pwc-archive/papers-with-abstracts huggingface dataset:
Event Representation through Semantic Roles: Evaluation of Coverage
Authors: Aliaksandr Huminski, Hao Zhang
Date: 2018-10-09 00:00:00

Semantic role theory is a widely used approach for event representation. Yet,
there are multiple indications that semantic role paradigm is necessary but not
sufficient to cover all elements of event structure. We conducted an analysis
of semantic role representation for events to provide an empirical evidence of
insufficiency. The consequence of that is a hybrid role-scalar approach. The
results are considered as preliminary in investigation of semantic roles
coverage for event representation.
Towards Verifying Semantic Roles Co-occurrence
Authors: Aliaksandr Huminski, Hao Zhang, Gangeshwar Krishnamurthy
Date: 2018-10-09 00:00:00

Semantic role theory considers roles as a small universal set of unanalyzed
entities. It means that formally there are no restrictions on role
combinations. We argue that the semantic roles co-occur in verb
representations. It means that there are hidden restrictions on role
combinations. To demonstrate that a practical and evidence-based approach has
been built on in-depth analysis of the largest verb database VerbNet. The
consequences of this approach are considered.
Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation
Authors: Mina Rezaei, Haojin Yang, Christoph Meinel
Date: 2018-10-09 00:00:00
Tasks: Cell Segmentation, Ensemble Learning, Lesion Segmentation, Segmentation, Semantic Segmentation

We propose a new generative adversarial architecture to mitigate imbalance
data problem in medical image semantic segmentation where the majority of
pixels belongs to a healthy region and few belong to lesion or non-health
region. A model trained with imbalanced data tends to bias toward healthy data
which is not desired in clinical applications and predicted outputs by these
networks have high precision and low sensitivity. We propose a new conditional
generative refinement network with three components: a generative, a
discriminative, and a refinement network to mitigate unbalanced data problem
through ensemble learning. The generative network learns to a segment at the
pixel level by getting feedback from the discriminative network according to
the true positive and true negative maps. On the other hand, the refinement
network learns to predict the false positive and the false negative masks
produced by the generative network that has significant value, especially in
medical application. The final semantic segmentation masks are then composed by
the output of the three networks. The proposed architecture shows
state-of-the-art results on LiTS-2017 for liver lesion segmentation, and two
microscopic cell segmentation datasets MDA231, PhC-HeLa. We have achieved
competitive results on BraTS-2017 for brain tumour segmentation.
Functionally Modular and Interpretable Temporal Filtering for Robust Segmentation
Authors: Jörg Wagner, Volker Fischer, Michael Herman, Sven Behnke
Date: 2018-10-09 00:00:00

The performance of autonomous systems heavily relies on their ability to
generate a robust representation of the environment. Deep neural networks have
greatly improved vision-based perception systems but still fail in challenging
situations, e.g. sensor outages or heavy weather. These failures are often
introduced by data-inherent perturbations, which significantly reduce the
information provided to the perception system. We propose a functionally
modularized temporal filter, which stabilizes an abstract feature
representation of a single-frame segmentation model using information of
previous time steps. Our filter module splits the filter task into multiple
less complex and more interpretable subtasks. The basic structure of the filter
is inspired by a Bayes estimator consisting of a prediction and an update step.
To make the prediction more transparent, we implement it using a geometric
projection and estimate its parameters. This additionally enables the
decomposition of the filter task into static representation filtering and
low-dimensional motion filtering. Our model can cope with missing frames and is
trainable in an end-to-end fashion. Using photorealistic, synthetic video data,
we show the ability of the proposed architecture to overcome data-inherent
perturbations. The experiments especially highlight advantages introduced by an
interpretable and explicit filter module.
Deep Attentive Tracking via Reciprocative Learning
Authors: Shi Pu, Yibing Song, Chao Ma, Honggang Zhang, Ming-Hsuan Yang
Date: 2018-10-09 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Visual Tracking

