pwc-archive/papers-with-abstracts huggingface dataset:
Person re-identification with fusion of hand-crafted and deep pose-based body region features
Authors: Jubin Johnson, Shunsuke Yasugi, Yoichi Sugino, Sugiri Pranata, ShengMei Shen
Date: 2018-03-27 00:00:00
Tasks: Metric Learning, Person Re-Identification

Person re-identification (re-ID) aims to accurately re- trieve a person from
a large-scale database of images cap- tured across multiple cameras. Existing
works learn deep representations using a large training subset of unique per-
sons. However, identifying unseen persons is critical for a good re-ID
algorithm. Moreover, the misalignment be- tween person crops to detection
errors or pose variations leads to poor feature matching. In this work, we
present a fusion of handcrafted features and deep feature representa- tion
learned using multiple body parts to complement the global body features that
achieves high performance on un- seen test images. Pose information is used to
detect body regions that are passed through Convolutional Neural Net- works
(CNN) to guide feature learning. Finally, a metric learning step enables robust
distance matching on a dis- criminative subspace. Experimental results on 4
popular re-ID benchmark datasets namely VIPer, DukeMTMC-reID, Market-1501 and
CUHK03 show that the proposed method achieves state-of-the-art performance in
image-based per- son re-identification.
A Divide-and-Conquer Approach to Compressed Sensing MRI
Authors: Liyan Sun, Zhiwen Fan, Xinghao Ding, Congbo Cai, Yue Huang, John Paisley
Date: 2018-03-27 00:00:00
Tasks: compressed sensing

Compressed sensing (CS) theory assures us that we can accurately reconstruct
magnetic resonance images using fewer k-space measurements than the Nyquist
sampling rate requires. In traditional CS-MRI inversion methods, the fact that
the energy within the Fourier measurement domain is distributed non-uniformly
is often neglected during reconstruction. As a result, more densely sampled
low-frequency information tends to dominate penalization schemes for
reconstructing MRI at the expense of high-frequency details. In this paper, we
propose a new framework for CS-MRI inversion in which we decompose the observed
k-space data into "subspaces" via sets of filters in a lossless way, and
reconstruct the images in these various spaces individually using off-the-shelf
algorithms. We then fuse the results to obtain the final reconstruction. In
this way we are able to focus reconstruction on frequency information within
the entire k-space more equally, preserving both high and low frequency
details. We demonstrate that the proposed framework is competitive with
state-of-the-art methods in CS-MRI in terms of quantitative performance, and
often improves an algorithm's results qualitatively compared with it's direct
application to k-space.
Compassionately Conservative Balanced Cuts for Image Segmentation
Authors: Nathan D. Cahill, Tyler L. Hayes, Renee T. Meinhold, John F. Hamilton
Date: 2018-03-27 00:00:00
Proceeding: CVPR 2018 6
Tasks: Clustering, graph partitioning, Image Segmentation, Segmentation, Semantic Segmentation

The Normalized Cut (NCut) objective function, widely used in data clustering
and image segmentation, quantifies the cost of graph partitioning in a way that
biases clusters or segments that are balanced towards having lower values than
unbalanced partitionings. However, this bias is so strong that it avoids any
singleton partitions, even when vertices are very weakly connected to the rest
of the graph. Motivated by the B\"uhler-Hein family of balanced cut costs, we
propose the family of Compassionately Conservative Balanced (CCB) Cut costs,
which are indexed by a parameter that can be used to strike a compromise
between the desire to avoid too many singleton partitions and the notion that
all partitions should be balanced. We show that CCB-Cut minimization can be
relaxed into an orthogonally constrained $\ell_{\tau}$-minimization problem
that coincides with the problem of computing Piecewise Flat Embeddings (PFE)
for one particular index value, and we present an algorithm for solving the
relaxed problem by iteratively minimizing a sequence of reweighted Rayleigh
quotients (IRRQ). Using images from the BSDS500 database, we show that image
segmentation based on CCB-Cut minimization provides better accuracy with
respect to ground truth and greater variability in region size than NCut-based
image segmentation.
Mittens: An Extension of GloVe for Learning Domain-Specialized Representations
Authors: Nicholas Dingwall, Christopher Potts
Date: 2018-03-27 00:00:00
Proceeding: NAACL 2018 6
Tasks: Representation Learning

