pwc-archive/papers-with-abstracts huggingface dataset:
RotationNet: Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints
Authors: Asako Kanezaki, Yasuyuki Matsushita, Yoshifumi Nishida
Date: 2016-03-20 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Object Classification, Object, Object Categorization, Pose Estimation

We propose a Convolutional Neural Network (CNN)-based model "RotationNet,"
which takes multi-view images of an object as input and jointly estimates its
pose and object category. Unlike previous approaches that use known viewpoint
labels for training, our method treats the viewpoint labels as latent
variables, which are learned in an unsupervised manner during the training
using an unaligned object dataset. RotationNet is designed to use only a
partial set of multi-view images for inference, and this property makes it
useful in practical scenarios where only partial views are available. Moreover,
our pose alignment strategy enables one to obtain view-specific feature
representations shared across classes, which is important to maintain high
accuracy in both object categorization and pose estimation. Effectiveness of
RotationNet is demonstrated by its superior performance to the state-of-the-art
methods of 3D object classification on 10- and 40-class ModelNet datasets. We
also show that RotationNet, even trained without known poses, achieves the
state-of-the-art performance on an object pose estimation dataset. The code is
available on https://github.com/kanezaki/rotationnet
Expanding a robot's life: Low power object recognition via FPGA-based DCNN deployment
Authors: Panagiotis G. Mousouliotis, Konstantinos L. Panayiotou, Emmanouil G. Tsardoulias, Loukas P. Petrou, Andreas L. Symeonidis
Date: 2018-03-23 00:00:00
Tasks: Object Recognition

FPGAs are commonly used to accelerate domain-specific algorithmic
implementations, as they can achieve impressive performance boosts, are
reprogrammable and exhibit minimal power consumption. In this work, the
SqueezeNet DCNN is accelerated using an SoC FPGA in order for the offered
object recognition resource to be employed in a robotic application.
Experiments are conducted to investigate the performance and power consumption
of the implementation in comparison to deployment on other widely-used
computational systems.
Adaptive Correlation Filters with Long-Term and Short-Term Memory for Object Tracking
Authors: Chao Ma, Jia-Bin Huang, Xiaokang Yang, Ming-Hsuan Yang
Date: 2017-07-07 00:00:00
Tasks: Object Tracking, Position

Object tracking is challenging as target objects often undergo drastic
appearance changes over time. Recently, adaptive correlation filters have been
successfully applied to object tracking. However, tracking algorithms relying
on highly adaptive correlation filters are prone to drift due to noisy updates.
Moreover, as these algorithms do not maintain long-term memory of target
appearance, they cannot recover from tracking failures caused by heavy
occlusion or target disappearance in the camera view. In this paper, we propose
to learn multiple adaptive correlation filters with both long-term and
short-term memory of target appearance for robust object tracking. First, we
learn a kernelized correlation filter with an aggressive learning rate for
locating target objects precisely. We take into account the appropriate size of
surrounding context and the feature representations. Second, we learn a
correlation filter over a feature pyramid centered at the estimated target
position for predicting scale changes. Third, we learn a complementary
correlation filter with a conservative learning rate to maintain long-term
memory of target appearance. We use the output responses of this long-term
filter to determine if tracking failure occurs. In the case of tracking
failures, we apply an incrementally learned detector to recover the target
position in a sliding window fashion. Extensive experimental results on
large-scale benchmark datasets demonstrate that the proposed algorithm performs
favorably against the state-of-the-art methods in terms of efficiency,
accuracy, and robustness.
Pose-Driven Deep Models for Person Re-Identification
Authors: Andreas Eberle
Date: 2018-03-23 00:00:00
Tasks: Person Re-Identification

