pwc-archive/papers-with-abstracts huggingface dataset:
Empirical Analysis of the Hessian of Over-Parametrized Neural Networks
Authors: Levent Sagun, Utku Evci, V. Ugur Guney, Yann Dauphin, Leon Bottou
Date: 2017-06-14 00:00:00
Proceeding: ICLR 2018 1

We study the properties of common loss surfaces through their Hessian matrix.
In particular, in the context of deep learning, we empirically show that the
spectrum of the Hessian is composed of two parts: (1) the bulk centered near
zero, (2) and outliers away from the bulk. We present numerical evidence and
mathematical justifications to the following conjectures laid out by Sagun et
al. (2016): Fixing data, increasing the number of parameters merely scales the
bulk of the spectrum; fixing the dimension and changing the data (for instance
adding more clusters or making the data less separable) only affects the
outliers. We believe that our observations have striking implications for
non-convex optimization in high dimensions. First, the flatness of such
landscapes (which can be measured by the singularity of the Hessian) implies
that classical notions of basins of attraction may be quite misleading. And
that the discussion of wide/narrow basins may be in need of a new perspective
around over-parametrization and redundancy that are able to create large
connected components at the bottom of the landscape. Second, the dependence of
small number of large eigenvalues to the data distribution can be linked to the
spectrum of the covariance matrix of gradients of model outputs. With this in
mind, we may reevaluate the connections within the data-architecture-algorithm
framework of a model, hoping that it would shed light into the geometry of
high-dimensional and non-convex spaces in modern applications. In particular,
we present a case that links the two observations: small and large batch
gradient descent appear to converge to different basins of attraction but we
show that they are in fact connected through their flat region and so belong to
the same basin.
30m resolution Global Annual Burned Area Mapping based on Landsat images and Google Earth Engine
Authors: Tengfei Long, Zhaoming Zhang, Guojin He, Weili Jiao, Chao Tang, Bingfang Wu, Xiaomei Zhang, Guizhou Wang, Ranyu Yin
Date: 2018-05-07 00:00:00
Tasks: Fire Detection, Time Series, Time Series Analysis

Heretofore, global burned area (BA) products are only available at coarse
spatial resolution, since most of the current global BA products are produced
with the help of active fire detection or dense time-series change analysis,
which requires very high temporal resolution. In this study, however, we focus
on automated global burned area mapping approach based on Landsat images. By
utilizing the huge catalog of satellite imagery as well as the high-performance
computing capacity of Google Earth Engine, we proposed an automated pipeline
for generating 30-meter resolution global-scale annual burned area map from
time-series of Landsat images, and a novel 30-meter resolution global annual
burned area map of 2015 (GABAM 2015) is released. GABAM 2015 consists of
spatial extent of fires that occurred during 2015 and not of fires that
occurred in previous years. Cross-comparison with recent Fire_cci version 5.0
BA product found a similar spatial distribution and a strong correlation
($R^2=0.74$) between the burned areas from the two products, although
differences were found in specific land cover categories (particularly in
agriculture land). Preliminary global validation showed the commission and
omission error of GABAM 2015 are 13.17% and 30.13%, respectively.
Relational Network for Skeleton-Based Action Recognition
Authors: Wu Zheng, Lin Li, Zhao-Xiang Zhang, Yan Huang, Liang Wang
Date: 2018-05-07 00:00:00
Tasks: Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

With the fast development of effective and low-cost human skeleton capture
systems, skeleton-based action recognition has attracted much attention
recently. Most existing methods use Convolutional Neural Network (CNN) and
Recurrent Neural Network (RNN) to extract spatio-temporal information embedded
in the skeleton sequences for action recognition. However, these approaches are
limited in the ability of relational modeling in a single skeleton, due to the
loss of important structural information when converting the raw skeleton data
to adapt to the input format of CNN or RNN. In this paper, we propose an
Attentional Recurrent Relational Network-LSTM (ARRN-LSTM) to simultaneously
model spatial configurations and temporal dynamics in skeletons for action
recognition. We introduce the Recurrent Relational Network to learn the spatial
features in a single skeleton, followed by a multi-layer LSTM to learn the
temporal features in the skeleton sequences. Between the two modules, we design
an adaptive attentional module to focus attention on the most discriminative
parts in the single skeleton. To exploit the complementarity from different
geometries in the skeleton for sufficient relational modeling, we design a
two-stream architecture to learn the structural features among joints and lines
simultaneously. Extensive experiments are conducted on several popular skeleton
datasets and the results show that the proposed approach achieves better
results than most mainstream methods.
Automatic Metric Validation for Grammatical Error Correction
Authors: Leshem Choshen, Omri Abend
Date: 2018-04-30 00:00:00
Proceeding: ACL 2018 7
Tasks: Grammatical Error Correction

