pwc-archive/papers-with-abstracts huggingface dataset:
Optimal Approach for Image Recognition using Deep Convolutional Architecture
Authors: Parth Shah, Vishvajit Bakrola, Supriya Pati
Date: 2019-04-25 00:00:00
Tasks: Deep Learning

In the recent time deep learning has achieved huge popularity due to its
performance in various machine learning algorithms. Deep learning as
hierarchical or structured learning attempts to model high level abstractions
in data by using a group of processing layers. The foundation of deep learning
architectures is inspired by the understanding of information processing and
neural responses in human brain. The architectures are created by stacking
multiple linear or non-linear operations. The article mainly focuses on the
state-of-art deep learning models and various real world applications specific
training methods. Selecting optimal architecture for specific problem is a
challenging task, at a closing stage of the article we proposed optimal
approach to deep convolutional architecture for the application of image
recognition.
Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications
Authors: Soo Ye Kim, Jihyong Oh, Munchurl Kim
Date: 2019-04-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: 4k, 8k, inverse tone mapping, Inverse-Tone-Mapping, Super-Resolution, Tone Mapping

Recent modern displays are now able to render high dynamic range (HDR), high resolution (HR) videos of up to 8K UHD (Ultra High Definition). Consequently, UHD HDR broadcasting and streaming have emerged as high quality premium services. However, due to the lack of original UHD HDR video content, appropriate conversion technologies are urgently needed to transform the legacy low resolution (LR) standard dynamic range (SDR) videos into UHD HDR versions. In this paper, we propose a joint super-resolution (SR) and inverse tone-mapping (ITM) framework, called Deep SR-ITM, which learns the direct mapping from LR SDR video to their HR HDR version. Joint SR and ITM is an intricate task, where high frequency details must be restored for SR, jointly with the local contrast, for ITM. Our network is able to restore fine details by decomposing the input image and focusing on the separate base (low frequency) and detail (high frequency) layers. Moreover, the proposed modulation blocks apply location-variant operations to enhance local contrast. The Deep SR-ITM shows good subjective quality with increased contrast and details, outperforming the previous joint SR-ITM method.
Indoor dense depth map at drone hovering
Authors: Arindam Saha, Soumyadip Maity, Brojeshwar Bhowmick
Date: 2019-04-25 00:00:00

Autonomous Micro Aerial Vehicles (MAVs) gained tremendous attention in recent
years. Autonomous flight in indoor requires a dense depth map for navigable
space detection which is the fundamental component for autonomous navigation.
In this paper, we address the problem of reconstructing dense depth while a
drone is hovering (small camera motion) in indoor scenes using already
estimated cameras and sparse point cloud obtained from a vSLAM. We start by
segmenting the scene based on sudden depth variation using sparse 3D points and
introduce a patch-based local plane fitting via energy minimization which
combines photometric consistency and co-planarity with neighbouring patches.
The method also combines a plane sweep technique for image segments having
almost no sparse point for initialization. Experiments show, the proposed
method produces better depth for indoor in artificial lighting condition,
low-textured environment compared to earlier literature in small motion.
Fusion-supervised Deep Cross-modal Hashing
Authors: Li Wang, Lei Zhu, En Yu, Jiande Sun, Huaxiang Zhang
Date: 2019-04-25 00:00:00
Tasks: Cross-Modal Retrieval, Deep Hashing, Retrieval

Deep hashing has recently received attention in cross-modal retrieval for its impressive advantages. However, existing hashing methods for cross-modal retrieval cannot fully capture the heterogeneous multi-modal correlation and exploit the semantic information. In this paper, we propose a novel \emph{Fusion-supervised Deep Cross-modal Hashing} (FDCH) approach. Firstly, FDCH learns unified binary codes through a fusion hash network with paired samples as input, which effectively enhances the modeling of the correlation of heterogeneous multi-modal data. Then, these high-quality unified hash codes further supervise the training of the modality-specific hash networks for encoding out-of-sample queries. Meanwhile, both pair-wise similarity information and classification information are embedded in the hash networks under one stream framework, which simultaneously preserves cross-modal similarity and keeps semantic consistency. Experimental results on two benchmark datasets demonstrate the state-of-the-art performance of FDCH.
A Conditional Adversarial Network for Scene Flow Estimation
Authors: Ravi Kumar Thakur, Snehasis Mukherjee
Date: 2019-04-25 00:00:00
Tasks: Optical Flow Estimation, Scene Flow Estimation

