pwc-archive/papers-with-abstracts huggingface dataset:
Dual-Glance Model for Deciphering Social Relationships
Authors: Junnan Li, Yongkang Wong, Qi Zhao, Mohan S. Kankanhalli
Date: 2017-08-02 00:00:00
Proceeding: ICCV 2017 10
Tasks: model, object-detection, Object Detection, Scene Parsing, Scene Understanding, Visual Social Relationship Recognition

Since the beginning of early civilizations, social relationships derived from
each individual fundamentally form the basis of social structure in our daily
life. In the computer vision literature, much progress has been made in scene
understanding, such as object detection and scene parsing. Recent research
focuses on the relationship between objects based on its functionality and
geometrical relations. In this work, we aim to study the problem of social
relationship recognition, in still images. We have proposed a dual-glance model
for social relationship recognition, where the first glance fixates at the
individual pair of interest and the second glance deploys attention mechanism
to explore contextual cues. We have also collected a new large scale People in
Social Context (PISC) dataset, which comprises of 22,670 images and 76,568
annotated samples from 9 types of social relationship. We provide benchmark
results on the PISC dataset, and qualitatively demonstrate the efficacy of the
proposed model.
Neural Aggregation Network for Video Face Recognition
Authors: Jiaolong Yang, Peiran Ren, Dong-Qing Zhang, Dong Chen, Fang Wen, Hongdong Li, Gang Hua
Date: 2016-03-17 00:00:00
Proceeding: CVPR 2017 7
Tasks: Face Identification, Face Recognition, Face Verification

This paper presents a Neural Aggregation Network (NAN) for video face
recognition. The network takes a face video or face image set of a person with
a variable number of face images as its input, and produces a compact,
fixed-dimension feature representation for recognition. The whole network is
composed of two modules. The feature embedding module is a deep Convolutional
Neural Network (CNN) which maps each face image to a feature vector. The
aggregation module consists of two attention blocks which adaptively aggregate
the feature vectors to form a single feature inside the convex hull spanned by
them. Due to the attention mechanism, the aggregation is invariant to the image
order. Our NAN is trained with a standard classification or verification loss
without any extra supervision signal, and we found that it automatically learns
to advocate high-quality face images while repelling low-quality ones such as
blurred, occluded and improperly exposed faces. The experiments on IJB-A,
YouTube Face, Celebrity-1000 video face recognition benchmarks show that it
consistently outperforms naive aggregation methods and achieves the
state-of-the-art accuracy.
On the Importance of Consistency in Training Deep Neural Networks
Authors: Chengxi Ye, Yezhou Yang, Cornelia Fermuller, Yiannis Aloimonos
Date: 2017-08-02 00:00:00

We explain that the difficulties of training deep neural networks come from a
syndrome of three consistency issues. This paper describes our efforts in their
analysis and treatment. The first issue is the training speed inconsistency in
different layers. We propose to address it with an intuitive,
simple-to-implement, low footprint second-order method. The second issue is the
scale inconsistency between the layer inputs and the layer residuals. We
explain how second-order information provides favorable convenience in removing
this roadblock. The third and most challenging issue is the inconsistency in
residual propagation. Based on the fundamental theorem of linear algebra, we
provide a mathematical characterization of the famous vanishing gradient
problem. Thus, an important design principle for future optimization and neural
network design is derived. We conclude this paper with the construction of a
novel contractive neural network.
Deep Recurrent Generative Decoder for Abstractive Text Summarization
Authors: Piji Li, Wai Lam, Lidong Bing, ZiHao Wang
Date: 2017-08-02 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Abstractive Text Summarization, Decoder, Text Summarization, Variational Inference

We propose a new framework for abstractive text summarization based on a
sequence-to-sequence oriented encoder-decoder model equipped with a deep
recurrent generative decoder (DRGN).
  Latent structure information implied in the target summaries is learned based
on a recurrent latent random model for improving the summarization quality.
  Neural variational inference is employed to address the intractable posterior
inference for the recurrent latent variables.
  Abstractive summaries are generated based on both the generative latent
variables and the discriminative deterministic states.
  Extensive experiments on some benchmark datasets in different languages show
that DRGN achieves improvements over the state-of-the-art methods.
Monocular Depth Estimation with Hierarchical Fusion of Dilated CNNs and Soft-Weighted-Sum Inference
Authors: Bo Li, Yuchao Dai, Mingyi He
Date: 2017-08-02 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation, Quantization, Semantic Segmentation

