pwc-archive/papers-with-abstracts huggingface dataset:
Dense 3D Face Decoding over 2500FPS: Joint Texture & Shape Convolutional Mesh Decoders
Authors: Yuxiang Zhou, Jiankang Deng, Irene Kotsia, Stefanos Zafeiriou
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6

3D Morphable Models (3DMMs) are statistical models that represent facial
texture and shape variations using a set of linear bases and more particular
Principal Component Analysis (PCA). 3DMMs were used as statistical priors for
reconstructing 3D faces from images by solving non-linear least square
optimization problems. Recently, 3DMMs were used as generative models for
training non-linear mappings (\ie, regressors) from image to the parameters of
the models via Deep Convolutional Neural Networks (DCNNs). Nevertheless, all of
the above methods use either fully connected layers or 2D convolutions on
parametric unwrapped UV spaces leading to large networks with many parameters.
In this paper, we present the first, to the best of our knowledge, non-linear
3DMMs by learning joint texture and shape auto-encoders using direct mesh
convolutions. We demonstrate how these auto-encoders can be used to train very
light-weight models that perform Coloured Mesh Decoding (CMD) in-the-wild at a
speed of over 2500 FPS.
Taco-VC: A Single Speaker Tacotron based Voice Conversion with Limited Data
Authors: Roee Levy Leshem, Raja Giryes
Date: 2019-04-06 00:00:00
Tasks: Phoneme Recognition, Speech Enhancement, Voice Conversion

This paper introduces Taco-VC, a novel architecture for voice conversion based on Tacotron synthesizer, which is a sequence-to-sequence with attention model. The training of multi-speaker voice conversion systems requires a large number of resources, both in training and corpus size. Taco-VC is implemented using a single speaker Tacotron synthesizer based on Phonetic PosteriorGrams (PPGs) and a single speaker WaveNet vocoder conditioned on mel spectrograms. To enhance the converted speech quality, and to overcome over-smoothing, the outputs of Tacotron are passed through a novel speechenhancement network, which is composed of a combination of the phoneme recognition and Tacotron networks. Our system is trained just with a single speaker corpus and adapts to new speakers using only a few minutes of training data. Using mid-size public datasets, our method outperforms the baseline in the VCC 2018 SPOKE non-parallel voice conversion task and achieves competitive results compared to multi-speaker networks trained on large private datasets.
Tracking Discrete and Continuous Entity State for Process Understanding
Authors: Aditya Gupta, Greg Durrett
Date: 2019-04-06 00:00:00
Proceeding: WS 2019 6
Tasks: Procedural Text Understanding

Procedural text, which describes entities and their interactions as they
undergo some process, depicts entities in a uniquely nuanced way. First, each
entity may have some observable discrete attributes, such as its state or
location; modeling these involves imposing global structure and enforcing
consistency. Second, an entity may have properties which are not made explicit
but can be effectively induced and tracked by neural networks. In this paper,
we propose a structured neural architecture that reflects this dual nature of
entity evolution. The model tracks each entity recurrently, updating its hidden
continuous representation at each step to contain relevant state information.
The global discrete state structure is explicitly modeled with a neural CRF
over the changing hidden representation of the entity. This CRF can explicitly
capture constraints on entity states over time, enforcing that, for example, an
entity cannot move to a location after it is destroyed. We evaluate the
performance of our proposed model on QA tasks over process paragraphs in the
ProPara dataset and find that our model achieves state-of-the-art results.
Instance-Level Meta Normalization
Authors: Songhao Jia, Ding-Jie Chen, Hwann-Tzong Chen
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6

This paper presents a normalization mechanism called Instance-Level Meta
Normalization (ILM~Norm) to address a learning-to-normalize problem. ILM~Norm
learns to predict the normalization parameters via both the feature
feed-forward and the gradient back-propagation paths. ILM~Norm provides a meta
normalization mechanism and has several good properties. It can be easily
plugged into existing instance-level normalization schemes such as Instance
Normalization, Layer Normalization, or Group Normalization. ILM~Norm normalizes
each instance individually and therefore maintains high performance even when
small mini-batch is used. The experimental results show that ILM~Norm well
adapts to different network architectures and tasks, and it consistently
improves the performance of the original models. The code is available at
url{https://github.com/Gasoonjia/ILM-Norm.
Split Batch Normalization: Improving Semi-Supervised Learning under Domain Shift
Authors: Michał Zając, Konrad Żołna, Stanisław Jastrzębski
Date: 2019-04-06 00:00:00
Proceeding: ICLR Workshop LLD 2019
Tasks: image-classification, Image Classification