Visual attention, derived from cognitive neuroscience, facilitates human
perception on the most pertinent subset of the sensory data. Recently,
significant efforts have been made to exploit attention schemes to advance
computer vision systems. For visual tracking, it is often challenging to track
target objects undergoing large appearance changes. Attention maps facilitate
visual tracking by selectively paying attention to temporal robust features.
Existing tracking-by-detection approaches mainly use additional attention
modules to generate feature weights as the classifiers are not equipped with
such mechanisms. In this paper, we propose a reciprocative learning algorithm
to exploit visual attention for training deep classifiers. The proposed
algorithm consists of feed-forward and backward operations to generate
attention maps, which serve as regularization terms coupled with the original
classification loss function for training. The deep classifier learns to attend
to the regions of target objects robust to appearance changes. Extensive
experiments on large-scale benchmark datasets show that the proposed attentive
tracking method performs favorably against the state-of-the-art approaches.
Realizing Learned Quadruped Locomotion Behaviors through Kinematic Motion Primitives
Authors: Abhik Singla, Shounak Bhattacharya, Dhaivat Dholakiya, Shalabh Bhatnagar, Ashitava Ghosal, Bharadwaj Amrutur, Shishir Kolathaya
Date: 2018-10-09 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

Humans and animals are believed to use a very minimal set of trajectories to
perform a wide variety of tasks including walking. Our main objective in this
paper is two fold 1) Obtain an effective tool to realize these basic motion
patterns for quadrupedal walking, called the kinematic motion primitives
(kMPs), via trajectories learned from deep reinforcement learning (D-RL) and 2)
Realize a set of behaviors, namely trot, walk, gallop and bound from these
kinematic motion primitives in our custom four legged robot, called the
`Stoch'. D-RL is a data driven approach, which has been shown to be very
effective for realizing all kinds of robust locomotion behaviors, both in
simulation and in experiment. On the other hand, kMPs are known to capture the
underlying structure of walking and yield a set of derived behaviors. We first
generate walking gaits from D-RL, which uses policy gradient based approaches.
We then analyze the resulting walking by using principal component analysis. We
observe that the kMPs extracted from PCA followed a similar pattern
irrespective of the type of gaits generated. Leveraging on this underlying
structure, we then realize walking in Stoch by a straightforward reconstruction
of joint trajectories from kMPs. This type of methodology improves the
transferability of these gaits to real hardware, lowers the computational
overhead on-board, and also avoids multiple training iterations by generating a
set of derived behaviors from a single learned gait.
Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional $\ell_1$-Balls via Envelope Complexity
Authors: Kohei Miyaguchi, Kenji Yamanishi
Date: 2018-10-09 00:00:00

We develop a new theoretical framework, the \emph{envelope complexity}, to
analyze the minimax regret with logarithmic loss functions and derive a
Bayesian predictor that adaptively achieves the minimax regret over
high-dimensional $\ell_1$-balls within a factor of two. The prior is newly
derived for achieving the minimax regret and called the
\emph{spike-and-tails~(ST) prior} as it looks like. The resulting regret bound
is so simple that it is completely determined with the smoothness of the loss
function and the radius of the balls except with logarithmic factors, and it
has a generalized form of existing regret/risk bounds. In the preliminary
experiment, we confirm that the ST prior outperforms the conventional
minimax-regret prior under non-high-dimensional asymptotics.
Knowing Where to Look? Analysis on Attention of Visual Question Answering System
Authors: Wei Li, Zehuan Yuan, Xiangzhong Fang, Changhu Wang
Date: 2018-10-09 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Attention mechanisms have been widely used in Visual Question Answering (VQA)
solutions due to their capacity to model deep cross-domain interactions.
Analyzing attention maps offers us a perspective to find out limitations of
current VQA systems and an opportunity to further improve them. In this paper,
we select two state-of-the-art VQA approaches with attention mechanisms to
study their robustness and disadvantages by visualizing and analyzing their
estimated attention maps. We find that both methods are sensitive to features,
and simultaneously, they perform badly for counting and multi-object related
questions. We believe that the findings and analytical method will help
researchers identify crucial challenges on the way to improve their own VQA
systems.
Learning Bounds for Greedy Approximation with Explicit Feature Maps from Multiple Kernels
Authors: Shahin Shahrampour, Vahid Tarokh
Date: 2018-10-09 00:00:00
Proceeding: NeurIPS 2018 12