We present a simple extension of the GloVe representation learning model that
begins with general-purpose representations and updates them based on data from
a specialized domain. We show that the resulting representations can lead to
faster learning and better results on a variety of tasks.
NestedNet: Learning Nested Sparse Structures in Deep Neural Networks
Authors: Eunwoo Kim, Chanho Ahn, Songhwai Oh
Date: 2017-12-11 00:00:00
Proceeding: CVPR 2018 6
Tasks: Knowledge Distillation, Scheduling

Recently, there have been increasing demands to construct compact deep
architectures to remove unnecessary redundancy and to improve the inference
speed. While many recent works focus on reducing the redundancy by eliminating
unneeded weight parameters, it is not possible to apply a single deep
architecture for multiple devices with different resources. When a new device
or circumstantial condition requires a new deep architecture, it is necessary
to construct and train a new network from scratch. In this work, we propose a
novel deep learning framework, called a nested sparse network, which exploits
an n-in-1-type nested structure in a neural network. A nested sparse network
consists of multiple levels of networks with a different sparsity ratio
associated with each level, and higher level networks share parameters with
lower level networks to enable stable nested learning. The proposed framework
realizes a resource-aware versatile architecture as the same network can meet
diverse resource requirements. Moreover, the proposed nested network can learn
different forms of knowledge in its internal networks at different levels,
enabling multiple tasks using a single network, such as coarse-to-fine
hierarchical classification. In order to train the proposed nested sparse
network, we propose efficient weight connection learning and channel and layer
scheduling strategies. We evaluate our network in multiple tasks, including
adaptive deep compression, knowledge distillation, and learning class
hierarchy, and demonstrate that nested sparse networks perform competitively,
but more efficiently, compared to existing methods.
Demystifying Differentiable Programming: Shift/Reset the Penultimate Backpropagator
Authors: Fei Wang, Daniel Zheng, James Decker, Xilun Wu, Grégory M. Essertel, Tiark Rompf
Date: 2018-03-27 00:00:00
Tasks: Machine Translation, Translation

Deep learning has seen tremendous success over the past decade in computer vision, machine translation, and gameplay. This success rests in crucial ways on gradient-descent optimization and the ability to learn parameters of a neural network by backpropagating observed errors. However, neural network architectures are growing increasingly sophisticated and diverse, which motivates an emerging quest for even more general forms of differentiable programming, where arbitrary parameterized computations can be trained by gradient descent. In this paper, we take a fresh look at automatic differentiation (AD) techniques, and especially aim to demystify the reverse-mode form of AD that generalizes backpropagation in neural networks. We uncover a tight connection between reverse-mode AD and delimited continuations, which permits implementing reverse-mode AD purely via operator overloading and without any auxiliary data structures. We further show how this formulation of AD can be fruitfully combined with multi-stage programming (staging), leading to a highly efficient implementation that combines the performance benefits of deep learning frameworks based on explicit reified computation graphs (e.g., TensorFlow) with the expressiveness of pure library approaches (e.g., PyTorch).
Forward-Backward Reinforcement Learning
Authors: Ashley D. Edwards, Laura Downs, James C. Davidson
Date: 2018-03-27 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Goals for reinforcement learning problems are typically defined through
hand-specified rewards. To design such problems, developers of learning
algorithms must inherently be aware of what the task goals are, yet we often
require agents to discover them on their own without any supervision beyond
these sparse rewards. While much of the power of reinforcement learning derives
from the concept that agents can learn with little guidance, this requirement
greatly burdens the training process. If we relax this one restriction and
endow the agent with knowledge of the reward function, and in particular of the
goal, we can leverage backwards induction to accelerate training. To achieve
this, we propose training a model to learn to take imagined reversal steps from
known goal states. Rather than training an agent exclusively to determine how
to reach a goal while moving forwards in time, our approach travels backwards
to jointly predict how we got there. We evaluate our work in Gridworld and
Towers of Hanoi and empirically demonstrate that it yields better performance
than standard DDQN.
MIS-SLAM: Real-time Large Scale Dense Deformable SLAM System in Minimal Invasive Surgery Based on Heterogeneous Computing
Authors: Jingwei Song, Jun Wang, Liang Zhao, Shoudong Huang, Gamini Dissanayake
Date: 2018-03-06 00:00:00
Tasks: CPU, GPU

