pwc-archive/papers-with-abstracts huggingface dataset:
Autonomous Driving without a Burden: View from Outside with Elevated LiDAR
Authors: Nalin Jayaweera, Nandana Rajatheva, Matti Latva-aho
Date: 2018-08-26 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Decision Making

The current autonomous driving architecture places a heavy burden in signal
processing for the graphics processing units (GPUs) in the car. This directly
translates into battery drain and lower energy efficiency, crucial factors in
electric vehicles. This is due to the high bit rate of the captured video and
other sensing inputs, mainly due to Light Detection and Ranging (LiDAR) sensor
at the top of the car which is an essential feature in autonomous vehicles.
LiDAR is needed to obtain a high precision map for the vehicle AI to make
relevant decisions. However, this is still a quite restricted view from the
car. This is the same even in the case of cars without a LiDAR such as Tesla.
The existing LiDARs and the cameras have limited horizontal and vertical fields
of visions. In all cases it can be argued that precision is lower, given the
smaller map generated. This also results in the accumulation of a large amount
of data in the order of several TBs in a day, the storage of which becomes
challenging. If we are to reduce the effort for the processing units inside the
car, we need to uplink the data to edge or an appropriately placed cloud.
However, the required data rates in the order of several Gbps are difficult to
be met even with the advent of 5G. Therefore, we propose to have a coordinated
set of LiDAR's outside at an elevation which can provide an integrated view
with a much larger field of vision (FoV) to a centralized decision making body
which then sends the required control actions to the vehicles with a lower bit
rate in the downlink and with the required latency. The calculations we have
based on industry standard equipment from several manufacturers show that this
is not just a concept but a feasible system which can be implemented.The
proposed system can play a supportive role with existing autonomous vehicle
architecture and it is easily applicable in an urban area.
Online Human Activity Recognition using Low-Power Wearable Devices
Authors: Ganapati Bhat, Ranadeep Deb, Vatika Vardhan Chaurasia, Holly Shill, Umit Y. Ogras
Date: 2018-08-26 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Human activity recognition~(HAR) has attracted significant research interest
due to its applications in health monitoring and patient rehabilitation. Recent
research on HAR focuses on using smartphones due to their widespread use.
However, this leads to inconvenient use, limited choice of sensors and
inefficient use of resources, since smartphones are not designed for HAR. This
paper presents the first HAR framework that can perform both online training
and inference. The proposed framework starts with a novel technique that
generates features using the fast Fourier and discrete wavelet transforms of a
textile-based stretch sensor and accelerometer. Using these features, we design
an artificial neural network classifier which is trained online using the
policy gradient algorithm. Experiments on a low power IoT device (TI-CC2650
MCU) with nine users show 97.7% accuracy in identifying six activities and
their transitions with less than 12.5 mW power consumption.
Ensemble Learning Applied to Classify GPS Trajectories of Birds into Male or Female
Authors: Dewan Fayzur
Date: 2018-08-26 00:00:00
Tasks: Ensemble Learning, Feature Engineering, Navigate

We describe our first-place solution to the Animal Behavior Challenge (ABC
2018) on predicting gender of bird from its GPS trajectory. The task consisted
in predicting the gender of shearwater based on how they navigate themselves
across a big ocean. The trajectories are collected from GPS loggers attached on
shearwaters' body, and represented as a variable-length sequence of GPS points
(latitude and longitude), and associated meta-information, such as the sun
azimuth, the sun elevation, the daytime, the elapsed time on each GPS location
after starting the trip, the local time (date is trimmed), and the indicator of
the day starting the from the trip. We used ensemble of several variants of
Gradient Boosting Classifier along with Gaussian Process Classifier and Support
Vector Classifier after extensive feature engineering and we ranked first out
of 74 registered teams. The variants of Gradient Boosting Classifier we tried
are CatBoost (Developed by Yandex), LightGBM (Developed by Microsoft), XGBoost
(Developed by Distributed Machine Learning Community). Our approach could
easily be adapted to other applications in which the goal is to predict a
classification output from a variable-length sequence.
Single Image Dehazing Based on Generic Regularity
Authors: Kushal Borkar, Snehasis Mukherjee
Date: 2018-08-26 00:00:00
Tasks: Image Dehazing, Single Image Dehazing