Person re-identification (re-id) is the task of recognizing and matching
persons at different locations recorded by cameras with non-overlapping views.
One of the main challenges of re-id is the large variance in person poses and
camera angles since neither of them can be influenced by the re-id system. In
this work, an effective approach to integrate coarse camera view information as
well as fine-grained pose information into a convolutional neural network (CNN)
model for learning discriminative re-id embeddings is introduced. In most
recent work pose information is either explicitly modeled within the re-id
system or explicitly used for pre-processing, for example by pose-normalizing
person images. In contrast, the proposed approach shows that a direct use of
camera view as well as the detected body joint locations into a standard CNN
can be used to significantly improve the robustness of learned re-id
embeddings. On four challenging surveillance and video re-id datasets
significant improvements over the current state of the art have been achieved.
Furthermore, a novel reordering of the MARS dataset, called X-MARS is
introduced to allow cross-validation of models trained for single-image re-id
on tracklet data.
Alarm-Based Prescriptive Process Monitoring
Authors: Irene Teinemaa, Niek Tax, Massimiliano de Leoni, Marlon Dumas, Fabrizio Maria Maggi
Date: 2018-03-23 00:00:00
Tasks: Predictive Process Monitoring

Predictive process monitoring is concerned with the analysis of events
produced during the execution of a process in order to predict the future state
of ongoing cases thereof. Existing techniques in this field are able to
predict, at each step of a case, the likelihood that the case will end up in an
undesired outcome. These techniques, however, do not take into account what
process workers may do with the generated predictions in order to decrease the
likelihood of undesired outcomes. This paper proposes a framework for
prescriptive process monitoring, which extends predictive process monitoring
approaches with the concepts of alarms, interventions, compensations, and
mitigation effects. The framework incorporates a parameterized cost model to
assess the cost-benefit tradeoffs of applying prescriptive process monitoring
in a given setting. The paper also outlines an approach to optimize the
generation of alarms given a dataset and a set of cost model parameters. The
proposed approach is empirically evaluated using a range of real-life event
logs.
Determinantal Point Processes for Coresets
Authors: Nicolas Tremblay, Simon Barthelmé, Pierre-Olivier Amblard
Date: 2018-03-23 00:00:00
Tasks: Point Processes, regression, Sensitivity

When faced with a data set too large to be processed all at once, an obvious solution is to retain only part of it. In practice this takes a wide variety of different forms, and among them "coresets" are especially appealing. A coreset is a (small) weighted sample of the original data that comes with the following guarantee: a cost function can be evaluated on the smaller set instead of the larger one, with low relative error. For some classes of problems, and via a careful choice of sampling distribution (based on the so-called "sensitivity" metric), iid random sampling has turned to be one of the most successful methods for building coresets efficiently. However, independent samples are sometimes overly redundant, and one could hope that enforcing diversity would lead to better performance. The difficulty lies in proving coreset properties in non-iid samples. We show that the coreset property holds for samples formed with determinantal point processes (DPP). DPPs are interesting because they are a rare example of repulsive point processes with tractable theoretical properties, enabling us to prove general coreset theorems. We apply our results to both the k-means and the linear regression problems, and give extensive empirical evidence that the small additional computational cost of DPP sampling comes with superior performance over its iid counterpart. Of independent interest, we also provide analytical formulas for the sensitivity in the linear regression and 1-means cases.
Can recurrent neural networks warp time?
Authors: Corentin Tallec, Yann Ollivier
Date: 2018-03-23 00:00:00
Proceeding: ICLR 2018 1

Successful recurrent models such as long short-term memories (LSTMs) and
gated recurrent units (GRUs) use ad hoc gating mechanisms. Empirically these
models have been found to improve the learning of medium to long term temporal
dependencies and to help with vanishing gradient issues. We prove that
learnable gates in a recurrent model formally provide quasi- invariance to
general time transformations in the input data. We recover part of the LSTM
architecture from a simple axiomatic approach. This result leads to a new way
of initializing gate biases in LSTMs and GRUs. Ex- perimentally, this new
chrono initialization is shown to greatly improve learning of long term
dependencies, with minimal implementation effort.
An Incremental Boolean Tensor Factorization approach to model Change Patterns of Objects in Images
Authors: S Saritha, G Santhosh Kumar
Date: 2018-03-23 00:00:00
Tasks: Change Detection, General Classification, Time Series, Time Series Analysis