The problem of Scene flow estimation in depth videos has been attracting
attention of researchers of robot vision, due to its potential application in
various areas of robotics. The conventional scene flow methods are difficult to
use in reallife applications due to their long computational overhead. We
propose a conditional adversarial network SceneFlowGAN for scene flow
estimation. The proposed SceneFlowGAN uses loss function at two ends: both
generator and descriptor ends. The proposed network is the first attempt to
estimate scene flow using generative adversarial networks, and is able to
estimate both the optical flow and disparity from the input stereo images
simultaneously. The proposed method is experimented on a large RGB-D benchmark
sceneflow dataset.
Out of the Box: A combined approach for handling occlusion in Human Pose Estimation
Authors: Rohit Jena
Date: 2019-04-25 00:00:00
Tasks: 3D Pose Estimation, Disentanglement, Pose Estimation

Human Pose estimation is a challenging problem, especially in the case of 3D
pose estimation from 2D images due to many different factors like occlusion,
depth ambiguities, intertwining of people, and in general crowds. 2D
multi-person human pose estimation in the wild also suffers from the same
problems - occlusion, ambiguities, and disentanglement of people's body parts.
Being a fundamental problem with loads of applications, including but not
limited to surveillance, economical motion capture for video games and movies,
and physiotherapy, this is an interesting problem to be solved both from a
practical perspective and from an intellectual perspective as well. Although
there are cases where no pose estimation can ever predict with 100% accuracy
(cases where even humans would fail), there are several algorithms that have
brought new state-of-the-art performance in human pose estimation in the wild.
We look at a few algorithms with different approaches and also formulate our
own approach to tackle a consistently bugging problem, i.e. occlusions.
Deep Multi-View Learning using Neuron-Wise Correlation-Maximizing Regularizers
Authors: Kui Jia, Jiehong Lin, Mingkui Tan, DaCheng Tao
Date: 2019-04-25 00:00:00
Tasks: 3D Object Recognition, General Classification, image-classification, Image Classification, MULTI-VIEW LEARNING, Object Recognition, Scene Recognition

Many machine learning problems concern with discovering or associating common
patterns in data of multiple views or modalities. Multi-view learning is of the
methods to achieve such goals. Recent methods propose deep multi-view networks
via adaptation of generic Deep Neural Networks (DNNs), which concatenate
features of individual views at intermediate network layers (i.e., fusion
layers). In this work, we study the problem of multi-view learning in such
end-to-end networks. We take a regularization approach via multi-view learning
criteria, and propose a novel, effective, and efficient neuron-wise
correlation-maximizing regularizer. We implement our proposed regularizers
collectively as a correlation-regularized network layer (CorrReg). CorrReg can
be applied to either fully-connected or convolutional fusion layers, simply by
replacing them with their CorrReg counterparts. By partitioning neurons of a
hidden layer in generic DNNs into multiple subsets, we also consider a
multi-view feature learning perspective of generic DNNs. Such a perspective
enables us to study deep multi-view learning in the context of regularized
network training, for which we present control experiments of benchmark image
classification to show the efficacy of our proposed CorrReg. To investigate how
CorrReg is useful for practical multi-view learning problems, we conduct
experiments of RGB-D object/scene recognition and multi-view based 3D object
recognition, using networks with fusion layers that concatenate intermediate
features of individual modalities or views for subsequent classification.
Applying CorrReg to fusion layers of these networks consistently improves
classification performance. In particular, we achieve the new state of the art
on the benchmark RGB-D object and RGB-D scene datasets. We make the
implementation of CorrReg publicly available.
A Deeper Look at Facial Expression Dataset Bias
Authors: Shan Li, Weihong Deng
Date: 2019-04-25 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER)