This paper proposes a novel technique for single image dehazing. Most of the
state-of-the-art methods for single image dehazing relies either on Dark
Channel Prior (DCP) or on Color line. The proposed method combines the two
different approaches. We initially compute the dark channel prior and then
apply a Nearest-Neighbor (NN) based regularization technique to obtain a smooth
transmission map of the hazy image. We consider the effect of airlight on the
image by using the color line model to assess the commitment of airlight in
each patch of the image and interpolate at the local neighborhood where the
estimate is unreliable. The NN based regularization of the DCP can remove the
haze, whereas, the color line based interpolation of airlight effect makes the
proposed system robust against the variation of haze within an image due to
multiple light sources. The proposed method is tested on benchmark datasets and
shows promising results compared to the state-of-the-art, including the deep
learning based methods, which require a huge computational setup. Moreover, the
proposed method outperforms the recent deep learning based methods when applied
on images with sky regions.
Adversarially Regularising Neural NLI Models to Integrate Logical Background Knowledge
Authors: Pasquale Minervini, Sebastian Riedel
Date: 2018-08-26 00:00:00
Proceeding: CONLL 2018 10
Tasks: BIG-bench Machine Learning, Language Modeling, Language Modelling, Natural Language Inference

Adversarial examples are inputs to machine learning models designed to cause
the model to make a mistake. They are useful for understanding the shortcomings
of machine learning models, interpreting their results, and for regularisation.
In NLP, however, most example generation strategies produce input text by using
known, pre-specified semantic transformations, requiring significant manual
effort and in-depth understanding of the problem and domain. In this paper, we
investigate the problem of automatically generating adversarial examples that
violate a set of given First-Order Logic constraints in Natural Language
Inference (NLI). We reduce the problem of identifying such adversarial examples
to a combinatorial optimisation problem, by maximising a quantity measuring the
degree of violation of such constraints and by using a language model for
generating linguistically-plausible examples. Furthermore, we propose a method
for adversarially regularising neural NLI models for incorporating background
knowledge. Our results show that, while the proposed method does not always
improve results on the SNLI and MultiNLI datasets, it significantly and
consistently increases the predictive accuracy on adversarially-crafted
datasets -- up to a 79.6% relative improvement -- while drastically reducing
the number of background knowledge violations. Furthermore, we show that
adversarial examples transfer among model architectures, and that the proposed
adversarial training procedure improves the robustness of NLI models to
adversarial examples.
Label and Sample: Efficient Training of Vehicle Object Detector from Sparsely Labeled Data
Authors: Xinlei Pan, Sung-Li Chiang, John Canny
Date: 2018-08-26 00:00:00
Tasks: object-detection, Object Detection, Object Tracking

Self-driving vehicle vision systems must deal with an extremely broad and
challenging set of scenes. They can potentially exploit an enormous amount of
training data collected from vehicles in the field, but the volumes are too
large to train offline naively. Not all training instances are equally valuable
though, and importance sampling can be used to prioritize which training images
to collect. This approach assumes that objects in images are labeled with high
accuracy. To generate accurate labels in the field, we exploit the
spatio-temporal coherence of vehicle video. We use a near-to-far labeling
strategy by first labeling large, close objects in the video, and tracking them
back in time to induce labels on small distant presentations of those objects.
In this paper we demonstrate the feasibility of this approach in several steps.
First, we note that an optimal subset (relative to all the objects encountered
and labeled) of labeled objects in images can be obtained by importance
sampling using gradients of the recognition network. Next we show that these
gradients can be approximated with very low error using the loss function,
which is already available when the CNN is running inference. Then, we
generalize these results to objects in a larger scene using an object detection
system. Finally, we describe a self-labeling scheme using object tracking.
Objects are tracked back in time (near-to-far) and labels of near objects are
used to check accuracy of those objects in the far field. We then evaluate the
accuracy of models trained on importance sampled data vs models trained on
complete data.
CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering
Authors: Zhengqi Li, Noah Snavely
Date: 2018-08-26 00:00:00
Proceeding: ECCV 2018 9
Tasks: Intrinsic Image Decomposition

Intrinsic image decomposition is a challenging, long-standing computer vision
problem for which ground truth data is very difficult to acquire. We explore
the use of synthetic data for training CNN-based intrinsic image decomposition
models, then applying these learned models to real-world images. To that end,
we present \ICG, a new, large-scale dataset of physically-based rendered images
of scenes with full ground truth decompositions. The rendering process we use
is carefully designed to yield high-quality, realistic images, which we find to
be crucial for this problem domain. We also propose a new end-to-end training
method that learns better decompositions by leveraging \ICG, and optionally IIW
and SAW, two recent datasets of sparse annotations on real-world images.
Surprisingly, we find that a decomposition network trained solely on our
synthetic data outperforms the state-of-the-art on both IIW and SAW, and
performance improves even further when IIW and SAW data is added during
training. Our work demonstrates the suprising effectiveness of
carefully-rendered synthetic data for the intrinsic images task.
Semi-Autoregressive Neural Machine Translation
Authors: Chunqi Wang, Ji Zhang, Haiqing Chen
Date: 2018-08-26 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Translation

