pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures
Authors: Pierre-François Marteau, Sylvie Gibet, Clément Reverdy
Date: 2016-11-23 00:00:00
Tasks: Action Recognition, Dimensionality Reduction, Gesture Recognition, Temporal Action Localization

In the scope of gestural action recognition, the size of the feature vector
representing movements is in general quite large especially when full body
movements are considered. Furthermore, this feature vector evolves during the
movement performance so that a complete movement is fully represented by a
matrix M of size DxT , whose element M i, j represents the value of feature i
at timestamps j. Many studies have addressed dimensionality reduction
considering only the size of the feature vector lying in R D to reduce both the
variability of gestural sequences expressed in the reduced space, and the
computational complexity of their processing. In return, very few of these
methods have explicitly addressed the dimensionality reduction along the time
axis. Yet this is a major issue when considering the use of elastic distances
which are characterized by a quadratic complexity along the time axis. We
present in this paper an evaluation of straightforward approaches aiming at
reducing the dimensionality of the matrix M for each movement, leading to
consider both the dimensionality reduction of the feature vector as well as its
reduction along the time axis. The dimensionality reduction of the feature
vector is achieved by selecting remarkable joints in the skeleton performing
the movement, basically the extremities of the articulatory chains composing
the skeleton. The temporal dimen-sionality reduction is achieved using either a
regular or adaptive down-sampling that seeks to minimize the reconstruction
error of the movements. Elastic and Euclidean kernels are then compared through
support vector machine learning. Two data sets 1 that are widely referenced in
the domain of human gesture recognition, and quite distinctive in terms of
quality of motion capture, are used for the experimental assessment of the
proposed approaches. On these data sets we experimentally show that it is
feasible, and possibly desirable, to significantly reduce simultaneously the
size of the feature vector and the number of skeleton frames to represent body
movements while maintaining a very good recognition rate. The method proves to
give satisfactory results at a level currently reached by state-of-the-art
methods on these data sets. We experimentally show that the computational
complexity reduction that is obtained makes this approach eligible for
real-time applications.
An incremental linear-time learning algorithm for the Optimum-Path Forest classifier
Authors: Moacir Ponti, Mateus Riva
Date: 2016-04-12 00:00:00
Tasks: ARC, General Classification

We present a classification method with incremental capabilities based on the
Optimum-Path Forest classifier (OPF). The OPF considers instances as nodes of a
fully-connected training graph, arc weights represent distances between two
feature vectors. Our algorithm includes new instances in an OPF in linear-time,
while keeping similar accuracies when compared with the original quadratic-time
model.
Tunable Sensitivity to Large Errors in Neural Network Training
Authors: Gil Keren, Sivan Sabato, Björn Schuller
Date: 2016-11-23 00:00:00
Tasks: Sensitivity

When humans learn a new concept, they might ignore examples that they cannot
make sense of at first, and only later focus on such examples, when they are
more useful for learning. We propose incorporating this idea of tunable
sensitivity for hard examples in neural network learning, using a new
generalization of the cross-entropy gradient step, which can be used in place
of the gradient in any gradient-based training method. The generalized gradient
is parameterized by a value that controls the sensitivity of the training
process to harder training examples. We tested our method on several benchmark
datasets. We propose, and corroborate in our experiments, that the optimal
level of sensitivity to hard example is positively correlated with the depth of
the network. Moreover, the test prediction error obtained by our method is
generally lower than that of the vanilla cross-entropy gradient learner. We
therefore conclude that tunable sensitivity can be helpful for neural network
learning.
Improved Image Boundaries for Better Video Segmentation
Authors: Anna Khoreva, Rodrigo Benenson, Fabio Galasso, Matthias Hein, Bernt Schiele
Date: 2016-05-12 00:00:00
Tasks: graph partitioning, Segmentation, Superpixels, Video Segmentation, Video Semantic Segmentation