Nonlinear kernels can be approximated using finite-dimensional feature maps
for efficient risk minimization. Due to the inherent trade-off between the
dimension of the (mapped) feature space and the approximation accuracy, the key
problem is to identify promising (explicit) features leading to a satisfactory
out-of-sample performance. In this work, we tackle this problem by efficiently
choosing such features from multiple kernels in a greedy fashion. Our method
sequentially selects these explicit features from a set of candidate features
using a correlation metric. We establish an out-of-sample error bound capturing
the trade-off between the error in terms of explicit features (approximation
error) and the error due to spectral properties of the best model in the
Hilbert space associated to the combined kernel (spectral error). The result
verifies that when the (best) underlying data model is sparse enough, i.e., the
spectral error is negligible, one can control the test error with a small
number of explicit features, that can scale poly-logarithmically with data. Our
empirical results show that given a fixed number of explicit features, the
method can achieve a lower test error with a smaller time cost, compared to the
state-of-the-art in data-dependent random features.
SNAP: A semismooth Newton algorithm for pathwise optimization with optimal local convergence rate and oracle properties
Authors: Jian Huang, Yuling Jiao, Xiliang Lu, Yueyong Shi, Qinglong Yang
Date: 2018-10-09 00:00:00
Tasks: regression

We propose a semismooth Newton algorithm for pathwise optimization (SNAP) for
the LASSO and Enet in sparse, high-dimensional linear regression. SNAP is
derived from a suitable formulation of the KKT conditions based on Newton
derivatives. It solves the semismooth KKT equations efficiently by actively and
continuously seeking the support of the regression coefficients along the
solution path with warm start. At each knot in the path, SNAP converges locally
superlinearly for the Enet criterion and achieves an optimal local convergence
rate for the LASSO criterion, i.e., SNAP converges in one step at the cost of
two matrix-vector multiplication per iteration. Under certain regularity
conditions on the design matrix and the minimum magnitude of the nonzero
elements of the target regression coefficients, we show that SNAP hits a
solution with the same signs as the regression coefficients and achieves a
sharp estimation error bound in finite steps with high probability. The
computational complexity of SNAP is shown to be the same as that of LARS and
coordinate descent algorithms per iteration. Simulation studies and real data
analysis support our theoretical results and demonstrate that SNAP is faster
and accurate than LARS and coordinate descent algorithms.
The Adversarial Attack and Detection under the Fisher Information Metric
Authors: Chenxiao Zhao, P. Thomas Fletcher, Mixue Yu, Yaxin Peng, Guixu Zhang, Chaomin Shen
Date: 2018-10-09 00:00:00
Tasks: Adversarial Attack

Many deep learning models are vulnerable to the adversarial attack, i.e.,
imperceptible but intentionally-designed perturbations to the input can cause
incorrect output of the networks. In this paper, using information geometry, we
provide a reasonable explanation for the vulnerability of deep learning models.
By considering the data space as a non-linear space with the Fisher information
metric induced from a neural network, we first propose an adversarial attack
algorithm termed one-step spectral attack (OSSA). The method is described by a
constrained quadratic form of the Fisher information matrix, where the optimal
adversarial perturbation is given by the first eigenvector, and the model
vulnerability is reflected by the eigenvalues. The larger an eigenvalue is, the
more vulnerable the model is to be attacked by the corresponding eigenvector.
Taking advantage of the property, we also propose an adversarial detection
method with the eigenvalues serving as characteristics. Both our attack and
detection algorithms are numerically optimized to work efficiently on large
datasets. Our evaluations show superior performance compared with other
methods, implying that the Fisher information is a promising approach to
investigate the adversarial attacks and defenses.
What made you do this? Understanding black-box decisions with sufficient input subsets
Authors: Brandon Carter, Jonas Mueller, Siddhartha Jain, David Gifford
Date: 2018-10-09 00:00:00
Tasks: Decision Making

