pwc-archive/papers-with-abstracts huggingface dataset:
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
Authors: Shaoshuai Shi, Xiaogang Wang, Hongsheng Li
Date: 2018-12-11 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Object Detection, object-detection, Object Detection, Object Proposal Generation, Robust 3D Object Detection

In this paper, we propose PointRCNN for 3D object detection from raw point cloud. The whole framework is composed of two stages: stage-1 for the bottom-up 3D proposal generation and stage-2 for refining proposals in the canonical coordinates to obtain the final detection results. Instead of generating proposals from RGB image or projecting point cloud to bird's view or voxels as previous methods do, our stage-1 sub-network directly generates a small number of high-quality 3D proposals from point cloud in a bottom-up manner via segmenting the point cloud of the whole scene into foreground points and background. The stage-2 sub-network transforms the pooled points of each proposal to canonical coordinates to learn better local spatial features, which is combined with global semantic features of each point learned in stage-1 for accurate box refinement and confidence prediction. Extensive experiments on the 3D detection benchmark of KITTI dataset show that our proposed architecture outperforms state-of-the-art methods with remarkable margins by using only point cloud as input. The code is available at https://github.com/sshaoshuai/PointRCNN.
Non-local Meets Global: An Integrated Paradigm for Hyperspectral Denoising
Authors: Wei He, Quanming Yao, Chao Li, Naoto Yokoya, Qibin Zhao
Date: 2018-12-11 00:00:00
Proceeding: CVPR 2019 6
Tasks: Denoising, Hyperspectral Image Denoising

Non-local low-rank tensor approximation has been developed as a
state-of-the-art method for hyperspectral image (HSI) denoising. Unfortunately,
with more spectral bands for HSI, while the running time of these methods
significantly increases, their denoising performance benefits little. In this
paper, we claim that the HSI underlines a global spectral low-rank subspace,
and the spectral subspaces of each full band patch groups should underlie this
global low-rank subspace. This motivates us to propose a unified
spatial-spectral paradigm for HSI denoising. As the new model is hard to
optimize, we further propose an efficient algorithm for optimization, which is
motivated by alternating minimization. This is done by first learning a
low-dimensional projection and the related reduced image from the noisy HSI.
Then, the non-local low-rank denoising and iterative regularization are
developed to refine the reduced image and projection, respectively. Finally,
experiments on synthetic and both real datasets demonstrate the superiority
against the other state-of-the-arts HSI denoising methods.
Unsupervised Degradation Learning for Single Image Super-Resolution
Authors: Tianyu Zhao, Wenqi Ren, Changqing Zhang, Dongwei Ren, QinGhua Hu
Date: 2018-12-11 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Deep Convolution Neural Networks (CNN) have achieved significant performance
on single image super-resolution (SR) recently. However, existing CNN-based
methods use artificially synthetic low-resolution (LR) and high-resolution (HR)
image pairs to train networks, which cannot handle real-world cases since the
degradation from HR to LR is much more complex than manually designed. To solve
this problem, we propose a real-world LR images guided bi-cycle network for
single image super-resolution, in which the bidirectional structural
consistency is exploited to train both the degradation and SR reconstruction
networks in an unsupervised way. Specifically, we propose a degradation network
to model the real-world degradation process from HR to LR via generative
adversarial networks, and these generated realistic LR images paired with
real-world HR images are exploited for training the SR reconstruction network,
forming the first cycle. Then in the second reverse cycle, consistency of
real-world LR images are exploited to further stabilize the training of SR
reconstruction and degradation networks. Extensive experiments on both
synthetic and real-world images demonstrate that the proposed algorithm
performs favorably against state-of-the-art single image SR methods.
Coarse-to-fine: A RNN-based hierarchical attention model for vehicle re-identification
Authors: Xiu-Shen Wei, Chen-Lin Zhang, Lingqiao Liu, Chunhua Shen, Jianxin Wu
Date: 2018-12-11 00:00:00
Tasks: Vehicle Re-Identification

Vehicle re-identification is an important problem and becomes desirable with
the rapid expansion of applications in video surveillance and intelligent
transportation. By recalling the identification process of human vision, we are
aware that there exists a native hierarchical dependency when humans identify
different vehicles. Specifically, humans always firstly determine one vehicle's
coarse-grained category, i.e., the car model/type. Then, under the branch of
the predicted car model/type, they are going to identify specific vehicles by
relying on subtle visual cues, e.g., customized paintings and windshield
stickers, at the fine-grained level. Inspired by the coarse-to-fine
hierarchical process, we propose an end-to-end RNN-based Hierarchical Attention
(RNN-HA) classification model for vehicle re-identification. RNN-HA consists of
three mutually coupled modules: the first module generates image
representations for vehicle images, the second hierarchical module models the
aforementioned hierarchical dependent relationship, and the last attention
module focuses on capturing the subtle visual information distinguishing
specific vehicles from each other. By conducting comprehensive experiments on
two vehicle re-identification benchmark datasets VeRi and VehicleID, we
demonstrate that the proposed model achieves superior performance over
state-of-the-art methods.
Machine Translation : From Statistical to modern Deep-learning practices
Authors: Siddhant Srivastava, Anupam Shukla, Ritu Tiwari
Date: 2018-12-11 00:00:00
Tasks: Decoder, Deep Learning, Machine Translation, Translation