Graph-based video segmentation methods rely on superpixels as starting point.
While most previous work has focused on the construction of the graph edges and
weights as well as solving the graph partitioning problem, this paper focuses
on better superpixels for video segmentation. We demonstrate by a comparative
analysis that superpixels extracted from boundaries perform best, and show that
boundary estimation can be significantly improved via image and time domain
cues. With superpixels generated from our better boundaries we observe
consistent improvement for two video segmentation methods in two different
datasets.
Deep Reinforcement Learning for Robotic Manipulation with Asynchronous Off-Policy Updates
Authors: Shixiang Gu, Ethan Holly, Timothy Lillicrap, Sergey Levine
Date: 2016-10-03 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning holds the promise of enabling autonomous robots to
learn large repertoires of behavioral skills with minimal human intervention.
However, robotic applications of reinforcement learning often compromise the
autonomy of the learning process in favor of achieving training times that are
practical for real physical systems. This typically involves introducing
hand-engineered policy representations and human-supplied demonstrations. Deep
reinforcement learning alleviates this limitation by training general-purpose
neural network policies, but applications of direct deep reinforcement learning
algorithms have so far been restricted to simulated settings and relatively
simple tasks, due to their apparent high sample complexity. In this paper, we
demonstrate that a recent deep reinforcement learning algorithm based on
off-policy training of deep Q-functions can scale to complex 3D manipulation
tasks and can learn deep neural network policies efficiently enough to train on
real physical robots. We demonstrate that the training times can be further
reduced by parallelizing the algorithm across multiple robots which pool their
policy updates asynchronously. Our experimental evaluation shows that our
method can learn a variety of 3D manipulation skills in simulation and a
complex door opening skill on real robots without any prior demonstrations or
manually designed representations.
Simple Does It: Weakly Supervised Instance and Semantic Segmentation
Authors: Anna Khoreva, Rodrigo Benenson, Jan Hosang, Matthias Hein, Bernt Schiele
Date: 2016-03-24 00:00:00
Proceeding: CVPR 2017 7
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

Semantic labelling and instance segmentation are two tasks that require
particularly costly annotations. Starting from weak supervision in the form of
bounding box detection annotations, we propose a new approach that does not
require modification of the segmentation training procedure. We show that when
carefully designing the input labels from given bounding boxes, even a single
round of training is enough to improve over previously reported weakly
supervised results. Overall, our weak supervision approach reaches ~95% of the
quality of the fully supervised model, both for semantic labelling and instance
segmentation.
Beyond knowing that: a new generation of epistemic logics
Authors: Yanjing Wang
Date: 2016-05-06 00:00:00
Tasks: Survey

Epistemic logic has become a major field of philosophical logic ever since
the groundbreaking work by Hintikka (1962). Despite its various successful
applications in theoretical computer science, AI, and game theory, the
technical development of the field has been mainly focusing on the
propositional part, i.e., the propositional modal logics of "knowing that".
However, knowledge is expressed in everyday life by using various other
locutions such as "knowing whether", "knowing what", "knowing how" and so on
(knowing-wh hereafter). Such knowledge expressions are better captured in
quantified epistemic logic, as was already discussed by Hintikka (1962) and his
sequel works at length. This paper aims to draw the attention back again to
such a fascinating but largely neglected topic. We first survey what Hintikka
and others did in the literature of quantified epistemic logic, and then
advocate a new quantifier-free approach to study the epistemic logics of
knowing-wh, which we believe can balance expressivity and complexity, and
capture the essential reasoning patterns about knowing-wh. We survey our recent
line of work on the epistemic logics of "knowing whether", "knowing what" and
"knowing how" to demonstrate the use of this new approach.
Generalized Fourier-Bessel operator and almost-periodic interpolation and approximation
Authors: Jean-Paul Gauthier, Dario Prandi
Date: 2016-11-23 00:00:00
Tasks: 2k, Image Reconstruction

