pwc-archive/papers-with-abstracts huggingface dataset:
City-Scale Road Audit System using Deep Learning
Authors: Sudhir Yarram, Girish Varma, C. V. Jawahar
Date: 2018-11-26 00:00:00
Tasks: Benchmarking, Deep Learning, Semantic Segmentation

Road networks in cities are massive and is a critical component of mobility.
Fast response to defects, that can occur not only due to regular wear and tear
but also because of extreme events like storms, is essential. Hence there is a
need for an automated system that is quick, scalable and cost-effective for
gathering information about defects. We propose a system for city-scale road
audit, using some of the most recent developments in deep learning and semantic
segmentation. For building and benchmarking the system, we curated a dataset
which has annotations required for road defects. However, many of the labels
required for road audit have high ambiguity which we overcome by proposing a
label hierarchy. We also propose a multi-step deep learning model that segments
the road, subdivide the road further into defects, tags the frame for each
defect and finally localizes the defects on a map gathered using GPS. We
analyze and evaluate the models on image tagging as well as segmentation at
different levels of the label hierarchy.
3D-LaneNet: End-to-End 3D Multiple Lane Detection
Authors: Noa Garnett, Rafi Cohen, Tomer Pe'er, Roee Lahav, Dan Levi
Date: 2018-11-26 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Lane Detection, Clustering, Lane Detection, object-detection, Object Detection

We introduce a network that directly predicts the 3D layout of lanes in a road scene from a single image. This work marks a first attempt to address this task with on-board sensing without assuming a known constant lane width or relying on pre-mapped environments. Our network architecture, 3D-LaneNet, applies two new concepts: intra-network inverse-perspective mapping (IPM) and anchor-based lane representation. The intra-network IPM projection facilitates a dual-representation information flow in both regular image-view and top-view. An anchor-per-column output representation enables our end-to-end approach which replaces common heuristics such as clustering and outlier rejection, casting lane estimation as an object detection problem. In addition, our approach explicitly handles complex situations such as lane merges and splits. Results are shown on two new 3D lane datasets, a synthetic and a real one. For comparison with existing methods, we test our approach on the image-only tuSimple lane detection benchmark, achieving performance competitive with state-of-the-art.
InstaNAS: Instance-aware Neural Architecture Search
Authors: An-Chieh Cheng, Chieh Hubert Lin, Da-Cheng Juan, Wei Wei, Min Sun
Date: 2018-11-26 00:00:00
Tasks: Diversity, Neural Architecture Search

Conventional Neural Architecture Search (NAS) aims at finding a single architecture that achieves the best performance, which usually optimizes task related learning objectives such as accuracy. However, a single architecture may not be representative enough for the whole dataset with high diversity and variety. Intuitively, electing domain-expert architectures that are proficient in domain-specific features can further benefit architecture related objectives such as latency. In this paper, we propose InstaNAS---an instance-aware NAS framework---that employs a controller trained to search for a "distribution of architectures" instead of a single architecture; This allows the model to use sophisticated architectures for the difficult samples, which usually comes with large architecture related cost, and shallow architectures for those easy samples. During the inference phase, the controller assigns each of the unseen input samples with a domain expert architecture that can achieve high accuracy with customized inference costs. Experiments within a search space inspired by MobileNetV2 show InstaNAS can achieve up to 48.8% latency reduction without compromising accuracy on a series of datasets against MobileNetV2.
IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments
Authors: Girish Varma, Anbumani Subramanian, Anoop Namboodiri, Manmohan Chandraker, C. V. Jawahar
Date: 2018-11-26 00:00:00
Tasks: Autonomous Navigation, Domain Adaptation, Few-Shot Learning, road scene understanding, Scene Understanding, Semantic Segmentation