Recent work has shown that using unlabeled data in semi-supervised learning
is not always beneficial and can even hurt generalization, especially when
there is a class mismatch between the unlabeled and labeled examples. We
investigate this phenomenon for image classification on the CIFAR-10 and the
ImageNet datasets, and with many other forms of domain shifts applied (e.g.
salt-and-pepper noise). Our main contribution is Split Batch Normalization
(Split-BN), a technique to improve SSL when the additional unlabeled data comes
from a shifted distribution. We achieve it by using separate batch
normalization statistics for unlabeled examples. Due to its simplicity, we
recommend it as a standard practice. Finally, we analyse how domain shift
affects the SSL training process. In particular, we find that during training
the statistics of hidden activations in late layers become markedly different
between the unlabeled and the labeled examples.
Team QCRI-MIT at SemEval-2019 Task 4: Propaganda Analysis Meets Hyperpartisan News Detection
Authors: Abdelrhman Saleh, Ramy Baly, Alberto Barrón-Cedeño, Giovanni Da San Martino, Mitra Mohtarami, Preslav Nakov, James Glass
Date: 2019-04-06 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: regression

In this paper, we describe our submission to SemEval-2019 Task 4 on
Hyperpartisan News Detection. Our system relies on a variety of engineered
features originally used to detect propaganda. This is based on the assumption
that biased messages are propagandistic in the sense that they promote a
particular political cause or viewpoint. We trained a logistic regression model
with features ranging from simple bag-of-words to vocabulary richness and text
readability features. Our system achieved 72.9% accuracy on the test data that
is annotated manually and 60.8% on the test data that is annotated with distant
supervision. Additional experiments showed that significant performance
improvements can be achieved with better feature pre-processing.
C2S2: Cost-aware Channel Sparse Selection for Progressive Network Pruning
Authors: Chih-Yao Chiu, Hwann-Tzong Chen, Tyng-Luh Liu
Date: 2019-04-06 00:00:00
Tasks: channel selection, image-classification, Image Classification, Network Pruning, Semantic Segmentation

This paper describes a channel-selection approach for simplifying deep neural
networks. Specifically, we propose a new type of generic network layer, called
pruning layer, to seamlessly augment a given pre-trained model for compression.
Each pruning layer, comprising $1 \times 1$ depth-wise kernels, is represented
with a dual format: one is real-valued and the other is binary. The former
enables a two-phase optimization process of network pruning to operate with an
end-to-end differentiable network, and the latter yields the mask information
for channel selection. Our method progressively performs the pruning task
layer-wise, and achieves channel selection according to a sparsity criterion to
favor pruning more channels. We also develop a cost-aware mechanism to prevent
the compression from sacrificing the expected network performance. Our results
for compressing several benchmark deep networks on image classification and
semantic segmentation are comparable to those by state-of-the-art.
DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detection
Authors: Yuemeng Li, Yong Fan
Date: 2019-04-06 00:00:00
Tasks: Automated Pulmonary Nodule Detection And Classification, Computed Tomography (CT), Decoder, Lung Nodule Detection, Region Proposal

Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans. It remains challenging to build nodule detection deep learning models with good generalization performance due to unbalanced positive and negative samples. In order to overcome this problem and further improve state-of-the-art nodule detection methods, we develop a novel deep 3D convolutional neural network with an Encoder-Decoder structure in conjunction with a region proposal network. Particularly, we utilize a dynamically scaled cross entropy loss to reduce the false positive rate and combat the sample imbalance problem associated with nodule detection. We adopt the squeeze-and-excitation structure to learn effective image features and utilize inter-dependency information of different feature maps. We have validated our method based on publicly available CT scans with manually labelled ground-truth obtained from LIDC/IDRI dataset and its subset LUNA16 with thinner slices. Ablation studies and experimental results have demonstrated that our method could outperform state-of-the-art nodule detection methods by a large margin.
LP-3DCNN: Unveiling Local Phase in 3D Convolutional Neural Networks
Authors: Sudhakar Kumawat, Shanmuganathan Raman
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Recognition, Position, Temporal Action Localization