Change detection process has recently progressed from a post-classification
method to an expert knowledge interpretation process of the time-series data.
The technique finds applications mainly in remote sensing images and can be
utilized to analyze urbanization and monitor forest regions. In this paper, a
framework to perform a knowledge based interpretation of the changes/no changes
observed in a spatiotemporal domain using tensor based approaches is presented.
An incremental approach to Boolean Tensor Factorization method is proposed in
this work, which is adopted to model the change patterns of objects/classes as
well as their associated features. The framework is evaluated under different
datasets to visualize the performance for the dependency factors. The algorithm
is also validated in comparison with the tradition Boolean Tensor Factorization
method and the results are substantial.
Deep learning and its application to medical image segmentation
Authors: Holger R. Roth, Chen Shen, Hirohisa ODA, Masahiro Oda, Yuichiro Hayashi, Kazunari Misawa, Kensaku MORI
Date: 2018-03-23 00:00:00
Tasks: Anatomy, Computed Tomography (CT), Deep Learning, Image Segmentation, Medical Image Segmentation, Organ Segmentation, Segmentation, Semantic Segmentation

One of the most common tasks in medical imaging is semantic segmentation.
Achieving this segmentation automatically has been an active area of research,
but the task has been proven very challenging due to the large variation of
anatomy across different patients. However, recent advances in deep learning
have made it possible to significantly improve the performance of image
recognition and semantic segmentation methods in the field of computer vision.
Due to the data driven approaches of hierarchical feature learning in deep
learning frameworks, these advances can be translated to medical images without
much difficulty. Several variations of deep convolutional neural networks have
been successfully applied to medical images. Especially fully convolutional
architectures have been proven efficient for segmentation of 3D medical images.
In this article, we describe how to build a 3D fully convolutional network
(FCN) that can process 3D images in order to produce automatic semantic
segmentations. The model is trained and evaluated on a clinical computed
tomography (CT) dataset and shows state-of-the-art performance in multi-organ
segmentation.
Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields
Authors: Yongcheng Jing, Yang Liu, Yezhou Yang, Zunlei Feng, Yizhou Yu, DaCheng Tao, Mingli Song
Date: 2018-02-20 00:00:00
Proceeding: ECCV 2018 9
Tasks: Style Transfer

The Fast Style Transfer methods have been recently proposed to transfer a
photograph to an artistic style in real-time. This task involves controlling
the stroke size in the stylized results, which remains an open challenge. In
this paper, we present a stroke controllable style transfer network that can
achieve continuous and spatial stroke size control. By analyzing the factors
that influence the stroke size, we propose to explicitly account for the
receptive field and the style image scales. We propose a StrokePyramid module
to endow the network with adaptive receptive fields, and two training
strategies to achieve faster convergence and augment new stroke sizes upon a
trained model respectively. By combining the proposed runtime control
strategies, our network can achieve continuous changes in stroke sizes and
produce distinct stroke sizes in different spatial regions within the same
output image.
Region-filtering Correlation Tracking
Authors: Nana Fan, Zhenyu He
Date: 2018-03-23 00:00:00
Tasks: Visual Tracking

Recently, correlation filters have demonstrated the excellent performance in
visual tracking. However, the base training sample region is larger than the
object region,including the Interference Region(IR). The IRs in training
samples from cyclic shifts of the base training sample severely degrade the
quality of a tracking model. In this paper, we propose the novel
Region-filtering Correlation Tracking (RFCT) to address this problem. We
immediately filter training samples by introducing a spatial map into the
standard CF formulation. Compared with existing correlation filter trackers,
our proposed tracker has the following advantages: (1) The correlation filter
can be learned on a larger search region without the interference of the IR by
a spatial map. (2) Due to processing training samples by a spatial map, it is
more general way to control background information and target information in
training samples. The values of the spatial map are not restricted, then a
better spatial map can be explored. (3) The weight proportions of accurate
filters are increased to alleviate model corruption. Experiments are performed
on two benchmark datasets: OTB-2013 and OTB-2015. Quantitative evaluations on
these benchmarks demonstrate that the proposed RFCT algorithm performs
favorably against several state-of-the-art methods.
mGPfusion: Predicting protein stability changes with Gaussian process kernel learning and data fusion
Authors: Emmi Jokinen, Markus Heinonen, Harri Lähdesmäki
Date: 2018-02-08 00:00:00
Tasks: Protein Design, Protein Stability Prediction