We consider functions $f$ of two real variables, given as trigonometric
functions over a finite set $F$ of frequencies. This set is assumed to be
closed under rotations in the frequency plane of angle $\frac{2k\pi}{M}$ for
some integer $M$. Firstly, we address the problem of evaluating these functions
over a similar finite set $E$ in the space plane and, secondly, we address the
problems of interpolating or approximating a function $g$ of two variables by
such an $f$ over the grid $E.$ In particular, for this aim, we establish an
abstract factorization theorem for the evaluation function, which is a key
point for an efficient numerical solution to these problems. This result is
based on the very special structure of the group $SE(2,N)$, subgroup of the
group $SE(2)$ of motions of the plane corresponding to discrete rotations,
which is a maximally almost periodic group.
  Although the motivation of this paper comes from our previous works on
biomimetic image reconstruction and pattern recognition, where these questions
appear naturally, this topic is related with several classical problems: the
FFT in polar coordinates, the Non Uniform FFT, the evaluation of general
trigonometric polynomials, and so on.
Video Captioning with Transferred Semantic Attributes
Authors: Yingwei Pan, Ting Yao, Houqiang Li, Tao Mei
Date: 2016-11-23 00:00:00
Proceeding: CVPR 2017 7
Tasks: Sentence, Video Captioning

Automatically generating natural language descriptions of videos plays a
fundamental challenge for computer vision community. Most recent progress in
this problem has been achieved through employing 2-D and/or 3-D Convolutional
Neural Networks (CNN) to encode video content and Recurrent Neural Networks
(RNN) to decode a sentence. In this paper, we present Long Short-Term Memory
with Transferred Semantic Attributes (LSTM-TSA)---a novel deep architecture
that incorporates the transferred semantic attributes learnt from images and
videos into the CNN plus RNN framework, by training them in an end-to-end
manner. The design of LSTM-TSA is highly inspired by the facts that 1) semantic
attributes play a significant contribution to captioning, and 2) images and
videos carry complementary semantics and thus can reinforce each other for
captioning. To boost video captioning, we propose a novel transfer unit to
model the mutually correlated attributes learnt from images and videos.
Extensive experiments are conducted on three public datasets, i.e., MSVD, M-VAD
and MPII-MD. Our proposed LSTM-TSA achieves to-date the best published
performance in sentence generation on MSVD: 52.8% and 74.0% in terms of BLEU@4
and CIDEr-D. Superior results when compared to state-of-the-art methods are
also reported on M-VAD and MPII-MD.
Attention Correctness in Neural Image Captioning
Authors: Chenxi Liu, Junhua Mao, Fei Sha, Alan Yuille
Date: 2016-05-31 00:00:00
Tasks: Image Captioning

Attention mechanisms have recently been introduced in deep learning for
various tasks in natural language processing and computer vision. But despite
their popularity, the "correctness" of the implicitly-learned attention maps
has only been assessed qualitatively by visualization of several examples. In
this paper we focus on evaluating and improving the correctness of attention in
neural image captioning models. Specifically, we propose a quantitative
evaluation metric for the consistency between the generated attention maps and
human annotations, using recently released datasets with alignment between
regions in images and entities in captions. We then propose novel models with
different levels of explicit supervision for learning attention maps during
training. The supervision can be strong when alignment between regions and
caption entities are available, or weak when only object segments and
categories are provided. We show on the popular Flickr30k and COCO datasets
that introducing supervision of attention maps during training solidly improves
both attention correctness and caption quality, showing the promise of making
machine perception more human-like.
Learning Cost-Effective and Interpretable Regimes for Treatment Recommendation
Authors: Himabindu Lakkaraju, Cynthia Rudin
Date: 2016-11-23 00:00:00
Tasks: Diagnostic