Traditional 3D Convolutional Neural Networks (CNNs) are computationally
expensive, memory intensive, prone to overfit, and most importantly, there is a
need to improve their feature learning capabilities. To address these issues,
we propose Rectified Local Phase Volume (ReLPV) block, an efficient alternative
to the standard 3D convolutional layer. The ReLPV block extracts the phase in a
3D local neighborhood (e.g., 3x3x3) of each position of the input map to obtain
the feature maps. The phase is extracted by computing 3D Short Term Fourier
Transform (STFT) at multiple fixed low frequency points in the 3D local
neighborhood of each position. These feature maps at different frequency points
are then linearly combined after passing them through an activation function.
The ReLPV block provides significant parameter savings of at least, 3^3 to 13^3
times compared to the standard 3D convolutional layer with the filter sizes
3x3x3 to 13x13x13, respectively. We show that the feature learning capabilities
of the ReLPV block are significantly better than the standard 3D convolutional
layer. Furthermore, it produces consistently better results across different 3D
data representations. We achieve state-of-the-art accuracy on the volumetric
ModelNet10 and ModelNet40 datasets while utilizing only 11% parameters of the
current state-of-the-art. We also improve the state-of-the-art on the UCF-101
split-1 action recognition dataset by 5.68% (when trained from scratch) while
using only 15% of the parameters of the state-of-the-art. The project webpage
is available at https://sites.google.com/view/lp-3dcnn/home.
VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research
Authors: Xin Wang, Jiawei Wu, Junkun Chen, Lei LI, Yuan-Fang Wang, William Yang Wang
Date: 2019-04-06 00:00:00
Proceeding: ICCV 2019 10
Tasks: Machine Translation, Translation, Video Captioning, Video Description, Video-Guided Machine Translation

We present a new large-scale multilingual video description dataset, VATEX, which contains over 41,250 videos and 825,000 captions in both English and Chinese. Among the captions, there are over 206,000 English-Chinese parallel translation pairs. Compared to the widely-used MSR-VTT dataset, VATEX is multilingual, larger, linguistically complex, and more diverse in terms of both video and natural language descriptions. We also introduce two tasks for video-and-language research based on VATEX: (1) Multilingual Video Captioning, aimed at describing a video in various languages with a compact unified captioning model, and (2) Video-guided Machine Translation, to translate a source language description into the target language using the video information as additional spatiotemporal context. Extensive experiments on the VATEX dataset show that, first, the unified multilingual model can not only produce both English and Chinese descriptions for a video more efficiently, but also offer improved performance over the monolingual models. Furthermore, we demonstrate that the spatiotemporal video context can be effectively utilized to align source and target languages and thus assist machine translation. In the end, we discuss the potentials of using VATEX for other video-and-language research.
A Compendium on Network and Host based Intrusion Detection Systems
Authors: Rahul-Vigneswaran K, Prabaharan Poornachandran, Soman Kp
Date: 2019-04-06 00:00:00
Tasks: Deep Learning, Intrusion Detection

The techniques of deep learning have become the state of the art methodology
for executing complicated tasks from various domains of computer vision,
natural language processing, and several other areas. Due to its rapid
development and promising benchmarks in those fields, researchers started
experimenting with this technique to perform in the area of, especially in
intrusion detection related tasks. Deep learning is a subset and a natural
extension of classical Machine learning and an evolved model of neural
networks. This paper contemplates and discusses all the methodologies related
to the leading edge Deep learning and Neural network models purposing to the
arena of Intrusion Detection Systems.
Self-supervised speaker embeddings
Authors: Themos Stafylakis, Johan Rohdin, Oldrich Plchot, Petr Mizera, Lukas Burget
Date: 2019-04-06 00:00:00
Tasks: Decoder, General Classification