Datasets play an important role in the progress of facial expression
recognition algorithms, but they may suffer from obvious biases caused by
different cultures and collection conditions. To look deeper into this bias, we
first conduct comprehensive experiments on dataset recognition and crossdataset
generalization tasks, and for the first time explore the intrinsic causes of
the dataset discrepancy. The results quantitatively verify that current
datasets have a strong buildin bias and corresponding analyses indicate that
the conditional probability distributions between source and target datasets
are different. However, previous researches are mainly based on shallow
features with limited discriminative ability under the assumption that the
conditional distribution remains unchanged across domains. To address these
issues, we further propose a novel deep Emotion-Conditional Adaption Network
(ECAN) to learn domain-invariant and discriminative feature representations,
which can match both the marginal and the conditional distributions across
domains simultaneously. In addition, the largely ignored expression class
distribution bias is also addressed by a learnable re-weighting parameter, so
that the training and testing domains can share similar class distribution.
Extensive cross-database experiments on both lab-controlled datasets (CK+,
JAFFE, MMI and Oulu-CASIA) and real-world databases (AffectNet, FER2013, RAF-DB
2.0 and SFEW 2.0) demonstrate that our ECAN can yield competitive performances
across various facial expression transfer tasks and outperform the
state-of-theart methods.
Divide and Conquer: A Deep CASA Approach to Talker-independent Monaural Speaker Separation
Authors: Yuzhou Liu, DeLiang Wang
Date: 2019-04-25 00:00:00
Tasks: Clustering, Speaker Separation, Speech Separation

We address talker-independent monaural speaker separation from the
perspectives of deep learning and computational auditory scene analysis (CASA).
Specifically, we decompose the multi-speaker separation task into the stages of
simultaneous grouping and sequential grouping. Simultaneous grouping is first
performed in each time frame by separating the spectra of different speakers
with a permutation-invariantly trained neural network. In the second stage, the
frame-level separated spectra are sequentially grouped to different speakers by
a clustering network. The proposed deep CASA approach optimizes frame-level
separation and speaker tracking in turn, and produces excellent results for
both objectives. Experimental results on the benchmark WSJ0-2mix database show
that the new approach achieves the state-of-the-art results with a modest model
size.
Forecasting in Big Data Environments: an Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)
Authors: Ali Habibnia, Esfandiar Maasoumi
Date: 2019-04-25 00:00:00

This paper considers improved forecasting in possibly nonlinear dynamic
settings, with high-dimension predictors ("big data" environments). To overcome
the curse of dimensionality and manage data and model complexity, we examine
shrinkage estimation of a back-propagation algorithm of a deep neural net with
skip-layer connections. We expressly include both linear and nonlinear
components. This is a high-dimensional learning approach including both
sparsity L1 and smoothness L2 penalties, allowing high-dimensionality and
nonlinearity to be accommodated in one step. This approach selects significant
predictors as well as the topology of the neural network. We estimate optimal
values of shrinkage hyperparameters by incorporating a gradient-based
optimization technique resulting in robust predictions with improved
reproducibility. The latter has been an issue in some approaches. This is
statistically interpretable and unravels some network structure, commonly left
to a black box. An additional advantage is that the nonlinear part tends to get
pruned if the underlying process is linear. In an application to forecasting
equity returns, the proposed approach captures nonlinear dynamics between
equities to enhance forecast performance. It offers an appreciable improvement
over current univariate and multivariate models by RMSE and actual portfolio
performance.
HAR-Net: Joint Learning of Hybrid Attention for Single-stage Object Detection
Authors: Ya-Li Li, Shengjin Wang
Date: 2019-04-25 00:00:00
Tasks: Object, object-detection, Object Detection