Decision makers, such as doctors and judges, make crucial decisions such as
recommending treatments to patients, and granting bails to defendants on a
daily basis. Such decisions typically involve weighting the potential benefits
of taking an action against the costs involved. In this work, we aim to
automate this task of learning {cost-effective, interpretable and actionable
treatment regimes. We formulate this as a problem of learning a decision list
-- a sequence of if-then-else rules -- which maps characteristics of subjects
(eg., diagnostic test results of patients) to treatments. We propose a novel
objective to construct a decision list which maximizes outcomes for the
population, and minimizes overall costs. We model the problem of learning such
a list as a Markov Decision Process (MDP) and employ a variant of the Upper
Confidence Bound for Trees (UCT) strategy which leverages customized checks for
pruning the search space effectively. Experimental results on real world
observational data capturing treatment recommendations for asthma patients
demonstrate the effectiveness of our approach.
Fast Task-Specific Target Detection via Graph Based Constraints Representation and Checking
Authors: Went Luan, Yezhou Yang, Cornelia Fermuller, John S. Baras
Date: 2016-11-14 00:00:00
Tasks: Object

In this work, we present a fast target detection framework for real-world
robotics applications. Considering that an intelligent agent attends to a
task-specific object target during execution, our goal is to detect the object
efficiently. We propose the concept of early recognition, which influences the
candidate proposal process to achieve fast and reliable detection performance.
To check the target constraints efficiently, we put forward a novel policy to
generate a sub-optimal checking order, and prove that it has bounded time cost
compared to the optimal checking sequence, which is not achievable in
polynomial time. Experiments on two different scenarios: 1) rigid object and 2)
non-rigid body part detection validate our pipeline. To show that our method is
widely applicable, we further present a human-robot interaction system based on
our non-rigid body part detection.
Fine-To-Coarse Global Registration of RGB-D Scans
Authors: Maciej Halber, Thomas Funkhouser
Date: 2016-07-28 00:00:00
Proceeding: CVPR 2017 7

RGB-D scanning of indoor environments is important for many applications,
including real estate, interior design, and virtual reality. However, it is
still challenging to register RGB-D images from a hand-held camera over a long
video sequence into a globally consistent 3D model. Current methods often can
lose tracking or drift and thus fail to reconstruct salient structures in large
environments (e.g., parallel walls in different rooms). To address this
problem, we propose a "fine-to-coarse" global registration algorithm that
leverages robust registrations at finer scales to seed detection and
enforcement of new correspondence and structural constraints at coarser scales.
To test global registration algorithms, we provide a benchmark with 10,401
manually-clicked point correspondences in 25 scenes from the SUN3D dataset.
During experiments with this benchmark, we find that our fine-to-coarse
algorithm registers long RGB-D sequences better than previous methods.
Interpretation of Prediction Models Using the Input Gradient
Authors: Yotam Hechtlinger
Date: 2016-11-23 00:00:00
Tasks: General Classification, Prediction, regression

State of the art machine learning algorithms are highly optimized to provide
the optimal prediction possible, naturally resulting in complex models. While
these models often outperform simpler more interpretable models by order of
magnitudes, in terms of understanding the way the model functions, we are often
facing a "black box".
  In this paper we suggest a simple method to interpret the behavior of any
predictive model, both for regression and classification. Given a particular
model, the information required to interpret it can be obtained by studying the
partial derivatives of the model with respect to the input. We exemplify this
insight by interpreting convolutional and multi-layer neural networks in the
field of natural language processing.
SyGuS-Comp 2016: Results and Analysis
Authors: Rajeev Alur, Dana Fisman, Rishabh Singh, Armando Solar-Lezama
Date: 2016-11-23 00:00:00

Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an
implementation f that meets both a semantic constraint given by a logical
formula $\varphi$ in a background theory T, and a syntactic constraint given by
a grammar G, which specifies the allowed set of candidate implementations. Such
a synthesis problem can be formally defined in SyGuS-IF, a language that is
built on top of SMT-LIB.
  The Syntax-Guided Synthesis Competition (SyGuS-Comp) is an effort to
facilitate, bring together and accelerate research and development of efficient
solvers for SyGuS by providing a platform for evaluating different synthesis
techniques on a comprehensive set of benchmarks. In this year's competition we
added a new track devoted to programming by examples. This track consisted of
two categories, one using the theory of bit-vectors and one using the theory of
strings. This paper presents and analyses the results of SyGuS-Comp'16.
Efficient Delivery Policy to Minimize User Traffic Consumption in Guaranteed Advertising
Authors: Jia Zhang, Zheng Wang, Qian Li, Jialin Zhang, Yanyan Lan, Qiang Li, Xiaoming Sun
Date: 2016-11-23 00:00:00

