pwc-archive/papers-with-abstracts huggingface dataset:
Center and Scale Prediction: Anchor-free Approach for Pedestrian and Face Detection
Authors: Wei Liu, Irtiza Hasan, Shengcai Liao
Date: 2019-04-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: Face Detection, object-detection, Object Detection, Pedestrian Detection

Object detection generally requires sliding-window classifiers in tradition or anchor box based predictions in modern deep learning approaches. However, either of these approaches requires tedious configurations in boxes. In this paper, we provide a new perspective where detecting objects is motivated as a high-level semantic feature detection task. Like edges, corners, blobs and other feature detectors, the proposed detector scans for feature points all over the image, for which the convolution is naturally suited. However, unlike these traditional low-level features, the proposed detector goes for a higher-level abstraction, that is, we are looking for central points where there are objects, and modern deep models are already capable of such a high-level semantic abstraction. Besides, like blob detection, we also predict the scales of the central points, which is also a straightforward convolution. Therefore, in this paper, pedestrian and face detection is simplified as a straightforward center and scale prediction task through convolutions. This way, the proposed method enjoys a box-free setting. Though structurally simple, it presents competitive accuracy on several challenging benchmarks, including pedestrian detection and face detection. Furthermore, a cross-dataset evaluation is performed, demonstrating a superior generalization ability of the proposed method. Code and models can be accessed at (https://github.com/liuwei16/CSP and https://github.com/hasanirtiza/Pedestron).
Weighted Automata Extraction from Recurrent Neural Networks via Regression on State Spaces
Authors: Takamasa Okudono, Masaki Waga, Taro Sekiyama, Ichiro Hasuo
Date: 2019-04-05 00:00:00
Tasks: regression

We present a method to extract a weighted finite automaton (WFA) from a recurrent neural network (RNN). Our algorithm is based on the WFA learning algorithm by Balle and Mohri, which is in turn an extension of Angluin's classic \lstar algorithm. Our technical novelty is in the use of \emph{regression} methods for the so-called equivalence queries, thus exploiting the internal state space of an RNN to prioritize counterexample candidates. This way we achieve a quantitative/weighted extension of the recent work by Weiss, Goldberg and Yahav that extracts DFAs. We experimentally evaluate the accuracy, expressivity and efficiency of the extracted WFAs.
Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models --- Is Single-Corpus Evaluation Enough?
Authors: Masato Mita, Tomoya Mizumoto, Masahiro Kaneko, Ryo Nagata, Kentaro Inui
Date: 2019-04-05 00:00:00
Proceeding: NAACL 2019 6
Tasks: Grammatical Error Correction, NMT

This study explores the necessity of performing cross-corpora evaluation for
grammatical error correction (GEC) models. GEC models have been previously
evaluated based on a single commonly applied corpus: the CoNLL-2014 benchmark.
However, the evaluation remains incomplete because the task difficulty varies
depending on the test corpus and conditions such as the proficiency levels of
the writers and essay topics. To overcome this limitation, we evaluate the
performance of several GEC models, including NMT-based (LSTM, CNN, and
transformer) and an SMT-based model, against various learner corpora
(CoNLL-2013, CoNLL-2014, FCE, JFLEG, ICNALE, and KJ). Evaluation results reveal
that the models' rankings considerably vary depending on the corpus, indicating
that single-corpus evaluation is insufficient for GEC models.
Simultaneous Dimensionality and Complexity Model Selection for Spectral Graph Clustering
Authors: Congyuan Yang, Carey E. Priebe, Youngser Park, David J. Marchette
Date: 2019-04-05 00:00:00
Tasks: Clustering, Graph Clustering, Model Selection, Spectral Graph Clustering, Stochastic Block Model