Contrary to i-vectors, speaker embeddings such as x-vectors are incapable of
leveraging unlabelled utterances, due to the classification loss over training
speakers. In this paper, we explore an alternative training strategy to enable
the use of unlabelled utterances in training. We propose to train speaker
embedding extractors via reconstructing the frames of a target speech segment,
given the inferred embedding of another speech segment of the same utterance.
We do this by attaching to the standard speaker embedding extractor a decoder
network, which we feed not merely with the speaker embedding, but also with the
estimated phone sequence of the target frame sequence. The reconstruction loss
can be used either as a single objective, or be combined with the standard
speaker classification loss. In the latter case, it acts as a regularizer,
encouraging generalizability to speakers unseen during training. In all cases,
the proposed architectures are trained from scratch and in an end-to-end
fashion. We demonstrate the benefits from the proposed approach on VoxCeleb and
Speakers in the wild, and we report notable improvements over the baseline.
When AWGN-based Denoiser Meets Real Noises
Authors: Yuqian Zhou, Jianbo Jiao, Haibin Huang, Yang Wang, Jue Wang, Honghui Shi, Thomas Huang
Date: 2019-04-06 00:00:00
Tasks: Denoising

Discriminative learning-based image denoisers have achieved promising performance on synthetic noises such as Additive White Gaussian Noise (AWGN). The synthetic noises adopted in most previous work are pixel-independent, but real noises are mostly spatially/channel-correlated and spatially/channel-variant. This domain gap yields unsatisfied performance on images with real noises if the model is only trained with AWGN. In this paper, we propose a novel approach to boost the performance of a real image denoiser which is trained only with synthetic pixel-independent noise data dominated by AWGN. First, we train a deep model that consists of a noise estimator and a denoiser with mixed AWGN and Random Value Impulse Noise (RVIN). We then investigate Pixel-shuffle Down-sampling (PD) strategy to adapt the trained model to real noises. Extensive experiments demonstrate the effectiveness and generalization of the proposed approach. Notably, our method achieves state-of-the-art performance on real sRGB images in the DND benchmark among models trained with synthetic noises. Codes are available at https://github.com/yzhouas/PD-Denoising-pytorch.
SDRSAC: Semidefinite-Based Randomized Approach for Robust Point Cloud Registration without Correspondences
Authors: Huu Le, Thanh-Toan Do, Tuan Hoang, Ngai-Man Cheung
Date: 2019-04-06 00:00:00
Tasks: Graph Matching, Point Cloud Registration

This paper presents a novel randomized algorithm for robust point cloud
registration without correspondences. Most existing registration approaches
require a set of putative correspondences obtained by extracting invariant
descriptors. However, such descriptors could become unreliable in noisy and
contaminated settings. In these settings, methods that directly handle input
point sets are preferable. Without correspondences, however, conventional
randomized techniques require a very large number of samples in order to reach
satisfactory solutions. In this paper, we propose a novel approach to address
this problem. In particular, our work enables the use of randomized methods for
point cloud registration without the need of putative correspondences. By
considering point cloud alignment as a special instance of graph matching and
employing an efficient semi-definite relaxation, we propose a novel sampling
mechanism, in which the size of the sampled subsets can be larger-than-minimal.
Our tight relaxation scheme enables fast rejection of the outliers in the
sampled sets, resulting in high-quality hypotheses. We conduct extensive
experiments to demonstrate that our approach outperforms other state-of-the-art
methods. Importantly, our proposed method serves as a generic framework which
can be extended to problems with known correspondences.
The Mathematics of Text Structure
Authors: Bob Coecke
Date: 2019-04-06 00:00:00
Tasks: Sentence