Metric validation in Grammatical Error Correction (GEC) is currently done by
observing the correlation between human and metric-induced rankings. However,
such correlation studies are costly, methodologically troublesome, and suffer
from low inter-rater agreement. We propose MAEGE, an automatic methodology for
GEC metric validation, that overcomes many of the difficulties with existing
practices. Experiments with \maege\ shed a new light on metric quality, showing
for example that the standard $M^2$ metric fares poorly on corpus-level
ranking. Moreover, we use MAEGE to perform a detailed analysis of metric
behavior, showing that correcting some types of errors is consistently
penalized by existing metrics.
Trajectory Representation and Landmark Projection for Continuous-Time Structure from Motion
Authors: Hannes Ovrén, Per-Erik Forssén
Date: 2018-05-07 00:00:00
Tasks: Translation

This paper revisits the problem of continuous-time structure from motion, and
introduces a number of extensions that improve convergence and efficiency. The
formulation with a $\mathcal{C}^2$-continuous spline for the trajectory
naturally incorporates inertial measurements, as derivatives of the sought
trajectory. We analyse the behaviour of split interpolation on $\mathbb{SO}(3)$
and on $\mathbb{R}^3$, and a joint interpolation on $\mathbb{SE}(3)$, and show
that the latter implicitly couples the direction of translation and rotation.
Such an assumption can make good sense for a camera mounted on a robot arm, but
not for hand-held or body-mounted cameras. Our experiments show that split
interpolation on $\mathbb{SO}(3)$ and on $\mathbb{R}^3$ is preferable over
$\mathbb{SE}(3)$ interpolation in all tested cases. Finally, we investigate the
problem of landmark reprojection on rolling shutter cameras, and show that the
tested reprojection methods give similar quality, while their computational
load varies by a factor of 2.
Zeroth Order Nonconvex Multi-Agent Optimization over Networks
Authors: Davood Hajinezhad, Mingyi Hong, Alfredo Garcia
Date: 2017-10-27 00:00:00
Tasks: Distributed Optimization

In this paper, we consider distributed optimization problems over a
multi-agent network, where each agent can only partially evaluate the objective
function, and it is allowed to exchange messages with its immediate neighbors.
Differently from all existing works on distributed optimization, our focus is
given to optimizing a class of non-convex problems, and under the challenging
setting where each agent can only access the zeroth-order information (i.e.,
the functional values) of its local functions. For different types of network
topologies such as undirected connected networks or star networks, we develop
efficient distributed algorithms and rigorously analyze their convergence and
rate of convergence (to the set of stationary solutions). Numerical results are
provided to demonstrate the efficiency of the proposed algorithms.
Detecting Traffic Lights by Single Shot Detection
Authors: Julian Müller, Klaus Dietmayer
Date: 2018-05-07 00:00:00
Tasks: object-detection, Object Detection

Recent improvements in object detection are driven by the success of
convolutional neural networks (CNN). They are able to learn rich features
outperforming hand-crafted features. So far, research in traffic light
detection mainly focused on hand-crafted features, such as color, shape or
brightness of the traffic light bulb. This paper presents a deep learning
approach for accurate traffic light detection in adapting a single shot
detection (SSD) approach. SSD performs object proposals creation and
classification using a single CNN. The original SSD struggles in detecting very
small objects, which is essential for traffic light detection. By our
adaptations it is possible to detect objects much smaller than ten pixels
without increasing the input image size. We present an extensive evaluation on
the DriveU Traffic Light Dataset (DTLD). We reach both, high accuracy and low
false positive rates. The trained model is real-time capable with ten frames
per second on a Nvidia Titan Xp. Code has been made available at
https://github.com/julimueller/tl_ssd.
Long-Term Human Motion Prediction by Modeling Motion Context and Enhancing Motion Dynamic
Authors: Yongyi Tang, Lin Ma, Wei Liu, Wei-Shi Zheng
Date: 2018-05-07 00:00:00
Tasks: Human motion prediction, motion prediction, Prediction