Our problem of interest is to cluster vertices of a graph by identifying underlying community structure. Among various vertex clustering approaches, spectral clustering is one of the most popular methods because it is easy to implement while often outperforming more traditional clustering algorithms. However, there are two inherent model selection problems in spectral clustering, namely estimating both the embedding dimension and number of clusters. This paper attempts to address the issue by establishing a novel model selection framework specifically for vertex clustering on graphs under a stochastic block model. The first contribution is a probabilistic model which approximates the distribution of the extended spectral embedding of a graph. The model is constructed based on a theoretical result of asymptotic normality of the informative part of the embedding, and on a simulation result providing a conjecture for the limiting behavior of the redundant part of the embedding. The second contribution is a simultaneous model selection framework. In contrast with the traditional approaches, our model selection procedure estimates embedding dimension and number of clusters simultaneously. Based on our conjectured distributional model, a theorem on the consistency of the estimates of model parameters is presented, providing support for the validity of our method. Algorithms for our simultaneous model selection for vertex clustering are proposed, demonstrating superior performance in simulation experiments. We illustrate our method via application to a collection of brain graphs.
Simulation of virtual cohorts increases predictive accuracy of cognitive decline in MCI subjects
Authors: Igor Koval, Stéphanie Allassonnière, Stanley Durrleman
Date: 2019-04-05 00:00:00
Tasks: Data Augmentation

The ability to predict the progression of biomarkers, notably in NDD, is
limited by the size of the longitudinal data sets, in terms of number of
patients, number of visits per patients and total follow-up time. To this end,
we introduce a data augmentation technique that is able to reproduce the
variability seen in a longitudinal training data set and simulate continuous
biomarkers trajectories for any number of virtual patients. Thanks to this
simulation framework, we propose to transform the training set into a simulated
data set with more patients, more time-points per patient and longer follow-up
duration. We illustrate this approach on the prediction of the MMSE of MCI
subjects of the ADNI data set. We show that it allows to reach predictions with
errors comparable to the noise in the data, estimated in test/retest studies,
achieving a improvement of 37% of the mean absolute error compared to the same
non-augmented model.
Branched Multi-Task Networks: Deciding What Layers To Share
Authors: Simon Vandenhende, Stamatios Georgoulis, Bert de Brabandere, Luc van Gool
Date: 2019-04-05 00:00:00
Proceeding: ICLR 2020 1
Tasks: Multi-Task Learning, Neural Architecture Search

In the context of multi-task learning, neural networks with branched architectures have often been employed to jointly tackle the tasks at hand. Such ramified networks typically start with a number of shared layers, after which different tasks branch out into their own sequence of layers. Understandably, as the number of possible network configurations is combinatorially large, deciding what layers to share and where to branch out becomes cumbersome. Prior works have either relied on ad hoc methods to determine the level of layer sharing, which is suboptimal, or utilized neural architecture search techniques to establish the network design, which is considerably expensive. In this paper, we go beyond these limitations and propose an approach to automatically construct branched multi-task networks, by leveraging the employed tasks' affinities. Given a specific budget, i.e. number of learnable parameters, the proposed approach generates architectures, in which shallow layers are task-agnostic, whereas deeper ones gradually grow more task-specific. Extensive experimental analysis across numerous, diverse multi-tasking datasets shows that, for a given budget, our method consistently yields networks with the highest performance, while for a certain performance threshold it requires the least amount of learnable parameters.
3D LiDAR and Stereo Fusion using Stereo Matching Network with Conditional Cost Volume Normalization
Authors: Tsun-Hsuan Wang, Hou-Ning Hu, Chieh Hubert Lin, Yi-Hsuan Tsai, Wei-Chen Chiu, Min Sun
Date: 2019-04-05 00:00:00
Tasks: Depth Completion, Stereo-LiDAR Fusion, Stereo Matching, Stereo Matching Hand