While several datasets for autonomous navigation have become available in
recent years, they tend to focus on structured driving environments. This
usually corresponds to well-delineated infrastructure such as lanes, a small
number of well-defined categories for traffic participants, low variation in
object or background appearance and strict adherence to traffic rules. We
propose IDD, a novel dataset for road scene understanding in unstructured
environments where the above assumptions are largely not satisfied. It consists
of 10,004 images, finely annotated with 34 classes collected from 182 drive
sequences on Indian roads. The label set is expanded in comparison to popular
benchmarks such as Cityscapes, to account for new classes. It also reflects
label distributions of road scenes significantly different from existing
datasets, with most classes displaying greater within-class diversity.
Consistent with real driving behaviours, it also identifies new classes such as
drivable areas besides the road. We propose a new four-level label hierarchy,
which allows varying degrees of complexity and opens up possibilities for new
training methods. Our empirical study provides an in-depth analysis of the
label characteristics. State-of-the-art methods for semantic segmentation
achieve much lower accuracies on our dataset, demonstrating its distinction
compared to Cityscapes. Finally, we propose that our dataset is an ideal
opportunity for new problems such as domain adaptation, few-shot learning and
behaviour prediction in road scenes.
Cross-domain Deep Feature Combination for Bird Species Classification with Audio-visual Data
Authors: Bold Naranchimeg, Chao Zhang, Takuya Akashi
Date: 2018-11-26 00:00:00
Tasks: Audio Classification, Bird Species Classification With Audio-Visual Data, General Classification, image-classification, Image Classification, Transfer Learning

In recent decade, many state-of-the-art algorithms on image classification as
well as audio classification have achieved noticeable successes with the
development of deep convolutional neural network (CNN). However, most of the
works only exploit single type of training data. In this paper, we present a
study on classifying bird species by exploiting the combination of both visual
(images) and audio (sounds) data using CNN, which has been sparsely treated so
far. Specifically, we propose CNN-based multimodal learning models in three
types of fusion strategies (early, middle, late) to settle the issues of
combining training data cross domains. The advantage of our proposed method
lies on the fact that We can utilize CNN not only to extract features from
image and audio data (spectrogram) but also to combine the features across
modalities. In the experiment, we train and evaluate the network structure on a
comprehensive CUB-200-2011 standard data set combing our originally collected
audio data set with respect to the data species. We observe that a model which
utilizes the combination of both data outperforms models trained with only an
either type of data. We also show that transfer learning can significantly
increase the classification performance.
Ontology Matching Techniques: A Gold Standard Model
Authors: Alok Chauhan, Vijayakumar V, Layth Sliman
Date: 2018-11-26 00:00:00
Tasks: model, Ontology Matching

Typically an ontology matching technique is a combination of much different
type of matchers operating at various abstraction levels such as structure,
semantic, syntax, instance etc. An ontology matching technique which employs
matchers at all possible abstraction levels is expected to give, in general,
best results in terms of precision, recall and F-measure due to improvement in
matching opportunities and if we discount efficiency issues which may improve
with better computing resources such as parallel processing. A gold standard
ontology matching model is derived from a model classification of ontology
matching techniques. A suitable metric is also defined based on gold standard
ontology matching model. A review of various ontology matching techniques
specified in recent research papers in the area was undertaken to categorize an
ontology matching technique as per newly proposed gold standard model and a
metric value for the whole group was computed. The results of the above study
support proposed gold standard ontology matching model.
Implanting Rational Knowledge into Distributed Representation at Morpheme Level
Authors: Zi Lin, Yang Liu
Date: 2018-11-26 00:00:00
Tasks: Word Similarity

Previously, researchers paid no attention to the creation of unambiguous
morpheme embeddings independent from the corpus, while such information plays
an important role in expressing the exact meanings of words for parataxis
languages like Chinese. In this paper, after constructing the Chinese lexical
and semantic ontology based on word-formation, we propose a novel approach to
implanting the structured rational knowledge into distributed representation at
morpheme level, naturally avoiding heavy disambiguation in the corpus. We
design a template to create the instances as pseudo-sentences merely from the
pieces of knowledge of morphemes built in the lexicon. To exploit hierarchical
information and tackle the data sparseness problem, the instance proliferation
technique is applied based on similarity to expand the collection of
pseudo-sentences. The distributed representation for morphemes can then be
trained on these pseudo-sentences using word2vec. For evaluation, we validate
the paradigmatic and syntagmatic relations of morpheme embeddings, and apply
the obtained embeddings to word similarity measurement, achieving significant
improvements over the classical models by more than 5 Spearman scores or 8
percentage points, which shows very promising prospects for adoption of the new
source of knowledge.
Phase-only Image Based Kernel Estimation for Single-image Blind Deblurring
Authors: Liyuan Pan, Richard Hartley, Miaomiao Liu, Yuchao Dai
Date: 2018-11-26 00:00:00
Tasks: Blind Image Deblurring, Deblurring, Image Deblurring, Single-Image Blind Deblurring