Existing approaches to neural machine translation are typically
autoregressive models. While these models attain state-of-the-art translation
quality, they are suffering from low parallelizability and thus slow at
decoding long sequences. In this paper, we propose a novel model for fast
sequence generation --- the semi-autoregressive Transformer (SAT). The SAT
keeps the autoregressive property in global but relieves in local and thus is
able to produce multiple successive words in parallel at each time step.
Experiments conducted on English-German and Chinese-English translation tasks
show that the SAT achieves a good balance between translation quality and
decoding speed. On WMT'14 English-German translation, the SAT achieves
5.58$\times$ speedup while maintains 88\% translation quality, significantly
better than the previous non-autoregressive methods. When produces two words at
each time step, the SAT is almost lossless (only 1\% degeneration in BLEU
score).
Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach
Authors: Jinming Duan, Ghalib Bello, Jo Schlemper, Wenjia Bai, Timothy J. W. Dawes, Carlo Biffi, Antonio de Marvao, Georgia Doumou, Declan P. O'Regan, Daniel Rueckert
Date: 2018-08-26 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular 3D models, despite the artefacts in input CMR volumes.
Title-Guided Encoding for Keyphrase Generation
Authors: Wang Chen, Yifan Gao, Jiani Zhang, Irwin King, Michael R. Lyu
Date: 2018-08-26 00:00:00
Tasks: Decoder, Keyphrase Generation

Keyphrase generation (KG) aims to generate a set of keyphrases given a
document, which is a fundamental task in natural language processing (NLP).
Most previous methods solve this problem in an extractive manner, while
recently, several attempts are made under the generative setting using deep
neural networks. However, the state-of-the-art generative methods simply treat
the document title and the document main body equally, ignoring the leading
role of the title to the overall document. To solve this problem, we introduce
a new model called Title-Guided Network (TG-Net) for automatic keyphrase
generation task based on the encoder-decoder architecture with two new
features: (i) the title is additionally employed as a query-like input, and
(ii) a title-guided encoder gathers the relevant information from the title to
each word in the document. Experiments on a range of KG datasets demonstrate
that our model outperforms the state-of-the-art models with a large margin,
especially for documents with either very low or very high title length ratios.
Analyzing Learned Representations of a Deep ASR Performance Prediction Model
Authors: Zied Elloumi, Laurent Besacier, Olivier Galibert, Benjamin Lecouteux
Date: 2018-08-26 00:00:00
Proceeding: WS 2018 11
Tasks: Multi-Task Learning, Prediction, TAG

This paper addresses a relatively new task: prediction of ASR performance on
unseen broadcast programs. In a previous paper, we presented an ASR performance
prediction system using CNNs that encode both text (ASR transcript) and speech,
in order to predict word error rate. This work is dedicated to the analysis of
speech signal embeddings and text embeddings learnt by the CNN while training
our prediction model. We try to better understand which information is captured
by the deep model and its relation with different conditioning factors. It is
shown that hidden layers convey a clear signal about speech style, accent and
broadcast type. We then try to leverage these 3 types of information at
training time through multi-task learning. Our experiments show that this
allows to train slightly more efficient ASR performance prediction systems that
- in addition - simultaneously tag the analyzed utterances according to their
speech style, accent and broadcast program origin.
Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification
Authors: Junyang Lin, Qi Su, Pengcheng Yang, Shuming Ma, Xu sun
Date: 2018-08-26 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Classification, General Classification, Multi Label Text Classification, Multi-Label Text Classification, text-classification, Text Classification

We propose a novel model for multi-label text classification, which is based
on sequence-to-sequence learning. The model generates higher-level semantic
unit representations with multi-level dilated convolution as well as a
corresponding hybrid attention mechanism that extracts both the information at
the word-level and the level of the semantic unit. Our designed dilated
convolution effectively reduces dimension and supports an exponential expansion
of receptive fields without loss of local information, and the
attention-over-attention mechanism is able to capture more summary relevant
information from the source context. Results of our experiments show that the
proposed model has significant advantages over the baseline models on the
dataset RCV1-V2 and Ren-CECps, and our analysis demonstrates that our model is
competitive to the deterministic hierarchical models and it is more robust to
classifying low-frequency labels.
Convolutional Neural Networks for Aerial Vehicle Detection and Recognition
Authors: Amir Soleimani, Nasser M. Nasrabadi, Elias Griffith, Jason Ralph, Simon Maskell
Date: 2018-08-26 00:00:00
Tasks: vehicle detection