The complementary characteristics of active and passive depth sensing
techniques motivate the fusion of the Li-DAR sensor and stereo camera for
improved depth perception. Instead of directly fusing estimated depths across
LiDAR and stereo modalities, we take advantages of the stereo matching network
with two enhanced techniques: Input Fusion and Conditional Cost Volume
Normalization (CCVNorm) on the LiDAR information. The proposed framework is
generic and closely integrated with the cost volume component that is commonly
utilized in stereo matching neural networks. We experimentally verify the
efficacy and robustness of our method on the KITTI Stereo and Depth Completion
datasets, obtaining favorable performance against various fusion strategies.
Moreover, we demonstrate that, with a hierarchical extension of CCVNorm, the
proposed method brings only slight overhead to the stereo matching network in
terms of computation time and model size. For project page, see
https://zswang666.github.io/Stereo-LiDAR-CCVNorm-Project-Page/
Point-to-Point Video Generation
Authors: Tsun-Hsuan Wang, Yen-Chi Cheng, Chieh Hubert Lin, Hwann-Tzong Chen, Min Sun
Date: 2019-04-05 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Manipulation, Video Editing, Video Generation

While image manipulation achieves tremendous breakthroughs (e.g., generating realistic faces) in recent years, video generation is much less explored and harder to control, which limits its applications in the real world. For instance, video editing requires temporal coherence across multiple clips and thus poses both start and end constraints within a video sequence. We introduce point-to-point video generation that controls the generation process with two control points: the targeted start- and end-frames. The task is challenging since the model not only generates a smooth transition of frames, but also plans ahead to ensure that the generated end-frame conforms to the targeted end-frame for videos of various length. We propose to maximize the modified variational lower bound of conditional data likelihood under a skip-frame training strategy. Our model can generate sequences such that their end-frame is consistent with the targeted end-frame without loss of quality and diversity. Extensive experiments are conducted on Stochastic Moving MNIST, Weizmann Human Action, and Human3.6M to evaluate the effectiveness of the proposed method. We demonstrate our method under a series of scenarios (e.g., dynamic length generation) and the qualitative results showcase the potential and merits of point-to-point generation. For project page, see https://zswang666.github.io/P2PVG-Project-Page/
Blind Deconvolution Microscopy Using Cycle Consistent CNN with Explicit PSF Layer
Authors: Sungjun Lim, Sang-Eun Lee, Sunghoe Chang, Jong Chul Ye
Date: 2019-04-05 00:00:00

Deconvolution microscopy has been extensively used to improve the resolution
of the widefield fluorescent microscopy. Conventional approaches, which usually
require the point spread function (PSF) measurement or blind estimation, are
however computationally expensive. Recently, CNN based approaches have been
explored as a fast and high performance alternative. In this paper, we present
a novel unsupervised deep neural network for blind deconvolution based on cycle
consistency and PSF modeling layers. In contrast to the recent CNN approaches
for similar problem, the explicit PSF modeling layers improve the robustness of
the algorithm. Experimental results confirm the efficacy of the algorithm.
Deep Predictive Video Compression with Bi-directional Prediction
Authors: Woonsung Park, Munchurl Kim
Date: 2019-04-05 00:00:00
Tasks: Decoder, Image Compression, MS-SSIM, Optical Flow Estimation, Prediction, SSIM, Video Compression

Recently, deep image compression has shown a big progress in terms of coding
efficiency and image quality improvement. However, relatively less attention
has been put on video compression using deep learning networks. In the paper,
we first propose a deep learning based bi-predictive coding network, called
BP-DVC Net, for video compression. Learned from the lesson of the conventional
video coding, a B-frame coding structure is incorporated in our BP-DVC Net.
While the bi-predictive coding in the conventional video codecs requires to
transmit to decoder sides the motion vectors for block motion and the residues
from prediction, our BP-DVC Net incorporates optical flow estimation networks
in both encoder and decoder sides so as not to transmit the motion information
to the decoder sides for coding efficiency improvement. Also, a bi-prediction
network in the BP-DVC Net is proposed and used to precisely predict the current
frame and to yield the resulting residues as small as possible. Furthermore,
our BP-DVC Net allows for the compressive feature maps to be entropy-coded
using the temporal context among the feature maps of adjacent frames. The
BP-DVC Net has an end-to-end video compression architecture with newly designed
flow and prediction losses. Experimental results show that the compression
performance of our proposed method is comparable to those of H.264, HEVC in
terms of PSNR and MS-SSIM.
Dense Haze: A benchmark for image dehazing with dense-haze and haze-free images
Authors: Codruta O. Ancuti, Cosmin Ancuti, Mateu Sbert, Radu Timofte
Date: 2019-04-05 00:00:00
Tasks: Image Dehazing, Single Image Dehazing