Real-time simultaneously localization and dense mapping is very helpful for
providing Virtual Reality and Augmented Reality for surgeons or even surgical
robots. In this paper, we propose MIS-SLAM: a complete real-time large scale
dense deformable SLAM system with stereoscope in Minimal Invasive Surgery based
on heterogeneous computing by making full use of CPU and GPU. Idled CPU is used
to perform ORB- SLAM for providing robust global pose. Strategies are taken to
integrate modules from CPU and GPU. We solved the key problem raised in
previous work, that is, fast movement of scope and blurry images make the scope
tracking fail. Benefiting from improved localization, MIS-SLAM can achieve
large scale scope localizing and dense mapping in real-time. It transforms and
deforms current model and incrementally fuses new observation while keeping
vivid texture. In-vivo experiments conducted on publicly available datasets
presented in the form of videos demonstrate the feasibility and practicality of
MIS-SLAM for potential clinical purpose.
MLE-induced Likelihood for Markov Random Fields
Authors: Jie Liu, Hao Zheng
Date: 2018-03-27 00:00:00

Due to the intractable partition function, the exact likelihood function for
a Markov random field (MRF), in many situations, can only be approximated.
Major approximation approaches include pseudolikelihood and Laplace
approximation. In this paper, we propose a novel way of approximating the
likelihood function through first approximating the marginal likelihood
functions of individual parameters and then reconstructing the joint likelihood
function from these marginal likelihood functions. For approximating the
marginal likelihood functions, we derive a particular likelihood function from
a modified scenario of coin tossing which is useful for capturing how one
parameter interacts with the remaining parameters in the likelihood function.
For reconstructing the joint likelihood function, we use an appropriate copula
to link up these marginal likelihood functions. Numerical investigation
suggests the superior performance of our approach. Especially as the size of
the MRF increases, both the numerical performance and the computational cost of
our approach remain consistently satisfactory, whereas Laplace approximation
deteriorates and pseudolikelihood becomes computationally unbearable.
Diversity Regularized Spatiotemporal Attention for Video-based Person Re-identification
Authors: Shuang Li, Slawomir Bak, Peter Carr, Xiaogang Wang
Date: 2018-03-27 00:00:00
Proceeding: CVPR 2018 6
Tasks: Diversity, Person Re-Identification, Video-Based Person Re-Identification

Video-based person re-identification matches video clips of people across
non-overlapping cameras. Most existing methods tackle this problem by encoding
each video frame in its entirety and computing an aggregate representation
across all frames. In practice, people are often partially occluded, which can
corrupt the extracted features. Instead, we propose a new spatiotemporal
attention model that automatically discovers a diverse set of distinctive body
parts. This allows useful information to be extracted from all frames without
succumbing to occlusions and misalignments. The network learns multiple spatial
attention models and employs a diversity regularization term to ensure multiple
models do not discover the same body part. Features extracted from local image
regions are organized by spatial attention model and are combined using
temporal attention. As a result, the network learns latent representations of
the face, torso and other body parts using the best available image patches
from the entire video sequence. Extensive evaluations on three datasets show
that our framework outperforms the state-of-the-art approaches by large margins
on multiple metrics.
Attacking the Madry Defense Model with $L_1$-based Adversarial Examples
Authors: Yash Sharma, Pin-Yu Chen
Date: 2017-10-30 00:00:00

