the
distance within the same class is decreased, while the distance between the
different classes is increased as much as possible. Moreover, we trained the
model jointly on six different datasets, which differs from common practice -
one model is just trained on one dataset and tested also on the same one.
However, the enormous number of possible triplet data among the large number of
training samples makes the training impossible. To address this challenge, a
double-sampling scheme is proposed to generate triplets of images as effective
as possible. The proposed framework is evaluated on several benchmark datasets.
The experimental results show that, our method is effective for the task of
person re-identification and it is comparable or even outperforms the
state-of-the-art methods.
Multiple Target Tracking by Learning Feature Representation and Distance Metric Jointly
Authors: Jun Xiang, Guoshuai Zhang, Jianhua Hou, Nong Sang, Rui Huang
Date: 2018-02-09 00:00:00
Tasks: Position, Triplet

Designing a robust affinity model is the key issue in multiple target
tracking (MTT). This paper proposes a novel affinity model by learning feature
representation and distance metric jointly in a unified deep architecture.
Specifically, we design a CNN network to obtain appearance cue tailored towards
person Re-ID, and an LSTM network for motion cue to predict target position,
respectively. Both cues are combined with a triplet loss function, which
performs end-to-end learning of the fused features in a desired embedding
space. Experiments in the challenging MOT benchmark demonstrate, that even by a
simple Linear Assignment strategy fed with affinity scores of our method, very
competitive results are achieved when compared with the most recent
state-of-theart approaches.
A Correlation Based Feature Representation for First-Person Activity Recognition
Authors: Reza Kahani, Alireza Talebpour, Ahmad Mahmoudi-Aznaveh
Date: 2017-11-15 00:00:00
Tasks: Activity Recognition, Egocentric Activity Recognition, Time Series, Time Series Analysis

In this paper, a simple yet efficient activity recognition method for
first-person video is introduced. The proposed method is appropriate for
representation of high-dimensional features such as those extracted from
convolutional neural networks (CNNs). The per-frame (per-segment) extracted
features are considered as a set of time series, and inter and intra-time
series relations are employed to represent the video descriptors. To find the
inter-time relations, the series are grouped and the linear correlation between
each pair of groups is calculated. The relations between them can represent the
scene dynamics and local motions. The introduced grouping strategy helps to
considerably reduce the computational cost. Furthermore, we split the series in
temporal direction in order to preserve long term motions and better focus on
each local time window. In order to extract the cyclic motion patterns, which
can be considered as primary components of various activities, intra-time
series correlations are exploited. The representation method results in highly
discriminative features which can be linearly classified. The experiments
confirm that our method outperforms the state-of-the-art methods on recognizing
first-person activities on the two challenging first-person datasets.
RSDNet: Learning to Predict Remaining Surgery Duration from Laparoscopic Videos Without Manual Annotations
Authors: Andru Putra Twinanda, Gaurav Yengera, Didier Mutter, Jacques Marescaux, Nicolas Padoy
Date: 2018-02-09 00:00:00

Accurate surgery duration estimation is necessary for optimal OR planning,
which plays an important role in patient comfort and safety as well as resource
optimization. It is, however, challenging to preoperatively predict surgery
duration since it varies significantly depending on the patient condition,
surgeon skills, and intraoperative situation. In this paper, we propose a deep
learning pipeline, referred to as RSDNet, which automatically estimates the
remaining surgery duration (RSD) intraoperatively by using only visual
information from laparoscopic videos. Previous state-of-the-art approaches for
RSD prediction are dependent on manual annotation, whose generation requires
expensive expert knowledge and is time-consuming, especially considering the
numerous types of surgeries performed in a hospital and the large number of
laparoscopic videos available. A crucial feature of RSDNet is that it does not
depend on any manual annotation during training, making it easily scalable to
many kinds of surgeries. The generalizability of our approach is demonstrated
by testing the pipeline on two large datasets containing different types of
surgeries: 120 cholecystectomy and 170 gastric bypass videos. The experimental
results also show that the proposed network significantly outperforms a
traditional method of estimating RSD without utilizing manual annotation.
Further, this work provides a deeper insight into the deep learning network
through visualization and interpretation of the features that are automatically
learned.
Vehicle Detection in Aerial Images
Authors: Michael Ying Yang, Wentong Liao, Xinbo Li, Bodo Rosenhahn
Date: 2018-01-22 00:00:00
Tasks: object-detection, Object Detection, vehicle detection