This paper investigates the problem of aerial vehicle recognition using a
text-guided deep convolutional neural network classifier. The network receives
an aerial image and a desired class, and makes a yes or no output by matching
the image and the textual description of the desired class. We train and test
our model on a synthetic aerial dataset and our desired classes consist of the
combination of the class types and colors of the vehicles. This strategy helps
when considering more classes in testing than in training.
Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error
Authors: Taiji Suzuki, Hiroshi Abe, Tomoya Murata, Shingo Horiuchi, Kotaro Ito, Tokuma Wachi, So Hirai, Masatoshi Yukishima, Tomoaki Nishimura
Date: 2018-08-26 00:00:00
Tasks: Edge-computing, Learning Theory, Model Compression

Compression techniques for deep neural network models are becoming very important for the efficient execution of high-performance deep learning systems on edge-computing devices. The concept of model compression is also important for analyzing the generalization error of deep learning, known as the compression-based error bound. However, there is still huge gap between a practically effective compression method and its rigorous background of statistical learning theory. To resolve this issue, we develop a new theoretical framework for model compression and propose a new pruning method called {\it spectral pruning} based on this framework. We define the ``degrees of freedom'' to quantify the intrinsic dimensionality of a model by using the eigenvalue distribution of the covariance matrix across the internal nodes and show that the compression ability is essentially controlled by this quantity. Moreover, we present a sharp generalization error bound of the compressed model and characterize the bias--variance tradeoff induced by the compression procedure. We apply our method to several datasets to justify our theoretical analyses and show the superiority of the the proposed method.
Rain Streak Removal for Single Image via Kernel Guided CNN
Authors: Ye-Tao Wang, Xi-Le Zhao, Tai-Xiang Jiang, Liang-Jian Deng, Yi Chang, Ting-Zhu Huang
Date: 2018-08-26 00:00:00

Rain streak removal is an important issue and has recently been investigated
extensively. Existing methods, especially the newly emerged deep learning
methods, could remove the rain streaks well in many cases. However the
essential factor in the generative procedure of the rain streaks, i.e., the
motion blur, which leads to the line pattern appearances, were neglected by the
deep learning rain streaks approaches and this resulted in over-derain or
under-derain results. In this paper, we propose a novel rain streak removal
approach using a kernel guided convolutional neural network (KGCNN), achieving
the state-of-the-art performance with simple network architectures. We first
model the rain streak interference with its motion blur mechanism. Then, our
framework starts with learning the motion blur kernel, which is determined by
two factors including angle and length, by a plain neural network, denoted as
parameter net, from a patch of the texture component. Then, after a
dimensionality stretching operation, the learned motion blur kernel is
stretched into a degradation map with the same spatial size as the rainy patch.
The stretched degradation map together with the texture patch is subsequently
input into a derain convolutional network, which is a typical ResNet
architecture and trained to output the rain streaks with the guidance of the
learned motion blur kernel. Experiments conducted on extensive synthetic and
real data demonstrate the effectiveness of the proposed method, which preserves
the texture and the contrast while removing the rain streaks.
Scale Drift Correction of Camera Geo-Localization using Geo-Tagged Images
Authors: Kazuya Iwami, Satoshi Ikehata, Kiyoharu Aizawa
Date: 2018-08-26 00:00:00
Tasks: 3D Reconstruction, Autonomous Navigation, geo-localization, Translation

Camera geo-localization from a monocular video is a fundamental task for
video analysis and autonomous navigation. Although 3D reconstruction is a key
technique to obtain camera poses, monocular 3D reconstruction in a large
environment tends to result in the accumulation of errors in rotation,
translation, and especially in scale: a problem known as scale drift. To
overcome these errors, we propose a novel framework that integrates incremental
structure from motion (SfM) and a scale drift correction method utilizing
geo-tagged images, such as those provided by Google Street View. Our correction
method begins by obtaining sparse 6-DoF correspondences between the
reconstructed 3D map coordinate system and the world coordinate system, by
using geo-tagged images. Then, it corrects scale drift by applying pose graph
optimization over Sim(3) constraints and bundle adjustment. Experimental
evaluations on large-scale datasets show that the proposed framework not only
sufficiently corrects scale drift, but also achieves accurate geo-localization
in a kilometer-scale environment.