Local explanation frameworks aim to rationalize particular decisions made by
a black-box prediction model. Existing techniques are often restricted to a
specific type of predictor or based on input saliency, which may be undesirably
sensitive to factors unrelated to the model's decision making process. We
instead propose sufficient input subsets that identify minimal subsets of
features whose observed values alone suffice for the same decision to be
reached, even if all other input feature values are missing. General principles
that globally govern a model's decision-making can also be revealed by
searching for clusters of such input patterns across many data points. Our
approach is conceptually straightforward, entirely model-agnostic, simply
implemented using instance-wise backward selection, and able to produce more
concise rationales than existing techniques. We demonstrate the utility of our
interpretation method on various neural network models trained on text, image,
and genomic data.
The Outer Product Structure of Neural Network Derivatives
Authors: Craig Bakker, Michael J. Henry, Nathan O. Hodas
Date: 2018-10-09 00:00:00

In this paper, we show that feedforward and recurrent neural networks exhibit
an outer product derivative structure but that convolutional neural networks do
not. This structure makes it possible to use higher-order information without
needing approximations or infeasibly large amounts of memory, and it may also
provide insights into the geometry of neural network optima. The ability to
easily access these derivatives also suggests a new, geometric approach to
regularization. We then discuss how this structure could be used to improve
training methods, increase network robustness and generalizability, and inform
network compression methods.
Visual Localization of Key Positions for Visually Impaired People
Authors: Ruiqi Cheng, Kaiwei Wang, Longqing Lin, Kailun Yang
Date: 2018-10-09 00:00:00
Tasks: Visual Localization

On the off-the-shelf navigational assistance devices, the localization
precision is limited to the signal error of global navigation satellite system
(GNSS). During travelling outdoors, the inaccurately localization perplexes
visually impaired people, especially at key positions, such as gates, bus
stations or intersections. The visual localization is a feasible approach to
improving the positioning precision of assistive devices. Using multiple image
descriptors, the paper proposes a robust and efficient visual localization
algorithm, which takes advantage of priori GNSS signals and multi-modal images
to achieve the accurate localization of key positions. In the experiments, we
implement the approach on the wearable system and test the performance of
visual localization under practical scenarios.
Information Geometry of Orthogonal Initializations and Training
Authors: Piotr A. Sokol, Il Memming Park
Date: 2018-10-09 00:00:00
Proceeding: ICLR 2020 1

Recently mean field theory has been successfully used to analyze properties of wide, random neural networks. It gave rise to a prescriptive theory for initializing feed-forward neural networks with orthogonal weights, which ensures that both the forward propagated activations and the backpropagated gradients are near $\ell_2$ isometries and as a consequence training is orders of magnitude faster. Despite strong empirical performance, the mechanisms by which critical initializations confer an advantage in the optimization of deep neural networks are poorly understood. Here we show a novel connection between the maximum curvature of the optimization landscape (gradient smoothness) as measured by the Fisher information matrix (FIM) and the spectral radius of the input-output Jacobian, which partially explains why more isometric networks can train much faster. Furthermore, given that orthogonal weights are necessary to ensure that gradient norms are approximately preserved at initialization, we experimentally investigate the benefits of maintaining orthogonality throughout training, from which we conclude that manifold optimization of weights performs well regardless of the smoothness of the gradients. Moreover, motivated by experimental results we show that a low condition number of the FIM is not predictive of faster learning.
Unsupervised Online Video Object Segmentation with Motion Property Understanding
Authors: Tao Zhuo, Zhiyong Cheng, Peng Zhang, Yongkang Wong, Mohan Kankanhalli
Date: 2018-10-09 00:00:00
Proceeding: IEEE Transactions on Image Processing 2019 8
Tasks: Motion Detection, Object, Segmentation, Semantic Segmentation, Unsupervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