Machine translation (MT) is an area of study in Natural Language processing
which deals with the automatic translation of human language, from one language
to another by the computer. Having a rich research history spanning nearly
three decades, Machine translation is one of the most sought after area of
research in the linguistics and computational community. In this paper, we
investigate the models based on deep learning that have achieved substantial
progress in recent years and becoming the prominent method in MT. We shall
discuss the two main deep-learning based Machine Translation methods, one at
component or domain level which leverages deep learning models to enhance the
efficacy of Statistical Machine Translation (SMT) and end-to-end deep learning
models in MT which uses neural networks to find correspondence between the
source and target languages using the encoder-decoder architecture. We conclude
this paper by providing a time line of the major research problems solved by
the researchers and also provide a comprehensive overview of present areas of
research in Neural Machine Translation.
Learning What to Remember: Long-term Episodic Memory Networks for Learning from Streaming Data
Authors: Hyunwoo Jung, Moonsu Han, Minki Kang, Sungju Hwang
Date: 2018-12-11 00:00:00
Proceeding: ICLR 2019 5
Tasks: Lifelong learning, Question Answering, Scheduling

Current generation of memory-augmented neural networks has limited
scalability as they cannot efficiently process data that are too large to fit
in the external memory storage. One example of this is lifelong learning
scenario where the model receives unlimited length of data stream as an input
which contains vast majority of uninformative entries. We tackle this problem
by proposing a memory network fit for long-term lifelong learning scenario,
which we refer to as Long-term Episodic Memory Networks (LEMN), that features a
RNN-based retention agent that learns to replace less important memory entries
based on the retention probability generated on each entry that is learned to
identify data instances of generic importance relative to other memory entries,
as well as its historical importance. Such learning of retention agent allows
our long-term episodic memory network to retain memory entries of generic
importance for a given task. We validate our model on a path-finding task as
well as synthetic and real question answering tasks, on which our model
achieves significant improvements over the memory augmented networks with
rule-based memory scheduling as well as an RL-based baseline that does not
consider relative or historical importance of the memory.
On the Dimensionality of Word Embedding
Authors: Zi Yin, Yuanyuan Shen
Date: 2018-12-11 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Open-Ended Question Answering, Word Embeddings

In this paper, we provide a theoretical understanding of word embedding and
its dimensionality. Motivated by the unitary-invariance of word embedding, we
propose the Pairwise Inner Product (PIP) loss, a novel metric on the
dissimilarity between word embeddings. Using techniques from matrix
perturbation theory, we reveal a fundamental bias-variance trade-off in
dimensionality selection for word embeddings. This bias-variance trade-off
sheds light on many empirical observations which were previously unexplained,
for example the existence of an optimal dimensionality. Moreover, new insights
and discoveries, like when and how word embeddings are robust to over-fitting,
are revealed. By optimizing over the bias-variance trade-off of the PIP loss,
we can explicitly answer the open question of dimensionality selection for word
embedding.
Learning Controllable Fair Representations
Authors: Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, Stefano Ermon
Date: 2018-12-11 00:00:00
Tasks: Fairness

Learning data representations that are transferable and are fair with respect to certain protected attributes is crucial to reducing unfair decisions while preserving the utility of the data. We propose an information-theoretically motivated objective for learning maximally expressive representations subject to fairness constraints. We demonstrate that a range of existing approaches optimize approximations to the Lagrangian dual of our objective. In contrast to these existing approaches, our objective allows the user to control the fairness of the representations by specifying limits on unfairness. Exploiting duality, we introduce a method that optimizes the model parameters as well as the expressiveness-fairness trade-off. Empirical evidence suggests that our proposed method can balance the trade-off between multiple notions of fairness and achieves higher expressiveness at a lower computational cost.
Automatic Feature Weight Determination using Indexing and Pseudo-Relevance Feedback for Multi-feature Content-Based Image Retrieval
Authors: Asheet Kumar, Shivam Choudhary, Vaibhav Singh Khokhar, Vikas Meena, Chiranjoy Chattopadhyay
Date: 2018-12-11 00:00:00
Tasks: Attribute, Content-Based Image Retrieval, Image Retrieval, Information Retrieval, Retrieval