The image blurring process is generally modelled as the convolution of a blur
kernel with a latent image. Therefore, the estimation of the blur kernel is
essentially important for blind image deblurring. Unlike existing approaches
which focus on approaching the problem by enforcing various priors on the blur
kernel and the latent image, we are aiming at obtaining a high quality blur
kernel directly by studying the problem in the frequency domain. We show that
the auto-correlation of the absolute phase-only image can provide faithful
information about the motion (e.g. the motion direction and magnitude, we call
it the motion pattern in this paper.) that caused the blur, leading to a new
and efficient blur kernel estimation approach. The blur kernel is then refined
and the sharp image is estimated by solving an optimization problem by
enforcing a regularization on the blur kernel and the latent image. We further
extend our approach to handle non-uniform blur, which involves spatially
varying blur kernels. Our approach is evaluated extensively on synthetic and
real data and shows good results compared to the state-of-the-art deblurring
approaches.
Bringing a Blurry Frame Alive at High Frame-Rate with an Event Camera
Authors: Liyuan Pan, Cedric Scheerlinck, Xin Yu, Richard Hartley, Miaomiao Liu, Yuchao Dai
Date: 2018-11-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: Video Generation

Event-based cameras can measure intensity changes (called `{\it events}')
with microsecond accuracy under high-speed motion and challenging lighting
conditions. With the active pixel sensor (APS), the event camera allows
simultaneous output of the intensity frames. However, the output images are
captured at a relatively low frame-rate and often suffer from motion blur. A
blurry image can be regarded as the integral of a sequence of latent images,
while the events indicate the changes between the latent images. Therefore, we
are able to model the blur-generation process by associating event data to a
latent image. In this paper, we propose a simple and effective approach, the
\textbf{Event-based Double Integral (EDI)} model, to reconstruct a high
frame-rate, sharp video from a single blurry frame and its event data. The
video generation is based on solving a simple non-convex optimization problem
in a single scalar variable. Experimental results on both synthetic and real
images demonstrate the superiority of our EDI model and optimization method in
comparison to the state-of-the-art.
Improving Gated Recurrent Unit Based Acoustic Modeling with Batch Normalization and Enlarged Context
Authors: Jie Li, Yahui Shan, Xiaorui Wang, Yan Li
Date: 2018-11-26 00:00:00

The use of future contextual information is typically shown to be helpful for
acoustic modeling. Recently, we proposed a RNN model called minimal gated
recurrent unit with input projection (mGRUIP), in which a context module namely
temporal convolution, is specifically designed to model the future context.
This model, mGRUIP with context module (mGRUIP-Ctx), has been shown to be able
of utilizing the future context effectively, meanwhile with quite low model
latency and computation cost. In this paper, we continue to improve mGRUIP-Ctx
with two revisions: applying BN methods and enlarging model context.
Experimental results on two Mandarin ASR tasks (8400 hours and 60K hours) show
that, the revised mGRUIP-Ctx outperform LSTM with a large margin (11% to 38%).
It even performs slightly better than a superior BLSTM on the 8400h task, with
33M less parameters and just 290ms model latency.
LSICC: A Large Scale Informal Chinese Corpus
Authors: Jianyu Zhao, Zhuoran Ji
Date: 2018-11-26 00:00:00
Tasks: Chinese Word Segmentation, Deep Learning, Sentiment Analysis