Human motion prediction aims at generating future frames of human motion
based on an observed sequence of skeletons. Recent methods employ the latest
hidden states of a recurrent neural network (RNN) to encode the historical
skeletons, which can only address short-term prediction. In this work, we
propose a motion context modeling by summarizing the historical human motion
with respect to the current prediction. A modified highway unit (MHU) is
proposed for efficiently eliminating motionless joints and estimating next pose
given the motion context. Furthermore, we enhance the motion dynamic by
minimizing the gram matrix loss for long-term motion prediction. Experimental
results show that the proposed model can promisingly forecast the human future
movements, which yields superior performances over related state-of-the-art
approaches. Moreover, specifying the motion context with the activity labels
enables our model to perform human motion transfer.
Deep Interest Network for Click-Through Rate Prediction
Authors: Guorui Zhou, Chengru Song, Xiaoqiang Zhu, Ying Fan, Han Zhu, Xiao Ma, Yanghui Yan, Junqi Jin, Han Li, Kun Gai
Date: 2017-06-21 00:00:00
Tasks: Click-Through Rate Prediction, Prediction

Click-through rate prediction is an essential task in industrial
applications, such as online advertising. Recently deep learning based models
have been proposed, which follow a similar Embedding\&MLP paradigm. In these
methods large scale sparse input features are first mapped into low dimensional
embedding vectors, and then transformed into fixed-length vectors in a
group-wise manner, finally concatenated together to fed into a multilayer
perceptron (MLP) to learn the nonlinear relations among features. In this way,
user features are compressed into a fixed-length representation vector, in
regardless of what candidate ads are. The use of fixed-length vector will be a
bottleneck, which brings difficulty for Embedding\&MLP methods to capture
user's diverse interests effectively from rich historical behaviors. In this
paper, we propose a novel model: Deep Interest Network (DIN) which tackles this
challenge by designing a local activation unit to adaptively learn the
representation of user interests from historical behaviors with respect to a
certain ad. This representation vector varies over different ads, improving the
expressive ability of model greatly. Besides, we develop two techniques:
mini-batch aware regularization and data adaptive activation function which can
help training industrial deep networks with hundreds of millions of parameters.
Experiments on two public datasets as well as an Alibaba real production
dataset with over 2 billion samples demonstrate the effectiveness of proposed
approaches, which achieve superior performance compared with state-of-the-art
methods. DIN now has been successfully deployed in the online display
advertising system in Alibaba, serving the main traffic.
Comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery
Authors: Sebastian Bodenstedt, Max Allan, Anthony Agustinos, Xiaofei Du, Luis Garcia-Peraza-Herrera, Hannes Kenngott, Thomas Kurmann, Beat Müller-Stich, Sebastien Ourselin, Daniil Pakhomov, Raphael Sznitman, Marvin Teichmann, Martin Thoma, Tom Vercauteren, Sandrine Voros, Martin Wagner, Pamela Wochner, Lena Maier-Hein, Danail Stoyanov, Stefanie Speidel
Date: 2018-05-07 00:00:00
Tasks: Benchmarking, Segmentation