Unsupervised video object segmentation aims to automatically segment moving objects over an unconstrained video without any user annotation. So far, only few unsupervised online methods have been reported in literature and their performance is still far from satisfactory, because the complementary information from future frames cannot be processed under online setting. To solve this challenging problem, in this paper, we propose a novel Unsupervised Online Video Object Segmentation (UOVOS) framework by construing the motion property to mean moving in concurrence with a generic object for segmented regions. By incorporating salient motion detection and object proposal, a pixel-wise fusion strategy is developed to effectively remove detection noise such as dynamic background and stationary objects. Furthermore, by leveraging the obtained segmentation from immediately preceding frames, a forward propagation algorithm is employed to deal with unreliable motion detection and object proposals. Experimental results on several benchmark datasets demonstrate the efficacy of the proposed method. Compared to the state-of-the-art unsupervised online segmentation algorithms, the proposed method achieves an absolute gain of 6.2%. Moreover, our method achieves better performance than the best unsupervised offline algorithm on the DAVIS-2016 benchmark dataset. Our code is available on the project website: https://github.com/visiontao/uovos.
Reinforcement Learning for Improving Agent Design
Authors: David Ha
Date: 2018-10-09 00:00:00
Tasks: OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In many reinforcement learning tasks, the goal is to learn a policy to manipulate an agent, whose design is fixed, to maximize some notion of cumulative reward. The design of the agent's physical structure is rarely optimized for the task at hand. In this work, we explore the possibility of learning a version of the agent's design that is better suited for its task, jointly with the policy. We propose an alteration to the popular OpenAI Gym framework, where we parameterize parts of an environment, and allow an agent to jointly learn to modify these environment parameters along with its policy. We demonstrate that an agent can learn a better structure of its body that is not only better suited for the task, but also facilitates policy learning. Joint learning of policy and structure may even uncover design principles that are useful for assisted-design applications. Videos of results at https://designrl.github.io/
Skeleton Driven Non-rigid Motion Tracking and 3D Reconstruction
Authors: Shafeeq Elanattil, Peyman Moghadam, Simon Denman, Sridha Sridharan, Clinton Fookes
Date: 2018-10-09 00:00:00
Tasks: 3D Reconstruction

This paper presents a method which can track and 3D reconstruct the non-rigid
surface motion of human performance using a moving RGB-D camera. 3D
reconstruction of marker-less human performance is a challenging problem due to
the large range of articulated motions and considerable non-rigid deformations.
Current approaches use local optimization for tracking. These methods need many
iterations to converge and may get stuck in local minima during sudden
articulated movements. We propose a puppet model-based tracking approach using
skeleton prior, which provides a better initialization for tracking articulated
movements. The proposed approach uses an aligned puppet model to estimate
correct correspondences for human performance capture. We also contribute a
synthetic dataset which provides ground truth locations for frame-by-frame
geometry and skeleton joints of human subjects. Experimental results show that
our approach is more robust when faced with sudden articulated motions, and
provides better 3D reconstruction compared to the existing state-of-the-art
approaches.
Average Margin Regularization for Classifiers
Authors: Matt Olfat, Anil Aswani
Date: 2018-10-09 00:00:00
Tasks: Adversarial Robustness

Adversarial robustness has become an important research topic given empirical demonstrations on the lack of robustness of deep neural networks. Unfortunately, recent theoretical results suggest that adversarial training induces a strict tradeoff between classification accuracy and adversarial robustness. In this paper, we propose and then study a new regularization for any margin classifier or deep neural network. We motivate this regularization by a novel generalization bound that shows a tradeoff in classifier accuracy between maximizing its margin and average margin. We thus call our approach an average margin (AM) regularization, and it consists of a linear term added to the objective. We theoretically show that for certain distributions AM regularization can both improve classifier accuracy and robustness to adversarial attacks. We conclude by using both synthetic and real data to empirically show that AM regularization can strictly improve both accuracy and robustness for support vector machine's (SVM's), relative to unregularized classifiers and adversarially trained classifiers.