Content-based image retrieval (CBIR) is one of the most active research areas
in multimedia information retrieval. Given a query image, the task is to search
relevant images in a repository. Low level features like color, texture, and
shape feature vectors of an image are always considered to be an important
attribute in CBIR system. Thus the performance of the CBIR system can be
enhanced by combining these feature vectors. In this paper, we propose a novel
CBIR framework by applying to index using multiclass SVM and finding the
appropriate weights of the individual features automatically using the
relevance ratio and mean difference. We have taken four feature descriptors to
represent color, texture and shape features. During retrieval, feature vectors
of query image are combined, weighted and compared with feature vectors of
images in the database to rank order the results. Experiments were performed on
four benchmark datasets and performance is compared with existing techniques to
validate the superiority of our proposed framework.
New Approaches to Inverse Structural Modification Theory using Random Projections
Authors: Prasad Cheema, Mehrisadat M. Alamdari, Gareth A. Vio
Date: 2018-12-11 00:00:00

In many contexts the modal properties of a structure change, either due to
the impact of a changing environment, fatigue, or due to the presence of
structural damage. For example during flight, an aircraft's modal properties
are known to change with both altitude and velocity. It is thus important to
quantify these changes given only a truncated set of modal data, which is
usually the case experimentally. This procedure is formally known as the
generalised inverse eigenvalue problem. In this paper we experimentally show
that first-order gradient-based methods that optimise objective functions
defined over a modal are prohibitive due to the required small step sizes. This
in turn leads to the justification of using a non-gradient, black box optimiser
in the form of particle swarm optimisation. We further show how it is possible
to solve such inverse eigenvalue problems in a lower dimensional space by the
use of random projections, which in many cases reduces the total dimensionality
of the optimisation problem by 80% to 99%. Two example problems are explored
involving a ten-dimensional mass-stiffness toy problem, and a one-dimensional
finite element mass-stiffness approximation for a Boeing 737-300 aircraft.
A Main/Subsidiary Network Framework for Simplifying Binary Neural Network
Authors: Yinghao Xu, Xin Dong, Yudian Li, Hao Su
Date: 2018-12-11 00:00:00
Tasks: Binarization, image-classification, Image Classification, Network Pruning

To reduce memory footprint and run-time latency, techniques such as neural
network pruning and binarization have been explored separately. However, it is
unclear how to combine the best of the two worlds to get extremely small and
efficient models. In this paper, we, for the first time, define the
filter-level pruning problem for binary neural networks, which cannot be solved
by simply migrating existing structural pruning methods for full-precision
models. A novel learning-based approach is proposed to prune filters in our
main/subsidiary network framework, where the main network is responsible for
learning representative features to optimize the prediction performance, and
the subsidiary component works as a filter selector on the main network. To
avoid gradient mismatch when training the subsidiary component, we propose a
layer-wise and bottom-up scheme. We also provide the theoretical and
experimental comparison between our learning-based and greedy rule-based
methods. Finally, we empirically demonstrate the effectiveness of our approach
applied on several binary models, including binarized NIN, VGG-11, and
ResNet-18, on various image classification datasets.
Identity-Enhanced Network for Facial Expression Recognition
Authors: Yanwei Li, Xingang Wang, Shilei Zhang, Lingxi Xie, Wenqi Wu, Hongyuan Yu, Zheng Zhu
Date: 2018-12-11 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Multi-Task Learning

Facial expression recognition is a challenging task, arguably because of
large intra-class variations and high inter-class similarities. The core
drawback of the existing approaches is the lack of ability to discriminate the
changes in appearance caused by emotions and identities. In this paper, we
present a novel identity-enhanced network (IDEnNet) to eliminate the negative
impact of identity factor and focus on recognizing facial expressions. Spatial
fusion combined with self-constrained multi-task learning are adopted to
jointly learn the expression representations and identity-related information.
We evaluate our approach on three popular datasets, namely Oulu-CASIA, CK+ and
MMI. IDEnNet improves the baseline consistently, and achieves the best or
comparable state-of-the-art on all three datasets.
2.5D Visual Sound
Authors: Ruohan Gao, Kristen Grauman
Date: 2018-12-11 00:00:00
Proceeding: CVPR 2019 6

Binaural audio provides a listener with 3D sound sensation, allowing a rich
perceptual experience of the scene. However, binaural recordings are scarcely
available and require nontrivial expertise and equipment to obtain. We propose
to convert common monaural audio into binaural audio by leveraging video. The
key idea is that visual frames reveal significant spatial cues that, while
explicitly lacking in the accompanying single-channel audio, are strongly
linked to it. Our multi-modal approach recovers this link from unlabeled video.
We devise a deep convolutional neural network that learns to decode the
monaural (single-channel) soundtrack into its binaural counterpart by injecting
visual information about object and scene configurations. We call the resulting
output 2.5D visual sound---the visual stream helps "lift" the flat single
channel audio into spatialized sound. In addition to sound generation, we show
the self-supervised representation learned by our network benefits audio-visual
source separation. Our video results:
http://vision.cs.utexas.edu/projects/2.5D_visual_sound/
Deep Learning on Graphs: A Survey
Authors: Ziwei Zhang, Peng Cui, Wenwu Zhu
Date: 2018-12-11 00:00:00
Tasks: Deep Learning, Reinforcement Learning, Survey

Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques. In this survey, we comprehensively review the different types of deep learning methods on graphs. We divide the existing methods into five categories based on their model architectures and training strategies: graph recurrent neural networks, graph convolutional networks, graph autoencoders, graph reinforcement learning, and graph adversarial methods. We then provide a comprehensive overview of these methods in a systematic manner mainly by following their development history. We also analyze the differences and compositions of different methods. Finally, we briefly outline the applications in which they have been used and discuss potential future research directions.
Predicting the Effects of News Sentiments on the Stock Market
Authors: Dev Shah, Haruna Isah, Farhana Zulkernine
Date: 2018-12-11 00:00:00
Tasks: Sentiment Analysis, Stock Market Prediction, Stock Price Prediction

Stock market forecasting is very important in the planning of business
activities. Stock price prediction has attracted many researchers in multiple
disciplines including computer science, statistics, economics, finance, and
operations research. Recent studies have shown that the vast amount of online
information in the public domain such as Wikipedia usage pattern, news stories
from the mainstream media, and social media discussions can have an observable
effect on investors opinions towards financial markets. The reliability of the
computational models on stock market prediction is important as it is very
sensitive to the economy and can directly lead to financial loss. In this
paper, we retrieved, extracted, and analyzed the effects of news sentiments on
the stock market. Our main contributions include the development of a sentiment
analysis dictionary for the financial sector, the development of a
dictionary-based sentiment analysis model, and the evaluation of the model for
gauging the effects of news sentiments on stocks for the pharmaceutical market.
Using only news sentiments, we achieved a directional accuracy of 70.59% in
predicting the trends in short-term stock price movement.
Loss Guided Activation for Action Recognition in Still Images
Authors: Lu Liu, Robby T. Tan, ShaoDi You
Date: 2018-12-11 00:00:00
Tasks: Action Recognition, Action Recognition In Still Images, Temporal Action Localization

One significant problem of deep-learning based human action recognition is
that it can be easily misled by the presence of irrelevant objects or
backgrounds. Existing methods commonly address this problem by employing
bounding boxes on the target humans as part of the input, in both training and
testing stages. This requirement of bounding boxes as part of the input is
needed to enable the methods to ignore irrelevant contexts and extract only
human features. However, we consider this solution is inefficient, since the
bounding boxes might not be available. Hence, instead of using a person
bounding box as an input, we introduce a human-mask loss to automatically guide
the activations of the feature maps to the target human who is performing the
action, and hence suppress the activations of misleading contexts. We propose a
multi-task deep learning method that jointly predicts the human action class
and human location heatmap. Extensive experiments demonstrate our approach is
more robust compared to the baseline methods under the presence of irrelevant
misleading contexts. Our method achieves 94.06\% and 40.65\% (in terms of mAP)
on Stanford40 and MPII dataset respectively, which are 3.14\% and 12.6\%
relative improvements over the best results reported in the literature, and
thus set new state-of-the-art results. Additionally, unlike some existing
methods, we eliminate the requirement of using a person bounding box as an
input during testing.
A Functional Taxonomy of Music Generation Systems
Authors: Dorien Herremans, Ching-Hua Chuan, Elaine Chew
Date: 2018-12-11 00:00:00
Tasks: Music Generation

Digital advances have transformed the face of automatic music generation
since its beginnings at the dawn of computing. Despite the many breakthroughs,
issues such as the musical tasks targeted by different machines and the degree
to which they succeed remain open questions. We present a functional taxonomy
for music generation systems with reference to existing systems. The taxonomy
organizes systems according to the purposes for which they were designed. It
also reveals the inter-relatedness amongst the systems. This design-centered
approach contrasts with predominant methods-based surveys and facilitates the
identification of grand challenges to set the stage for new breakthroughs.
KF-LAX: Kronecker-factored curvature estimation for control variate optimization in reinforcement learning
Authors: Mohammad Firouzi
Date: 2018-12-11 00:00:00
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A key challenge for gradient based optimization methods in model-free
reinforcement learning is to develop an approach that is sample efficient and
has low variance. In this work, we apply Kronecker-factored curvature
estimation technique (KFAC) to a recently proposed gradient estimator for
control variate optimization, RELAX, to increase the sample efficiency of using
this gradient estimation method in reinforcement learning. The performance of
the proposed method is demonstrated on a synthetic problem and a set of three
discrete control task Atari games.