Monocular depth estimation is a challenging task in complex compositions
depicting multiple objects of diverse scales. Albeit the recent great progress
thanks to the deep convolutional neural networks (CNNs), the state-of-the-art
monocular depth estimation methods still fall short to handle such real-world
challenging scenarios. In this paper, we propose a deep end-to-end learning
framework to tackle these challenges, which learns the direct mapping from a
color image to the corresponding depth map. First, we represent monocular depth
estimation as a multi-category dense labeling task by contrast to the
regression based formulation. In this way, we could build upon the recent
progress in dense labeling such as semantic segmentation. Second, we fuse
different side-outputs from our front-end dilated convolutional neural network
in a hierarchical way to exploit the multi-scale depth cues for depth
estimation, which is critical to achieve scale-aware depth estimation. Third,
we propose to utilize soft-weighted-sum inference instead of the hard-max
inference, transforming the discretized depth score to continuous depth value.
Thus, we reduce the influence of quantization error and improve the robustness
of our method. Extensive experiments on the NYU Depth V2 and KITTI datasets
show the superiority of our method compared with current state-of-the-art
methods. Furthermore, experiments on the NYU V2 dataset reveal that our model
is able to learn the probability distribution of depth.
Towards the Success Rate of One: Real-time Unconstrained Salient Object Detection
Authors: Mahyar Najibi, Fan Yang, Qiaosong Wang, Robinson Piramuthu
Date: 2017-07-31 00:00:00
Tasks: GPU, Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

In this work, we propose an efficient and effective approach for
unconstrained salient object detection in images using deep convolutional
neural networks. Instead of generating thousands of candidate bounding boxes
and refining them, our network directly learns to generate the saliency map
containing the exact number of salient objects. During training, we convert the
ground-truth rectangular boxes to Gaussian distributions that better capture
the ROI regarding individual salient objects. During inference, the network
predicts Gaussian distributions centered at salient objects with an appropriate
covariance, from which bounding boxes are easily inferred. Notably, our network
performs saliency map prediction without pixel-level annotations, salient
object detection without object proposals, and salient object subitizing
simultaneously, all in a single pass within a unified framework. Extensive
experiments show that our approach outperforms existing methods on various
datasets by a large margin, and achieves more than 100 fps with VGG16 network
on a single GPU during inference.
Tube Convolutional Neural Network (T-CNN) for Action Detection in Videos
Authors: Rui Hou, Chen Chen, Mubarak Shah
Date: 2017-03-30 00:00:00
Proceeding: ICCV 2017 10
Tasks: Action Detection, image-classification, Image Classification, object-detection, Object Detection, Video Action Detection

Deep learning has been demonstrated to achieve excellent results for image
classification and object detection. However, the impact of deep learning on
video analysis (e.g. action detection and recognition) has been limited due to
complexity of video data and lack of annotations. Previous convolutional neural
networks (CNN) based video action detection approaches usually consist of two
major steps: frame-level action proposal detection and association of proposals
across frames. Also, these methods employ two-stream CNN framework to handle
spatial and temporal feature separately. In this paper, we propose an
end-to-end deep network called Tube Convolutional Neural Network (T-CNN) for
action detection in videos. The proposed architecture is a unified network that
is able to recognize and localize action based on 3D convolution features. A
video is first divided into equal length clips and for each clip a set of tube
proposals are generated next based on 3D Convolutional Network (ConvNet)
features. Finally, the tube proposals of different clips are linked together
employing network flow and spatio-temporal action detection is performed using
these linked video proposals. Extensive experiments on several video datasets
demonstrate the superior performance of T-CNN for classifying and localizing
actions in both trimmed and untrimmed videos compared to state-of-the-arts.
Chinese Typography Transfer
Authors: Jie Chang, Yujun Gu
Date: 2017-07-16 00:00:00