Intraoperative segmentation and tracking of minimally invasive instruments is
a prerequisite for computer- and robotic-assisted surgery. Since additional
hardware like tracking systems or the robot encoders are cumbersome and lack
accuracy, surgical vision is evolving as promising techniques to segment and
track the instruments using only the endoscopic images. However, what is
missing so far are common image data sets for consistent evaluation and
benchmarking of algorithms against each other. The paper presents a comparative
validation study of different vision-based methods for instrument segmentation
and tracking in the context of robotic as well as conventional laparoscopic
surgery. The contribution of the paper is twofold: we introduce a comprehensive
validation data set that was provided to the study participants and present the
results of the comparative validation study. Based on the results of the
validation study, we arrive at the conclusion that modern deep learning
approaches outperform other methods in instrument segmentation tasks, but the
results are still not perfect. Furthermore, we show that merging results from
different methods actually significantly increases accuracy in comparison to
the best stand-alone method. On the other hand, the results of the instrument
tracking task show that this is still an open challenge, especially during
challenging scenarios in conventional laparoscopic surgery.
Sentence-State LSTM for Text Representation
Authors: Yue Zhang, Qi Liu, Linfeng Song
Date: 2018-05-07 00:00:00
Proceeding: ACL 2018 7
Tasks: General Classification, Named Entity Recognition (NER), Part-Of-Speech Tagging, Sentence, Sentiment Analysis, Text Classification

Bi-directional LSTMs are a powerful tool for text representation. On the
other hand, they have been shown to suffer various limitations due to their
sequential nature. We investigate an alternative LSTM structure for encoding
text, which consists of a parallel state for each word. Recurrent steps are
used to perform local and global information exchange between words
simultaneously, rather than incremental reading of a sequence of words. Results
on various classification and sequence labelling benchmarks show that the
proposed model has strong representation power, giving highly competitive
performances compared to stacked BiLSTM models with similar parameter numbers.
Deep Ordinal Hashing with Spatial Attention
Authors: Lu Jin, Xiangbo Shu, Kai Li, Zechao Li, Guo-Jun Qi, Jinhui Tang
Date: 2018-05-07 00:00:00
Tasks: Deep Hashing, Image Retrieval, Retrieval

Hashing has attracted increasing research attentions in recent years due to
its high efficiency of computation and storage in image retrieval. Recent works
have demonstrated the superiority of simultaneous feature representations and
hash functions learning with deep neural networks. However, most existing deep
hashing methods directly learn the hash functions by encoding the global
semantic information, while ignoring the local spatial information of images.
The loss of local spatial structure makes the performance bottleneck of hash
functions, therefore limiting its application for accurate similarity
retrieval. In this work, we propose a novel Deep Ordinal Hashing (DOH) method,
which learns ordinal representations by leveraging the ranking structure of
feature space from both local and global views. In particular, to effectively
build the ranking structure, we propose to learn the rank correlation space by
exploiting the local spatial information from Fully Convolutional Network (FCN)
and the global semantic information from the Convolutional Neural Network (CNN)
simultaneously. More specifically, an effective spatial attention model is
designed to capture the local spatial information by selectively learning
well-specified locations closely related to target objects. In such hashing
framework,the local spatial and global semantic nature of images are captured
in an end-to-end ranking-to-hashing manner. Experimental results conducted on
three widely-used datasets demonstrate that the proposed DOH method
significantly outperforms the state-of-the-art hashing methods.
Fast View Synthesis with Deep Stereo Vision
Authors: Tewodros Habtegebrial, Kiran varanasi, Christian Bailer, Didier Stricker
Date: 2018-04-25 00:00:00
Tasks: Novel View Synthesis, Structured Prediction

Novel view synthesis is an important problem in computer vision and graphics.
Over the years a large number of solutions have been put forward to solve the
problem. However, the large-baseline novel view synthesis problem is far from
being "solved". Recent works have attempted to use Convolutional Neural
Networks (CNNs) to solve view synthesis tasks. Due to the difficulty of
learning scene geometry and interpreting camera motion, CNNs are often unable
to generate realistic novel views. In this paper, we present a novel view
synthesis approach based on stereo-vision and CNNs that decomposes the problem
into two sub-tasks: view dependent geometry estimation and texture inpainting.
Both tasks are structured prediction problems that could be effectively learned
with CNNs. Experiments on the KITTI Odometry dataset show that our approach is
more accurate and significantly faster than the current state-of-the-art. The
code and supplementary material will be publicly available. Results could be
found here https://youtu.be/5pzS9jc-5t0
Unpaired Multi-Domain Image Generation via Regularized Conditional GANs
Authors: Xudong Mao, Qing Li
Date: 2018-05-07 00:00:00
Tasks: Domain Adaptation, Image Generation, Unsupervised Domain Adaptation