The detection of vehicles in aerial images is widely applied in many
applications. Comparing with object detection in the ground view images,
vehicle detection in aerial images remains a challenging problem because of
small vehicle size, monotone appearance and the complex background. In this
paper, we propose a novel double focal loss convolutional neural network
framework (DFL-CNN). In the proposed framework, the skip connection is used in
the CNN structure to enhance the feature learning. Also, the focal loss
function is used to substitute for conventional cross entropy loss function in
both of the region proposed network and the final classifier. We further
introduce the first large-scale vehicle detection dataset ITCVD with ground
truth annotations for all the vehicles in the scene. We demonstrate the
performance of our model on the existing benchmark DLR 3K dataset as well as
the ITCVD dataset. The experimental results show that our DFL-CNN outperforms
the baselines on vehicle detection.
Using Discretization for Extending the Set of Predictive Features
Authors: Avi Rosenfeld, Ron Illuz, Dovid Gottesman, Mark Last
Date: 2018-02-09 00:00:00
Tasks: Attribute

To date, attribute discretization is typically performed by replacing the
original set of continuous features with a transposed set of discrete ones.
This paper provides support for a new idea that discretized features should
often be used in addition to existing features and as such, datasets should be
extended, and not replaced, by discretization. We also claim that
discretization algorithms should be developed with the explicit purpose of
enriching a non-discretized dataset with discretized values. We present such an
algorithm, D-MIAT, a supervised algorithm that discretizes data based on
Minority Interesting Attribute Thresholds. D-MIAT only generates new features
when strong indications exist for one of the target values needing to be
learned and thus is intended to be used in addition to the original data. We
present extensive empirical results demonstrating the success of using D-MIAT
on $ 28 $ benchmark datasets. We also demonstrate that $ 10 $ other
discretization algorithms can also be used to generate features that yield
improved performance when used in combination with the original non-discretized
data. Our results show that the best predictive performance is attained using a
combination of the original dataset with added features from a "standard"
supervised discretization algorithm and D-MIAT.
Full-Frame Scene Coordinate Regression for Image-Based Localization
Authors: Xiaotian Li, Juha Ylioinas, Juho Kannala
Date: 2018-02-09 00:00:00
Tasks: Camera Relocalization, Data Augmentation, Decoder, Image-Based Localization, regression

Image-based localization, or camera relocalization, is a fundamental problem
in computer vision and robotics, and it refers to estimating camera pose from
an image. Recent state-of-the-art approaches use learning based methods, such
as Random Forests (RFs) and Convolutional Neural Networks (CNNs), to regress
for each pixel in the image its corresponding position in the scene's world
coordinate frame, and solve the final pose via a RANSAC-based optimization
scheme using the predicted correspondences. In this paper, instead of in a
patch-based manner, we propose to perform the scene coordinate regression in a
full-frame manner to make the computation efficient at test time and, more
importantly, to add more global context to the regression process to improve
the robustness. To do so, we adopt a fully convolutional encoder-decoder neural
network architecture which accepts a whole image as input and produces scene
coordinate predictions for all pixels in the image. However, using more global
context is prone to overfitting. To alleviate this issue, we propose to use
data augmentation to generate more data for training. In addition to the data
augmentation in 2D image space, we also augment the data in 3D space. We
evaluate our approach on the publicly available 7-Scenes dataset, and
experiments show that it has better scene coordinate predictions and achieves
state-of-the-art results in localization with improved robustness on the
hardest frames (e.g., frames with repeated structures).
Learning Robust Options
Authors: Daniel J. Mankowitz, Timothy A. Mann, Pierre-Luc Bacon, Doina Precup, Shie Mannor
Date: 2018-02-09 00:00:00
Tasks: Reinforcement Learning

Robust reinforcement learning aims to produce policies that have strong
guarantees even in the face of environments/transition models whose parameters
have strong uncertainty. Existing work uses value-based methods and the usual
primitive action setting. In this paper, we propose robust methods for learning
temporally abstract actions, in the framework of options. We present a Robust
Options Policy Iteration (ROPI) algorithm with convergence guarantees, which
learns options that are robust to model uncertainty. We utilize ROPI to learn
robust options with the Robust Options Deep Q Network (RO-DQN) that solves
multiple tasks and mitigates model misspecification due to model uncertainty.
We present experimental results which suggest that policy iteration with linear
features may have an inherent form of robustness when using coarse feature
representations. In addition, we present experimental results which demonstrate
that robustness helps policy iteration implemented on top of deep neural
networks to generalize over a much broader range of dynamics than non-robust
policy iteration.
Balancing Two-Player Stochastic Games with Soft Q-Learning
Authors: Jordi Grau-Moya, Felix Leibfried, Haitham Bou-Ammar
Date: 2018-02-09 00:00:00
Tasks: Q-Learning, Reinforcement Learning, Reinforcement Learning (RL), Vocal Bursts Valence Prediction