Object detection has been a challenging task in computer vision. Although
significant progress has been made in object detection with deep neural
networks, the attention mechanism is far from development. In this paper, we
propose the hybrid attention mechanism for single-stage object detection.
First, we present the modules of spatial attention, channel attention and
aligned attention for single-stage object detection. In particular, stacked
dilated convolution layers with symmetrically fixed rates are constructed to
learn spatial attention. The channel attention is proposed with the cross-level
group normalization and squeeze-and-excitation module. Aligned attention is
constructed with organized deformable filters. Second, the three kinds of
attention are unified to construct the hybrid attention mechanism. We then
embed the hybrid attention into Retina-Net and propose the efficient
single-stage HAR-Net for object detection. The attention modules and the
proposed HAR-Net are evaluated on the COCO detection dataset. Experiments
demonstrate that hybrid attention can significantly improve the detection
accuracy and the HAR-Net can achieve the state-of-the-art 45.8\% mAP,
outperform existing single-stage object detectors.
Learning Discriminative Features Via Weights-biased Softmax Loss
Authors: XiaoBin Li, Weiqiang Wang
Date: 2019-04-25 00:00:00
Tasks: image-classification, Image Classification

Loss functions play a key role in training superior deep neural networks. In
convolutional neural networks (CNNs), the popular cross entropy loss together
with softmax does not explicitly guarantee minimization of intra-class variance
or maximization of inter-class variance. In the early studies, there is no
theoretical analysis and experiments explicitly indicating how to choose the
number of units in fully connected layer. To help CNNs learn features more fast
and discriminative, there are two contributions in this paper. First, we
determine the minimum number of units in FC layer by rigorous theoretical
analysis and extensive experiment, which reduces CNNs' parameter memory and
training time. Second, we propose a negative-focused weights-biased softmax
(W-Softmax) loss to help CNNs learn more discriminative features. The proposed
W-Softmax loss not only theoretically formulates the intraclass compactness and
inter-class separability, but also can avoid overfitting by enlarging decision
margins. Moreover, the size of decision margins can be flexibly controlled by
adjusting a hyperparameter $\alpha$. Extensive experimental results on several
benchmark datasets show the superiority of W-Softmax in image classification
tasks.
Summarizing Data Succinctly with the Most Informative Itemsets
Authors: Michael Mampaey, Jilles Vreeken, Nikolaj Tatti
Date: 2019-04-25 00:00:00
Tasks: Descriptive

Knowledge discovery from data is an inherently iterative process. That is,
what we know about the data greatly determines our expectations, and therefore,
what results we would find interesting and/or surprising. Given new knowledge
about the data, our expectations will change. Hence, in order to avoid
redundant results, knowledge discovery algorithms ideally should follow such an
iterative updating procedure.
  With this in mind, we introduce a well-founded approach for succinctly
summarizing data with the most informative itemsets; using a probabilistic
maximum entropy model, we iteratively find the itemset that provides us the
most novel information--that is, for which the frequency in the data surprises
us the most---and in turn we update our model accordingly. As we use the
Maximum Entropy principle to obtain unbiased probabilistic models, and only
include those itemsets that are most informative with regard to the current
model, the summaries we construct are guaranteed to be both descriptive and
non-redundant.
  The algorithm that we present, called MTV, can either discover the top-$k$
most informative itemsets, or we can employ either the Bayesian Information
Criterion (BIC) or the Minimum Description Length (MDL) principle to
automatically identify the set of itemsets that together summarize the data
well. In other words, our method will `tell you what you need to know' about
the data. Importantly, it is a one-phase algorithm: rather than picking
itemsets from a user-provided candidate set, itemsets and their supports are
mined on-the-fly. To further its applicability, we provide an efficient method
to compute the maximum entropy distribution using Quick Inclusion-Exclusion.
  Experiments on our method, using synthetic, benchmark, and real data, show
that the discovered summaries are succinct, and correctly identify the key
patterns in the data.
TreeGrad: Transferring Tree Ensembles to Neural Networks
Authors: Chapman Siu
Date: 2019-04-25 00:00:00
Tasks: BIG-bench Machine Learning, Neural Architecture Search