In this paper, we study the problem of multi-domain image generation, the
goal of which is to generate pairs of corresponding images from different
domains. With the recent development in generative models, image generation has
achieved great progress and has been applied to various computer vision tasks.
However, multi-domain image generation may not achieve the desired performance
due to the difficulty of learning the correspondence of different domain
images, especially when the information of paired samples is not given. To
tackle this problem, we propose Regularized Conditional GAN (RegCGAN) which is
capable of learning to generate corresponding images in the absence of paired
training data. RegCGAN is based on the conditional GAN, and we introduce two
regularizers to guide the model to learn the corresponding semantics of
different domains. We evaluate the proposed model on several tasks for which
paired training data is not given, including the generation of edges and
photos, the generation of faces with different attributes, etc. The
experimental results show that our model can successfully generate
corresponding images for all these tasks, while outperforms the baseline
methods. We also introduce an approach of applying RegCGAN to unsupervised
domain adaptation.
Paraphrase to Explicate: Revealing Implicit Noun-Compound Relations
Authors: Vered Shwartz, Ido Dagan
Date: 2018-05-07 00:00:00
Proceeding: ACL 2018 7
Tasks: General Classification

Revealing the implicit semantic relation between the constituents of a
noun-compound is important for many NLP applications. It has been addressed in
the literature either as a classification task to a set of pre-defined
relations or by producing free text paraphrases explicating the relations. Most
existing paraphrasing methods lack the ability to generalize, and have a hard
time interpreting infrequent or new noun-compounds. We propose a neural model
that generalizes better by representing paraphrases in a continuous space,
generalizing for both unseen noun-compounds and rare paraphrases. Our model
helps improving performance on both the noun-compound paraphrasing and
classification tasks.
Dual CNN Models for Unsupervised Monocular Depth Estimation
Authors: Vamshi Krishna Repala, Shiv Ram Dubey
Date: 2018-04-16 00:00:00
Tasks: Depth Estimation, Image Reconstruction, Monocular Depth Estimation, Unsupervised Monocular Depth Estimation

The unsupervised depth estimation is the recent trend by utilizing the binocular stereo images to get rid of depth map ground truth. In unsupervised depth computation, the disparity images are generated by training the CNN with an image reconstruction loss. In this paper, a dual CNN based model is presented for unsupervised depth estimation with 6 losses (DNM6) with individual CNN for each view to generate the corresponding disparity map. The proposed dual CNN model is also extended with 12 losses (DNM12) by utilizing the cross disparities. The presented DNM6 and DNM12 models are experimented over KITTI driving and Cityscapes urban database and compared with the recent state-of-the-art result of unsupervised depth estimation. The code is available at: https://github.com/ishmav16/Dual-CNN-Models-for-Unsupervised-Monocular-Depth-Estimation.
ECO: Efficient Convolutional Network for Online Video Understanding
Authors: Mohammadreza Zolfaghari, Kamaljeet Singh, Thomas Brox
Date: 2018-04-24 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Classification, Action Recognition, General Classification, Retrieval, Video Captioning, Video Classification, Video Retrieval, Video Understanding

The state of the art in video understanding suffers from two problems: (1)
The major part of reasoning is performed locally in the video, therefore, it
misses important relationships within actions that span several seconds. (2)
While there are local methods with fast per-frame processing, the processing of
the whole video is not efficient and hampers fast video retrieval or online
classification of long-term activities. In this paper, we introduce a network
architecture that takes long-term content into account and enables fast
per-video processing at the same time. The architecture is based on merging
long-term content already in the network rather than in a post-hoc fusion.
Together with a sampling strategy, which exploits that neighboring frames are
largely redundant, this yields high-quality action classification and video
captioning at up to 230 videos per second, where each video can consist of a
few hundred frames. The approach achieves competitive performance across all
datasets while being 10x to 80x faster than state-of-the-art methods.