In this work, we study the guaranteed delivery model which is widely used in
online display advertising. In the guaranteed delivery scenario, ad exposures
(which are also called impressions in some works) to users are guaranteed by
contracts signed in advance between advertisers and publishers. A crucial
problem for the advertising platform is how to fully utilize the valuable user
traffic to generate as much as possible revenue.
  Different from previous works which usually minimize the penalty of
unsatisfied contracts and some other cost (e.g. representativeness), we propose
the novel consumption minimization model, in which the primary objective is to
minimize the user traffic consumed to satisfy all contracts. Under this model,
we develop a near optimal method to deliver ads for users. The main advantage
of our method lies in that it consumes nearly as least as possible user traffic
to satisfy all contracts, therefore more contracts can be accepted to produce
more revenue. It also enables the publishers to estimate how much user traffic
is redundant or short so that they can sell or buy this part of traffic in bulk
in the exchange market. Furthermore, it is robust with regard to priori
knowledge of user type distribution. Finally, the simulation shows that our
method outperforms the traditional state-of-the-art methods.
Programs as Black-Box Explanations
Authors: Sameer Singh, Marco Tulio Ribeiro, Carlos Guestrin
Date: 2016-11-22 00:00:00
Tasks: Program induction

Recent work in model-agnostic explanations of black-box machine learning has
demonstrated that interpretability of complex models does not have to come at
the cost of accuracy or model flexibility. However, it is not clear what kind
of explanations, such as linear models, decision trees, and rule lists, are the
appropriate family to consider, and different tasks and models may benefit from
different kinds of explanations. Instead of picking a single family of
representations, in this work we propose to use "programs" as model-agnostic
explanations. We show that small programs can be expressive yet intuitive as
explanations, and generalize over a number of existing interpretable families.
We propose a prototype program induction method based on simulated annealing
that approximates the local behavior of black-box classifiers around a specific
prediction using random perturbations. Finally, we present preliminary
application on small datasets and show that the generated explanations are
intuitive and accurate for a number of classifiers.
Relaxed Earth Mover's Distances for Chain- and Tree-connected Spaces and their use as a Loss Function in Deep Learning
Authors: Manuel Martinez, Monica Haurilet, Ziad Al-Halah, Makarand Tapaswi, Rainer Stiefelhagen
Date: 2016-11-22 00:00:00
Tasks: Deep Learning, Small Data Image Classification

The Earth Mover's Distance (EMD) computes the optimal cost of transforming
one distribution into another, given a known transport metric between them. In
deep learning, the EMD loss allows us to embed information during training
about the output space structure like hierarchical or semantic relations. This
helps in achieving better output smoothness and generalization. However EMD is
computationally expensive.Moreover, solving EMD optimization problems usually
require complex techniques like lasso. These properties limit the applicability
of EMD-based approaches in large scale machine learning.
  We address in this work the difficulties facing incorporation of EMD-based
loss in deep learning frameworks. Additionally, we provide insight and novel
solutions on how to integrate such loss function in training deep neural
networks. Specifically, we make three main contributions: (i) we provide an
in-depth analysis of the fastest state-of-the-art EMD algorithm (Sinkhorn
Distance) and discuss its limitations in deep learning scenarios. (ii) we
derive fast and numerically stable closed-form solutions for the EMD gradient
in output spaces with chain- and tree- connectivity; and (iii) we propose a
relaxed form of the EMD gradient with equivalent computational complexity but
faster convergence rate. We support our claims with experiments on real
datasets. In a restricted data setting on the ImageNet dataset, we train a
model to classify 1000 categories using 50K images, and demonstrate that our
relaxed EMD loss achieves better Top-1 accuracy than the cross entropy loss.
Overall, we show that our relaxed EMD loss criterion is a powerful asset for
deep learning in the small data regime.