Proteins are commonly used by biochemical industry for numerous processes.
Refining these proteins' properties via mutations causes stability effects as
well. Accurate computational method to predict how mutations affect protein
stability are necessary to facilitate efficient protein design. However,
accuracy of predictive models is ultimately constrained by the limited
availability of experimental data. We have developed mGPfusion, a novel
Gaussian process (GP) method for predicting protein's stability changes upon
single and multiple mutations. This method complements the limited experimental
data with large amounts of molecular simulation data. We introduce a Bayesian
data fusion model that re-calibrates the experimental and in silico data
sources and then learns a predictive GP model from the combined data. Our
protein-specific model requires experimental data only regarding the protein of
interest and performs well even with few experimental measurements. The
mGPfusion models proteins by contact maps and infers the stability effects
caused by mutations with a mixture of graph kernels. Our results show that
mGPfusion outperforms state-of-the-art methods in predicting protein stability
on a dataset of 15 different proteins and that incorporating molecular
simulation data improves the model learning and prediction accuracy.
Improving DNN Robustness to Adversarial Attacks using Jacobian Regularization
Authors: Daniel Jakubovitz, Raja Giryes
Date: 2018-03-23 00:00:00
Proceeding: ECCV 2018 9

Deep neural networks have lately shown tremendous performance in various applications including vision and speech processing tasks. However, alongside their ability to perform these tasks with such high accuracy, it has been shown that they are highly susceptible to adversarial attacks: a small change in the input would cause the network to err with high confidence. This phenomenon exposes an inherent fault in these networks and their ability to generalize well. For this reason, providing robustness to adversarial attacks is an important challenge in networks training, which has led to extensive research. In this work, we suggest a theoretically inspired novel approach to improve the networks' robustness. Our method applies regularization using the Frobenius norm of the Jacobian of the network, which is applied as post-processing, after regular training has finished. We demonstrate empirically that it leads to enhanced robustness results with a minimal change in the original network's accuracy.
Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking
Authors: Feng Li, Cheng Tian, WangMeng Zuo, Lei Zhang, Ming-Hsuan Yang
Date: 2018-03-23 00:00:00
Proceeding: CVPR 2018 6
Tasks: Video Object Tracking, Visual Object Tracking, Visual Tracking

Discriminative Correlation Filters (DCF) are efficient in visual tracking but
suffer from unwanted boundary effects. Spatially Regularized DCF (SRDCF) has
been suggested to resolve this issue by enforcing spatial penalty on DCF
coefficients, which, inevitably, improves the tracking performance at the price
of increasing complexity. To tackle online updating, SRDCF formulates its model
on multiple training images, further adding difficulties in improving
efficiency. In this work, by introducing temporal regularization to SRDCF with
single sample, we present our spatial-temporal regularized correlation filters
(STRCF). Motivated by online Passive-Agressive (PA) algorithm, we introduce the
temporal regularization to SRDCF with single sample, thus resulting in our
spatial-temporal regularized correlation filters (STRCF). The STRCF formulation
can not only serve as a reasonable approximation to SRDCF with multiple
training samples, but also provide a more robust appearance model than SRDCF in
the case of large appearance variations. Besides, it can be efficiently solved
via the alternating direction method of multipliers (ADMM). By incorporating
both temporal and spatial regularization, our STRCF can handle boundary effects
without much loss in efficiency and achieve superior performance over SRDCF in
terms of accuracy and speed. Experiments are conducted on three benchmark
datasets: OTB-2015, Temple-Color, and VOT-2016. Compared with SRDCF, STRCF with
hand-crafted features provides a 5 times speedup and achieves a gain of 5.4%
and 3.6% AUC score on OTB-2015 and Temple-Color, respectively. Moreover, STRCF
combined with CNN features also performs favorably against state-of-the-art
CNN-based trackers and achieves an AUC score of 68.3% on OTB-2015.
Broadcasting Convolutional Network for Visual Relational Reasoning
Authors: Simyung Chang, John Yang, SeongUk Park, Nojun Kwak
Date: 2017-12-07 00:00:00
Proceeding: ECCV 2018 9
Tasks: Relation, Relational Reasoning, Relation Network