The Madry Lab recently hosted a competition designed to test the robustness
of their adversarially trained MNIST model. Attacks were constrained to perturb
each pixel of the input image by a scaled maximal $L_\infty$ distortion
$\epsilon$ = 0.3. This discourages the use of attacks which are not optimized
on the $L_\infty$ distortion metric. Our experimental results demonstrate that
by relaxing the $L_\infty$ constraint of the competition, the elastic-net
attack to deep neural networks (EAD) can generate transferable adversarial
examples which, despite their high average $L_\infty$ distortion, have minimal
visual distortion. These results call into question the use of $L_\infty$ as a
sole measure for visual distortion, and further demonstrate the power of EAD at
generating robust adversarial examples.
Bypassing Feature Squeezing by Increasing Adversary Strength
Authors: Yash Sharma, Pin-Yu Chen
Date: 2018-03-27 00:00:00

Feature Squeezing is a recently proposed defense method which reduces the
search space available to an adversary by coalescing samples that correspond to
many different feature vectors in the original space into a single sample. It
has been shown that feature squeezing defenses can be combined in a joint
detection framework to achieve high detection rates against state-of-the-art
attacks. However, we demonstrate on the MNIST and CIFAR-10 datasets that by
increasing the adversary strength of said state-of-the-art attacks, one can
bypass the detection framework with adversarial examples of minimal visual
distortion. These results suggest for proposed defenses to validate against
stronger attack configurations.
Accelerating Empowerment Computation with UCT Tree Search
Authors: Christoph Salge, Christian Guckelsberger, Rodrigo Canaan, Tobias Mahlmann
Date: 2018-03-27 00:00:00
Tasks: Minecraft

Models of intrinsic motivation present an important means to produce sensible
behaviour in the absence of extrinsic rewards. Applications in video games are
varied, and range from intrinsically motivated general game-playing agents to
non-player characters such as companions and enemies. The information-theoretic
quantity of Empowerment is a particularly promising candidate motivation to
produce believable, generic and robust behaviour. However, while it can be used
in the absence of external reward functions that would need to be crafted and
learned, empowerment is computationally expensive. In this paper, we propose a
modified UCT tree search method to mitigate empowerment's computational
complexity in discrete and deterministic scenarios. We demonstrate how to
modify a Monte-Carlo Search Tree with UCT to realise empowerment maximisation,
and discuss three additional modifications that facilitate better sampling. We
evaluate the approach both quantitatively, by analysing how close our approach
gets to the baseline of exhaustive empowerment computation with varying amounts
of computational resources, and qualitatively, by analysing the resulting
behaviour in a Minecraft-like scenario.
A Decision Tree Approach to Predicting Recidivism in Domestic Violence
Authors: Senuri Wijenayake, Timothy Graham, Peter Christen
Date: 2018-03-27 00:00:00
Tasks: Decision Making, feature selection, regression

Domestic violence (DV) is a global social and public health issue that is
highly gendered. Being able to accurately predict DV recidivism, i.e.,
re-offending of a previously convicted offender, can speed up and improve risk
assessment procedures for police and front-line agencies, better protect
victims of DV, and potentially prevent future re-occurrences of DV. Previous
work in DV recidivism has employed different classification techniques,
including decision tree (DT) induction and logistic regression, where the main
focus was on achieving high prediction accuracy. As a result, even the diagrams
of trained DTs were often too difficult to interpret due to their size and
complexity, making decision-making challenging. Given there is often a
trade-off between model accuracy and interpretability, in this work our aim is
to employ DT induction to obtain both interpretable trees as well as high
prediction accuracy. Specifically, we implement and evaluate different
approaches to deal with class imbalance as well as feature selection. Compared
to previous work in DV recidivism prediction that employed logistic regression,
our approach can achieve comparable area under the ROC curve results by using
only 3 of 11 available features and generating understandable decision trees
that contain only 4 leaf nodes.
Three Birds One Stone: A General Architecture for Salient Object Segmentation, Edge Detection and Skeleton Extraction
Authors: Qibin Hou, Jiang-Jiang Liu, Ming-Ming Cheng, Ali Borji, Philip H. S. Torr
Date: 2018-03-27 00:00:00
Tasks: Edge Detection, Semantic Segmentation