In previous work we gave a mathematical foundation, referred to as DisCoCat, for how words interact in a sentence in order to produce the meaning of that sentence. To do so, we exploited the perfect structural match of grammar and categories of meaning spaces. Here, we give a mathematical foundation, referred to as DisCoCirc, for how sentences interact in texts in order to produce the meaning of that text. First we revisit DisCoCat. While in DisCoCat all meanings are fixed as states (i.e. have no input), in DisCoCirc word meanings correspond to a type, or system, and the states of this system can evolve. Sentences are gates within a circuit which update the variable meanings of those words. Like in DisCoCat, word meanings can live in a variety of spaces e.g. propositional, vectorial, or cognitive. The compositional structure are string diagrams representing information flows, and an entire text yields a single string diagram in which word meanings lift to the meaning of an entire text. While the developments in this paper are independent of a physical embodiment (cf. classical vs. quantum computing), both the compositional formalism and suggested meaning model are highly quantum-inspired, and implementation on a quantum computer would come with a range of benefits. We also praise Jim Lambek for his role in mathematical linguistics in general, and the development of the DisCo program more specifically.
Few-Shot Learning via Saliency-guided Hallucination of Samples
Authors: Hongguang Zhang, Jing Zhang, Piotr Koniusz
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Few-Shot Learning, Hallucination

Learning new concepts from a few of samples is a standard challenge in
computer vision. The main directions to improve the learning ability of
few-shot training models include (i) a robust similarity learning and (ii)
generating or hallucinating additional data from the limited existing samples.
In this paper, we follow the latter direction and present a novel data
hallucination model. Currently, most datapoint generators contain a specialized
network (i.e., GAN) tasked with hallucinating new datapoints, thus requiring
large numbers of annotated data for their training in the first place. In this
paper, we propose a novel less-costly hallucination method for few-shot
learning which utilizes saliency maps. To this end, we employ a saliency
network to obtain the foregrounds and backgrounds of available image samples
and feed the resulting maps into a two-stream network to hallucinate datapoints
directly in the feature space from viable foreground-background combinations.
To the best of our knowledge, we are the first to leverage saliency maps for
such a task and we demonstrate their usefulness in hallucinating additional
datapoints for few-shot learning. Our proposed network achieves the state of
the art on publicly available datasets.
Deep Stacked Hierarchical Multi-patch Network for Image Deblurring
Authors: Hongguang Zhang, Yuchao Dai, Hongdong Li, Piotr Koniusz
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Deblurring, Image Deblurring

Despite deep end-to-end learning methods have shown their superiority in
removing non-uniform motion blur, there still exist major challenges with the
current multi-scale and scale-recurrent models: 1) Deconvolution/upsampling
operations in the coarse-to-fine scheme result in expensive runtime; 2) Simply
increasing the model depth with finer-scale levels cannot improve the quality
of deblurring. To tackle the above problems, we present a deep hierarchical
multi-patch network inspired by Spatial Pyramid Matching to deal with blurry
images via a fine-to-coarse hierarchical representation. To deal with the
performance saturation w.r.t. depth, we propose a stacked version of our
multi-patch model. Our proposed basic multi-patch model achieves the
state-of-the-art performance on the GoPro dataset while enjoying a 40x faster
runtime compared to current multi-scale methods. With 30ms to process an image
at 1280x720 resolution, it is the first real-time deep motion deblurring model
for 720p images at 30fps. For stacked networks, significant improvements (over
1.2dB) are achieved on the GoPro dataset by increasing the network depth.
Moreover, by varying the depth of the stacked model, one can adapt the
performance and runtime of the same network for different application
scenarios.
Embodied Question Answering in Photorealistic Environments with Point Cloud Perception
Authors: Erik Wijmans, Samyak Datta, Oleksandr Maksymets, Abhishek Das, Georgia Gkioxari, Stefan Lee, Irfan Essa, Devi Parikh, Dhruv Batra
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Embodied Question Answering, Question Answering

To help bridge the gap between internet vision-style problems and the goal of
vision for embodied perception we instantiate a large-scale navigation task --
Embodied Question Answering [1] in photo-realistic environments (Matterport
3D). We thoroughly study navigation policies that utilize 3D point clouds, RGB
images, or their combination. Our analysis of these models reveals several key
findings. We find that two seemingly naive navigation baselines, forward-only
and random, are strong navigators and challenging to outperform, due to the
specific choice of the evaluation setting presented by [1]. We find a novel
loss-weighting scheme we call Inflection Weighting to be important when
training recurrent models for navigation with behavior cloning and are able to
out perform the baselines with this technique. We find that point clouds
provide a richer signal than RGB images for learning obstacle avoidance,
motivating the use (and continued study) of 3D deep learning models for
embodied navigation.