In this paper, we propose the Broadcasting Convolutional Network (BCN) that
extracts key object features from the global field of an entire input image and
recognizes their relationship with local features. BCN is a simple network
module that collects effective spatial features, embeds location information
and broadcasts them to the entire feature maps. We further introduce the
Multi-Relational Network (multiRN) that improves the existing Relation Network
(RN) by utilizing the BCN module. In pixel-based relation reasoning problems,
with the help of BCN, multiRN extends the concept of `pairwise relations' in
conventional RNs to `multiwise relations' by relating each object with multiple
objects at once. This yields in O(n) complexity for n objects, which is a vast
computational gain from RNs that take O(n^2). Through experiments, multiRN has
achieved a state-of-the-art performance on CLEVR dataset, which proves the
usability of BCN on relation reasoning problems.
Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries
Authors: Junjie Hu, Mete Ozay, Yan Zhang, Takayuki Okatani
Date: 2018-03-23 00:00:00
Tasks: Decoder, Depth Estimation, Monocular Depth Estimation

This paper considers the problem of single image depth estimation. The
employment of convolutional neural networks (CNNs) has recently brought about
significant advancements in the research of this problem. However, most
existing methods suffer from loss of spatial resolution in the estimated depth
maps; a typical symptom is distorted and blurry reconstruction of object
boundaries. In this paper, toward more accurate estimation with a focus on
depth maps with higher spatial resolution, we propose two improvements to
existing approaches. One is about the strategy of fusing features extracted at
different scales, for which we propose an improved network architecture
consisting of four modules: an encoder, decoder, multi-scale feature fusion
module, and refinement module. The other is about loss functions for measuring
inference errors used in training. We show that three loss terms, which measure
errors in depth, gradients and surface normals, respectively, contribute to
improvement of accuracy in an complementary fashion. Experimental results show
that these two improvements enable to attain higher accuracy than the current
state-of-the-arts, which is given by finer resolution reconstruction, for
example, with small objects and object boundaries.
Pyramid Stereo Matching Network
Authors: Jia-Ren Chang, Yong-Sheng Chen
Date: 2018-03-23 00:00:00
Proceeding: CVPR 2018 6
Tasks: Depth Estimation, Omnnidirectional Stereo Depth Estimation, Stereo Depth Estimation, Stereo-LiDAR Fusion, Stereo Matching, Stereo Matching Hand

Recent work has shown that depth estimation from a stereo pair of images can
be formulated as a supervised learning task to be resolved with convolutional
neural networks (CNNs). However, current architectures rely on patch-based
Siamese networks, lacking the means to exploit context information for finding
correspondence in illposed regions. To tackle this problem, we propose PSMNet,
a pyramid stereo matching network consisting of two main modules: spatial
pyramid pooling and 3D CNN. The spatial pyramid pooling module takes advantage
of the capacity of global context information by aggregating context in
different scales and locations to form a cost volume. The 3D CNN learns to
regularize cost volume using stacked multiple hourglass networks in conjunction
with intermediate supervision. The proposed approach was evaluated on several
benchmark datasets. Our method ranked first in the KITTI 2012 and 2015
leaderboards before March 18, 2018. The codes of PSMNet are available at:
https://github.com/JiaRenChang/PSMNet.