Deep learning based natural language processing model is proven powerful, but
need large-scale dataset. Due to the significant gap between the real-world
tasks and existing Chinese corpus, in this paper, we introduce a large-scale
corpus of informal Chinese. This corpus contains around 37 million book reviews
and 50 thousand netizen's comments to the news. We explore the informal words
frequencies of the corpus and show the difference between our corpus and the
existing ones. The corpus can be further used to train deep learning based
natural language processing tasks such as Chinese word segmentation, sentiment
analysis.
Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
Authors: Charlotte Pelletier, Geoffrey I. Webb, Francois Petitjean
Date: 2018-11-26 00:00:00
Tasks: Classification, General Classification, Image Classification, Time Series, Time Series Analysis

New remote sensing sensors now acquire high spatial and spectral Satellite
Image Time Series (SITS) of the world. These series of images are a key
component of classification systems that aim at obtaining up-to-date and
accurate land cover maps of the Earth's surfaces. More specifically, the
combination of the temporal, spectral and spatial resolutions of new SITS makes
possible to monitor vegetation dynamics. Although traditional classification
algorithms, such as Random Forest (RF), have been successfully applied for SITS
classification, these algorithms do not make the most of the temporal domain.
Conversely, some approaches that take into account the temporal dimension have
recently been tested, especially Recurrent Neural Networks (RNNs). This paper
proposes an exhaustive study of another deep learning approaches, namely
Temporal Convolutional Neural Networks (TempCNNs) where convolutions are
applied in the temporal dimension. The goal is to quantitatively and
qualitatively evaluate the contribution of TempCNNs for SITS classification.
This paper proposes a set of experiments performed on one million time series
extracted from 46 Formosat-2 images. The experimental results show that
TempCNNs are more accurate than RF and RNNs, that are the current state of the
art for SITS classification. We also highlight some differences with results
obtained in computer vision, e.g. about pooling layers. Moreover, we provide
some general guidelines on the network architecture, common regularization
mechanisms, and hyper-parameter values such as batch size. Finally, we assess
the visual quality of the land cover maps produced by TempCNNs.
Reinforcement Learning for Uplift Modeling
Authors: Chenchen Li, Xiang Yan, Xiaotie Deng, Yuan Qi, Wei Chu, Le Song, Junlong Qiao, Jianshan He, Junwu Xiong
Date: 2018-11-26 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Uplift modeling aims to directly model the incremental impact of a treatment
on an individual response. In this work, we address the problem from a new
angle and reformulate it as a Markov Decision Process (MDP). We conducted
extensive experiments on both a synthetic dataset and real-world scenarios, and
showed that our method can achieve significant improvement over previous
methods.
Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead
Authors: Cynthia Rudin
Date: 2018-11-26 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Interpretable Machine Learning

Black box machine learning models are currently being used for high stakes decision-making throughout society, causing problems throughout healthcare, criminal justice, and in other domains. People have hoped that creating methods for explaining these black box models will alleviate some of these problems, but trying to \textit{explain} black box models, rather than creating models that are \textit{interpretable} in the first place, is likely to perpetuate bad practices and can potentially cause catastrophic harm to society. There is a way forward -- it is to design models that are inherently interpretable. This manuscript clarifies the chasm between explaining black boxes and using inherently interpretable models, outlines several key reasons why explainable black boxes should be avoided in high-stakes decisions, identifies challenges to interpretable machine learning, and provides several example applications where interpretable models could potentially replace black box models in criminal justice, healthcare, and computer vision.
Spatially Controllable Image Synthesis with Internal Representation Collaging
Authors: Ryohei Suzuki, Masanori Koyama, Takeru Miyato, Taizan Yonetsuji, Huachun Zhu
Date: 2018-11-26 00:00:00
Tasks: Image Generation

We present a novel CNN-based image editing strategy that allows the user to
change the semantic information of an image over an arbitrary region by
manipulating the feature-space representation of the image in a trained GAN
model. We will present two variants of our strategy: (1) spatial conditional
batch normalization (sCBN), a type of conditional batch normalization with
user-specifiable spatial weight maps, and (2) feature-blending, a method of
directly modifying the intermediate features. Our methods can be used to edit
both artificial image and real image, and they both can be used together with
any GAN with conditional normalization layers. We will demonstrate the power of
our method through experiments on various types of GANs trained on different
datasets. Code will be available at
https://github.com/pfnet-research/neural-collage.
Frequency Principle in Deep Learning with General Loss Functions and Its Potential Application
Authors: Zhi-Qin John Xu
Date: 2018-11-26 00:00:00