Within the context of video games the notion of perfectly rational agents can
be undesirable as it leads to uninteresting situations, where humans face tough
adversarial decision makers. Current frameworks for stochastic games and
reinforcement learning prohibit tuneable strategies as they seek optimal
performance. In this paper, we enable such tuneable behaviour by generalising
soft Q-learning to stochastic games, where more than one agent interact
strategically. We contribute both theoretically and empirically. On the theory
side, we show that games with soft Q-learning exhibit a unique value and
generalise team games and zero-sum games far beyond these two extremes to cover
a continuous spectrum of gaming behaviour. Experimentally, we show how tuning
agents' constraints affect performance and demonstrate, through a neural
network architecture, how to reliably balance games with high-dimensional
representations.
Deep clustering of longitudinal data
Authors: Louis Falissard, Guy Fagherazzi, Newton Howard, Bruno Falissard
Date: 2018-02-09 00:00:00
Tasks: Clustering, Deep Clustering, Epidemiology

Deep neural networks are a family of computational models that have led to a
dramatical improvement of the state of the art in several domains such as
image, voice or text analysis. These methods provide a framework to model
complex, non-linear interactions in large datasets, and are naturally suited to
the analysis of hierarchical data such as, for instance, longitudinal data with
the use of recurrent neural networks. In the other hand, cohort studies have
become a tool of importance in the research field of epidemiology. In such
studies, variables are measured repeatedly over time, to allow the practitioner
to study their temporal evolution as trajectories, and, as such, as
longitudinal data. This paper investigates the application of the advanced
modelling techniques provided by the deep learning framework in the analysis of
the longitudinal data provided by cohort studies. Methods: A method for
visualizing and clustering longitudinal dataset is proposed, and compared to
other widely used approaches to the problem on both real and simulated
datasets. Results: The proposed method is shown to be coherent with the
preexisting procedures on simple tasks, and to outperform them on more complex
tasks such as the partitioning of longitudinal datasets into non-spherical
clusters. Conclusion: Deep artificial neural networks can be used to visualize
longitudinal data in a low dimensional manifold that is much simpler to
interpret than traditional longitudinal plots are. Consequently, practitioners
should start considering the use of deep artificial neural networks for the
analysis of their longitudinal data in studies to come.
Curve Registered Coupled Low Rank Factorization
Authors: Jeremy Emile Cohen, Rodrigo Cabral Farias, Bertrand Rivet
Date: 2018-02-09 00:00:00
Tasks: Tensor Decomposition

We propose an extension of the canonical polyadic (CP) tensor model where one
of the latent factors is allowed to vary through data slices in a constrained
way. The components of the latent factors, which we want to retrieve from data,
can vary from one slice to another up to a diffeomorphism. We suppose that the
diffeomorphisms are also unknown, thus merging curve registration and tensor
decomposition in one model, which we call registered CP. We present an
algorithm to retrieve both the latent factors and the diffeomorphism, which is
assumed to be in a parametrized form. At the end of the paper, we show
simulation results comparing registered CP with other models from the
literature.
Natural Language Inference over Interaction Space: ICLR 2018 Reproducibility Report
Authors: Martin Mirakyan, Karen Hambardzumyan, Hrant Khachatrian
Date: 2018-02-09 00:00:00
Tasks: Model Selection, Natural Language Inference

We have tried to reproduce the results of the paper "Natural Language
Inference over Interaction Space" submitted to ICLR 2018 conference as part of
the ICLR 2018 Reproducibility Challenge. Initially, we were not aware that the
code was available, so we started to implement the network from scratch. We
have evaluated our version of the model on Stanford NLI dataset and reached
86.38% accuracy on the test set, while the paper claims 88.0% accuracy. The
main difference, as we understand it, comes from the optimizers and the way
model selection is performed.
Self-Bounded Prediction Suffix Tree via Approximate String Matching
Authors: Dongwoo Kim, Christian Walder
Date: 2018-02-09 00:00:00
Proceeding: ICML 2018 7
Tasks: Prediction

Prediction suffix trees (PST) provide an effective tool for sequence
modelling and prediction. Current prediction techniques for PSTs rely on exact
matching between the suffix of the current sequence and the previously observed
sequence. We present a provably correct algorithm for learning a PST with
approximate suffix matching by relaxing the exact matching condition. We then
present a self-bounded enhancement of our algorithm where the depth of suffix
tree grows automatically in response to the model performance on a training
sequence. Through experiments on synthetic datasets as well as three real-world
datasets, we show that the approximate matching PST results in better
predictive performance than the other variants of PST.
Inference, Learning and Attention Mechanisms that Exploit and Preserve Sparsity in Convolutional Networks
Authors: Timo Hackel, Mikhail Usvyatsov, Silvano Galliani, Jan D. Wegner, Konrad Schindler
Date: 2018-01-31 00:00:00
Tasks: GPU