In this paper, we aim at solving pixel-wise binary problems, including
salient object segmentation, skeleton extraction, and edge detection, by
introducing a unified architecture. Previous works have proposed tailored
methods for solving each of the three tasks independently. Here, we show that
these tasks share some similarities that can be exploited for developing a
unified framework. In particular, we introduce a horizontal cascade, each
component of which is densely connected to the outputs of previous component.
Stringing these components together allows us to effectively exploit features
across different levels hierarchically to effectively address the multiple
pixel-wise binary regression tasks. To assess the performance of our proposed
network on these tasks, we carry out exhaustive evaluations on multiple
representative datasets. Although these tasks are inherently very different, we
show that our unified approach performs very well on all of them and works far
better than current single-purpose state-of-the-art methods. All the code in
this paper will be publicly available.
SO-Net: Self-Organizing Network for Point Cloud Analysis
Authors: Jiaxin Li, Ben M. Chen, Gim Hee Lee
Date: 2018-03-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Part Segmentation, 3D Point Cloud Classification, 3D Point Cloud Linear Classification, Point cloud reconstruction, Retrieval, Unsupervised 3D Point Cloud Linear Evaluation

This paper presents SO-Net, a permutation invariant architecture for deep
learning with orderless point clouds. The SO-Net models the spatial
distribution of point cloud by building a Self-Organizing Map (SOM). Based on
the SOM, SO-Net performs hierarchical feature extraction on individual points
and SOM nodes, and ultimately represents the input point cloud by a single
feature vector. The receptive field of the network can be systematically
adjusted by conducting point-to-node k nearest neighbor search. In recognition
tasks such as point cloud reconstruction, classification, object part
segmentation and shape retrieval, our proposed network demonstrates performance
that is similar with or better than state-of-the-art approaches. In addition,
the training speed is significantly faster than existing point cloud
recognition networks because of the parallelizability and simplicity of the
proposed architecture. Our code is available at the project website.
https://github.com/lijx10/SO-Net
WebSeg: Learning Semantic Segmentation from Web Searches
Authors: Qibin Hou, Ming-Ming Cheng, Jiang-Jiang Liu, Philip H. S. Torr
Date: 2018-03-27 00:00:00
Tasks: Diversity, Segmentation, Semantic Segmentation

In this paper, we improve semantic segmentation by automatically learning
from Flickr images associated with a particular keyword, without relying on any
explicit user annotations, thus substantially alleviating the dependence on
accurate annotations when compared to previous weakly supervised methods.
  To solve such a challenging problem, we leverage several low-level cues (such
as saliency, edges, etc.) to help generate a proxy ground truth. Due to the
diversity of web-crawled images, we anticipate a large amount of 'label noise'
in which other objects might be present. We design an online noise filtering
scheme which is able to deal with this label noise, especially in cluttered
images. We use this filtering strategy as an auxiliary module to help assist
the segmentation network in learning cleaner proxy annotations. Extensive
experiments on the popular PASCAL VOC 2012 semantic segmentation benchmark show
surprising good results in both our WebSeg (mIoU = 57.0%) and weakly supervised
(mIoU = 63.3%) settings.
Detecting and Recognizing Human-Object Interactions
Authors: Georgia Gkioxari, Ross Girshick, Piotr Dollár, Kaiming He
Date: 2017-04-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: Human-Object Interaction Detection, Object

To understand the visual world, a machine must not only recognize individual
object instances but also how they interact. Humans are often at the center of
such interactions and detecting human-object interactions is an important
practical and scientific problem. In this paper, we address the task of
detecting <human, verb, object> triplets in challenging everyday photos. We
propose a novel model that is driven by a human-centric approach. Our
hypothesis is that the appearance of a person -- their pose, clothing, action
-- is a powerful cue for localizing the objects they are interacting with. To
exploit this cue, our model learns to predict an action-specific density over
target object locations based on the appearance of a detected person. Our model
also jointly learns to detect people and objects, and by fusing these
predictions it efficiently infers interaction triplets in a clean, jointly
trained end-to-end system we call InteractNet. We validate our approach on the
recently introduced Verbs in COCO (V-COCO) and HICO-DET datasets, where we show
quantitatively compelling results.