Previous studies have shown that deep neural networks (DNNs) with common
settings often capture target functions from low to high frequency, which is
called Frequency Principle (F-Principle). It has also been shown that
F-Principle can provide an understanding to the often observed good
generalization ability of DNNs. However, previous studies focused on the loss
function of mean square error, while various loss functions are used in
practice. In this work, we show that the F-Principle holds for a general loss
function (e.g., mean square error, cross entropy, etc.). In addition, DNN's
F-Principle may be applied to develop numerical schemes for solving various
problems which would benefit from a fast converging of low frequency. As an
example of the potential usage of F-Principle, we apply DNN in solving
differential equations, in which conventional methods (e.g., Jacobi method) is
usually slow in solving problems due to the convergence from high to low
frequency.
Self-similarity Grouping: A Simple Unsupervised Cross Domain Adaptation Approach for Person Re-identification
Authors: Yang Fu, Yunchao Wei, Guanshuo Wang, Yuqian Zhou, Honghui Shi, Thomas Huang
Date: 2018-11-26 00:00:00
Proceeding: ICCV 2019 10
Tasks: Clustering, Domain Adaptation, One-Shot Learning, Person Re-Identification, Unsupervised Domain Adaptation, Unsupervised Person Re-Identification

Domain adaptation in person re-identification (re-ID) has always been a challenging task. In this work, we explore how to harness the natural similar characteristics existing in the samples from the target domain for learning to conduct person re-ID in an unsupervised manner. Concretely, we propose a Self-similarity Grouping (SSG) approach, which exploits the potential similarity (from global body to local parts) of unlabeled samples to automatically build multiple clusters from different views. These independent clusters are then assigned with labels, which serve as the pseudo identities to supervise the training process. We repeatedly and alternatively conduct such a grouping and training process until the model is stable. Despite the apparent simplify, our SSG outperforms the state-of-the-arts by more than 4.6% (DukeMTMC to Market1501) and 4.4% (Market1501 to DukeMTMC) in mAP, respectively. Upon our SSG, we further introduce a clustering-guided semisupervised approach named SSG ++ to conduct the one-shot domain adaption in an open set setting (i.e. the number of independent identities from the target domain is unknown). Without spending much effort on labeling, our SSG ++ can further promote the mAP upon SSG by 10.7% and 6.9%, respectively. Our Code is available at: https://github.com/OasisYang/SSG .
FilterReg: Robust and Efficient Probabilistic Point-Set Registration using Gaussian Filter and Twist Parameterization
Authors: Wei Gao, Russ Tedrake
Date: 2018-11-26 00:00:00
Proceeding: CVPR 2019 6

Probabilistic point-set registration methods have been gaining more attention for their robustness to noise, outliers and occlusions. However, these methods tend to be much slower than the popular iterative closest point (ICP) algorithms, which severely limits their usability. In this paper, we contribute a novel probabilistic registration method that achieves state-of-the-art robustness as well as substantially faster computational performance than modern ICP implementations. This is achieved using a rigorous yet computationally-efficient probabilistic formulation. Point-set registration is cast as a maximum likelihood estimation and solved using the EM algorithm. We show that with a simple augmentation, the E step can be formulated as a filtering problem, allowing us to leverage advances in efficient Gaussian filtering methods. We also propose a customized permutohedral filter for improved efficiency while retaining sufficient accuracy for our task. Additionally, we present a simple and efficient twist parameterization that generalizes our method to the registration of articulated and deformable objects. For articulated objects, the complexity of our method is almost independent of the Degrees Of Freedom (DOFs), which makes it highly efficient even for high DOF systems. The results demonstrate the proposed method consistently outperforms many competitive baselines on a variety of registration tasks.