While CNNs naturally lend themselves to densely sampled data, and sophisticated implementations are available, they lack the ability to efficiently process sparse data. In this work we introduce a suite of tools that exploit sparsity in both the feature maps and the filter weights, and thereby allow for significantly lower memory footprints and computation times than the conventional dense framework when processing data with a high degree of sparsity. Our scheme provides (i) an efficient GPU implementation of a convolution layer based on direct, sparse convolution; (ii) a filter step within the convolution layer, which we call attention, that prevents fill-in, i.e., the tendency of convolution to rapidly decrease sparsity, and guarantees an upper bound on the computational resources; and (iii) an adaptation of the back-propagation algorithm, which makes it possible to combine our approach with standard learning frameworks, while still exploiting sparsity in the data and the model.
Gaussian-Dirichlet Posterior Dominance in Sequential Learning
Authors: Ian Osband, Benjamin Van Roy
Date: 2017-02-14 00:00:00

We consider the problem of sequential learning from categorical observations
bounded in [0,1]. We establish an ordering between the Dirichlet posterior over
categorical outcomes and a Gaussian posterior under observations with N(0,1)
noise. We establish that, conditioned upon identical data with at least two
observations, the posterior mean of the categorical distribution will always
second-order stochastically dominate the posterior mean of the Gaussian
distribution. These results provide a useful tool for the analysis of
sequential learning under categorical outcomes.
A Unified Approach for Multi-step Temporal-Difference Learning with Eligibility Traces in Reinforcement Learning
Authors: Long Yang, Minhao Shi, Qian Zheng, Wenjia Meng, Gang Pan
Date: 2018-02-09 00:00:00
Tasks: Reinforcement Learning

Recently, a new multi-step temporal learning algorithm, called $Q(\sigma)$,
unifies $n$-step Tree-Backup (when $\sigma=0$) and $n$-step Sarsa (when
$\sigma=1$) by introducing a sampling parameter $\sigma$. However, similar to
other multi-step temporal-difference learning algorithms, $Q(\sigma)$ needs
much memory consumption and computation time. Eligibility trace is an important
mechanism to transform the off-line updates into efficient on-line ones which
consume less memory and computation time. In this paper, we further develop the
original $Q(\sigma)$, combine it with eligibility traces and propose a new
algorithm, called $Q(\sigma ,\lambda)$, in which $\lambda$ is trace-decay
parameter. This idea unifies Sarsa$(\lambda)$ (when $\sigma =1$) and
$Q^{\pi}(\lambda)$ (when $\sigma =0$). Furthermore, we give an upper error
bound of $Q(\sigma ,\lambda)$ policy evaluation algorithm. We prove that
$Q(\sigma,\lambda)$ control algorithm can converge to the optimal value
function exponentially. We also empirically compare it with conventional
temporal-difference learning methods. Results show that, with an intermediate
value of $\sigma$, $Q(\sigma ,\lambda)$ creates a mixture of the existing
algorithms that can learn the optimal value significantly faster than the
extreme end ($\sigma=0$, or $1$).
Adversarial Metric Learning
Authors: Shuo Chen, Chen Gong, Jian Yang, Xiang Li, Yang Wei, Jun Li
Date: 2018-02-09 00:00:00
Tasks: Metric Learning

In the past decades, intensive efforts have been put to design various loss
functions and metric forms for metric learning problem. These improvements have
shown promising results when the test data is similar to the training data.
However, the trained models often fail to produce reliable distances on the
ambiguous test pairs due to the distribution bias between training set and test
set. To address this problem, the Adversarial Metric Learning (AML) is proposed
in this paper, which automatically generates adversarial pairs to remedy the
distribution bias and facilitate robust metric learning. Specifically, AML
consists of two adversarial stages, i.e. confusion and distinguishment. In
confusion stage, the ambiguous but critical adversarial data pairs are
adaptively generated to mislead the learned metric. In distinguishment stage, a
metric is exhaustively learned to try its best to distinguish both the
adversarial pairs and the original training pairs. Thanks to the challenges
posed by the confusion stage in such competing process, the AML model is able
to grasp plentiful difficult knowledge that has not been contained by the
original training pairs, so the discriminability of AML can be significantly
improved. The entire model is formulated into optimization framework, of which
the global convergence is theoretically proved. The experimental results on toy
data and practical datasets clearly demonstrate the superiority of AML to the
representative state-of-the-art metric learning methodologies.