Single image dehazing is an ill-posed problem that has recently drawn
important attention. Despite the significant increase in interest shown for
dehazing over the past few years, the validation of the dehazing methods
remains largely unsatisfactory, due to the lack of pairs of real hazy and
corresponding haze-free reference images. To address this limitation, we
introduce Dense-Haze - a novel dehazing dataset. Characterized by dense and
homogeneous hazy scenes, Dense-Haze contains 33 pairs of real hazy and
corresponding haze-free images of various outdoor scenes. The hazy scenes have
been recorded by introducing real haze, generated by professional haze
machines. The hazy and haze-free corresponding scenes contain the same visual
content captured under the same illumination parameters. Dense-Haze dataset
aims to push significantly the state-of-the-art in single-image dehazing by
promoting robust methods for real and various hazy scenes. We also provide a
comprehensive qualitative and quantitative evaluation of state-of-the-art
single image dehazing techniques based on the Dense-Haze dataset. Not
surprisingly, our study reveals that the existing dehazing techniques perform
poorly for dense homogeneous hazy scenes and that there is still much room for
improvement.
WaveCycleGAN2: Time-domain Neural Post-filter for Speech Waveform Generation
Authors: Kou Tanaka, Hirokazu Kameoka, Takuhiro Kaneko, Nobukatsu Hojo
Date: 2019-04-05 00:00:00
Tasks: Speech Synthesis

WaveCycleGAN has recently been proposed to bridge the gap between natural and
synthesized speech waveforms in statistical parametric speech synthesis and
provides fast inference with a moving average model rather than an
autoregressive model and high-quality speech synthesis with the adversarial
training. However, the human ear can still distinguish the processed speech
waveforms from natural ones. One possible cause of this distinguishability is
the aliasing observed in the processed speech waveform via down/up-sampling
modules. To solve the aliasing and provide higher quality speech synthesis, we
propose WaveCycleGAN2, which 1) uses generators without down/up-sampling
modules and 2) combines discriminators of the waveform domain and acoustic
parameter domain. The results show that the proposed method 1) alleviates the
aliasing well, 2) is useful for both speech waveforms generated by
analysis-and-synthesis and statistical parametric speech synthesis, and 3)
achieves a mean opinion score comparable to those of natural speech and speech
synthesized by WaveNet (open WaveNet) and WaveGlow while processing speech
samples at a rate of more than 150 kHz on an NVIDIA Tesla P100.
Snap and Find: Deep Discrete Cross-domain Garment Image Retrieval
Authors: Yadan Luo, Ziwei Wang, Zi Huang, Yang Yang, Huimin Lu
Date: 2019-04-05 00:00:00
Tasks: Attribute, Image Retrieval, Retrieval

With the increasing number of online stores, there is a pressing need for
intelligent search systems to understand the item photos snapped by customers
and search against large-scale product databases to find their desired items.
However, it is challenging for conventional retrieval systems to match up the
item photos captured by customers and the ones officially released by stores,
especially for garment images. To bridge the customer- and store- provided
garment photos, existing studies have been widely exploiting the clothing
attributes (\textit{e.g.,} black) and landmarks (\textit{e.g.,} collar) to
learn a common embedding space for garment representations. Unfortunately they
omit the sequential correlation of attributes and consume large quantity of
human labors to label the landmarks. In this paper, we propose a deep
multi-task cross-domain hashing termed \textit{DMCH}, in which cross-domain
embedding and sequential attribute learning are modeled simultaneously.
Sequential attribute learning not only provides the semantic guidance for
embedding, but also generates rich attention on discriminative local details
(\textit{e.g.,} black buttons) of clothing items without requiring extra
landmark labels. This leads to promising performance and 306$\times$ boost on
efficiency when compared with the state-of-the-art models, which is
demonstrated through rigorous experiments on two public fashion datasets.
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
Authors: Yinpeng Dong, Tianyu Pang, Hang Su, Jun Zhu
Date: 2019-04-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: Translation