Gradient Boosting Decision Tree (GBDT) are popular machine learning algorithms with implementations such as LightGBM and in popular machine learning toolkits like Scikit-Learn. Many implementations can only produce trees in an offline manner and in a greedy manner. We explore ways to convert existing GBDT implementations to known neural network architectures with minimal performance loss in order to allow decision splits to be updated in an online manner and provide extensions to allow splits points to be altered as a neural architecture search problem. We provide learning bounds for our neural network.
Lipschitz Bandit Optimization with Improved Efficiency
Authors: Xu Zhu, David B. Dunson
Date: 2019-04-25 00:00:00

We consider the Lipschitz bandit optimization problem with an emphasis on practical efficiency. Although there is rich literature on regret analysis of this type of problem, e.g., [Kleinberg et al. 2008, Bubeck et al. 2011, Slivkins 2014], their proposed algorithms suffer from serious practical problems including extreme time complexity and dependence on oracle implementations. With this motivation, we propose a novel algorithm with an Upper Confidence Bound (UCB) exploration, namely Tree UCB-Hoeffding, using adaptive partitions. Our partitioning scheme is easy to implement and does not require any oracle settings. With a tree-based search strategy, the total computational cost can be improved to $\mathcal{O}(T\log T)$ for the first $T$ iterations. In addition, our algorithm achieves the regret lower bound up to a logarithmic factor.
CBHE: Corner-based Building Height Estimation for Complex Street Scene Images
Authors: Yunxiang Zhao, Jianzhong Qi, Rui Zhang
Date: 2019-04-25 00:00:00
Tasks: valid

Building height estimation is important in many applications such as 3D city reconstruction, urban planning, and navigation. Recently, a new building height estimation method using street scene images and 2D maps was proposed. This method is more scalable than traditional methods that use high-resolution optical data, LiDAR data, or RADAR data which are expensive to obtain. The method needs to detect building rooflines and then compute building height via the pinhole camera model. We observe that this method has limitations in handling complex street scene images in which buildings overlap with each other and the rooflines are difficult to locate. We propose CBHE, a building height estimation algorithm considering both building corners and rooflines. CBHE first obtains building corner and roofline candidates in street scene images based on building footprints from 2D maps and the camera parameters. Then, we use a deep neural network named BuildingNet to classify and filter corner and roofline candidates. Based on the valid corners and rooflines from BuildingNet, CBHE computes building height via the pinhole camera model. Experimental results show that the proposed BuildingNet yields a higher accuracy on building corner and roofline candidate filtering compared with the state-of-the-art open set classifiers. Meanwhile, CBHE outperforms the baseline algorithm by over 10% in building height estimation accuracy.
Declarative Recursive Computation on an RDBMS, or, Why You Should Use a Database For Distributed Machine Learning
Authors: Dimitrije Jankov, Shangyu Luo, Binhang Yuan, Zhuhua Cai, Jia Zou, Chris Jermaine, Zekai J. Gao
Date: 2019-04-25 00:00:00
Tasks: BIG-bench Machine Learning, Management

A number of popular systems, most notably Google's TensorFlow, have been
implemented from the ground up to support machine learning tasks. We consider
how to make a very small set of changes to a modern relational database
management system (RDBMS) to make it suitable for distributed learning
computations. Changes include adding better support for recursion, and
optimization and execution of very large compute plans. We also show that there
are key advantages to using an RDBMS as a machine learning platform. In
particular, learning based on a database management system allows for trivial
scaling to large data sets and especially large models, where different
computational units operate on different parts of a model that may be too large
to fit into RAM.