Deep neural networks are vulnerable to adversarial examples, which can
mislead classifiers by adding imperceptible perturbations. An intriguing
property of adversarial examples is their good transferability, making
black-box attacks feasible in real-world applications. Due to the threat of
adversarial attacks, many methods have been proposed to improve the robustness.
Several state-of-the-art defenses are shown to be robust against transferable
adversarial examples. In this paper, we propose a translation-invariant attack
method to generate more transferable adversarial examples against the defense
models. By optimizing a perturbation over an ensemble of translated images, the
generated adversarial example is less sensitive to the white-box model being
attacked and has better transferability. To improve the efficiency of attacks,
we further show that our method can be implemented by convolving the gradient
at the untranslated image with a pre-defined kernel. Our method is generally
applicable to any gradient-based attack method. Extensive experiments on the
ImageNet dataset validate the effectiveness of the proposed method. Our best
attack fools eight state-of-the-art defenses at an 82% success rate on average
based only on the transferability, demonstrating the insecurity of the current
defense techniques.
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
Authors: Dimitrios Stamoulis, Ruizhou Ding, Di Wang, Dimitrios Lymberopoulos, Bodhi Priyantha, Jie Liu, Diana Marculescu
Date: 2019-04-05 00:00:00
Tasks: General Classification, GPU, image-classification, Image Classification, Neural Architecture Search

Can we automatically design a Convolutional Network (ConvNet) with the
highest image classification accuracy under the runtime constraint of a mobile
device? Neural architecture search (NAS) has revolutionized the design of
hardware-efficient ConvNets by automating this process. However, the NAS
problem remains challenging due to the combinatorially large design space,
causing a significant searching time (at least 200 GPU-hours). To alleviate
this complexity, we propose Single-Path NAS, a novel differentiable NAS method
for designing hardware-efficient ConvNets in less than 4 hours. Our
contributions are as follows: 1. Single-path search space: Compared to previous
differentiable NAS methods, Single-Path NAS uses one single-path
over-parameterized ConvNet to encode all architectural decisions with shared
convolutional kernel parameters, hence drastically decreasing the number of
trainable parameters and the search cost down to few epochs. 2.
Hardware-efficient ImageNet classification: Single-Path NAS achieves 74.96%
top-1 accuracy on ImageNet with 79ms latency on a Pixel 1 phone, which is
state-of-the-art accuracy compared to NAS methods with similar constraints
(<80ms). 3. NAS efficiency: Single-Path NAS search cost is only 8 epochs (30
TPU-hours), which is up to 5,000x faster compared to prior work. 4.
Reproducibility: Unlike all recent mobile-efficient NAS methods which only
release pretrained models, we open-source our entire codebase at:
https://github.com/dstamoulis/single-path-nas.
An Attentive Survey of Attention Models
Authors: Sneha Chaudhari, Varun Mithal, Gungor Polatkan, Rohan Ramanath
Date: 2019-04-05 00:00:00
Tasks: Survey

Attention Model has now become an important concept in neural networks that has been researched within diverse application domains. This survey provides a structured and comprehensive overview of the developments in modeling attention. In particular, we propose a taxonomy which groups existing techniques into coherent categories. We review salient neural architectures in which attention has been incorporated, and discuss applications in which modeling attention has shown a significant impact. We also describe how attention has been used to improve the interpretability of neural networks. Finally, we discuss some future research directions in attention. We hope this survey will provide a succinct introduction to attention models and guide practitioners while developing approaches for their applications.