In this paper, we propose a new network architecture for Chinese typography
transformation based on deep learning. The architecture consists of two
sub-networks: (1)a fully convolutional network(FCN) aiming at transferring
specified typography style to another in condition of preserving structure
information; (2)an adversarial network aiming at generating more realistic
strokes in some details. Unlike models proposed before 2012 relying on the
complex segmentation of Chinese components or strokes, our model treats every
Chinese character as an inseparable image, so pre-processing or
post-preprocessing are abandoned. Besides, our model adopts end-to-end training
without pre-trained used in other deep models. The experiments demonstrates
that our model can synthesize realistic-looking target typography from any
source typography both on printed style and handwriting style.
Exact Tensor Completion from Sparsely Corrupted Observations via Convex Optimization
Authors: Jonathan Q. Jiang, Michael K. Ng
Date: 2017-08-02 00:00:00

This paper conducts a rigorous analysis for provable estimation of
multidimensional arrays, in particular third-order tensors, from a random
subset of its corrupted entries. Our study rests heavily on a recently proposed
tensor algebraic framework in which we can obtain tensor singular value
decomposition (t-SVD) that is similar to the SVD for matrices, and define a new
notion of tensor rank referred to as the tubal rank. We prove that by simply
solving a convex program, which minimizes a weighted combination of tubal
nuclear norm, a convex surrogate for the tubal rank, and the $\ell_1$-norm, one
can recover an incoherent tensor exactly with overwhelming probability,
provided that its tubal rank is not too large and that the corruptions are
reasonably sparse. Interestingly, our result includes the recovery guarantees
for the problems of tensor completion (TC) and tensor principal component
analysis (TRPCA) under the same algebraic setup as special cases. An
alternating direction method of multipliers (ADMM) algorithm is presented to
solve this optimization problem. Numerical experiments verify our theory and
real-world applications demonstrate the effectiveness of our algorithm.
LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation
Authors: Jianwei Yang, Anitha Kannan, Dhruv Batra, Devi Parikh
Date: 2017-03-05 00:00:00
Tasks: Image Generation

We present LR-GAN: an adversarial image generation model which takes scene
structure and context into account. Unlike previous generative adversarial
networks (GANs), the proposed GAN learns to generate image background and
foregrounds separately and recursively, and stitch the foregrounds on the
background in a contextually relevant manner to produce a complete natural
image. For each foreground, the model learns to generate its appearance, shape
and pose. The whole model is unsupervised, and is trained in an end-to-end
manner with gradient descent methods. The experiments demonstrate that LR-GAN
can generate more natural images with objects that are more human recognizable
than DCGAN.
Compositional Human Pose Regression
Authors: Xiao Sun, Jiaxiang Shang, Shuang Liang, Yichen Wei
Date: 2017-04-01 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Pose Estimation, regression

Regression based methods are not performing as well as detection based
methods for human pose estimation. A central problem is that the structural
information in the pose is not well exploited in the previous regression
methods. In this work, we propose a structure-aware regression approach. It
adopts a reparameterized pose representation using bones instead of joints. It
exploits the joint connection structure to define a compositional loss function
that encodes the long range interactions in the pose. It is simple, effective,
and general for both 2D and 3D pose estimation in a unified setting.
Comprehensive evaluation validates the effectiveness of our approach. It
significantly advances the state-of-the-art on Human3.6M and is competitive
with state-of-the-art results on MPII.
Geometric Convolutional Neural Network for Analyzing Surface-Based Neuroimaging Data
Authors: Si-Baek Seong, Chongwon Pae, Hae-Jeong Park
Date: 2017-08-02 00:00:00
Tasks: General Classification

The conventional CNN, widely used for two-dimensional images, however, is not
directly applicable to non-regular geometric surface, such as a cortical
thickness. We propose Geometric CNN (gCNN) that deals with data representation
over a spherical surface and renders pattern recognition in a multi-shell mesh
structure. The classification accuracy for sex was significantly higher than
that of SVM and image based CNN. It only uses MRI thickness data to classify
gender but this method can expand to classify disease from other MRI or fMRI
data
A Simple Loss Function for Improving the Convergence and Accuracy of Visual Question Answering Models
Authors: Ilija Ilievski, Jiashi Feng
Date: 2017-08-02 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual question answering as recently proposed multimodal learning task has
enjoyed wide attention from the deep learning community. Lately, the focus was
on developing new representation fusion methods and attention mechanisms to
achieve superior performance. On the other hand, very little focus has been put
on the models' loss function, arguably one of the most important aspects of
training deep learning models. The prevailing practice is to use cross entropy
loss function that penalizes the probability given to all the answers in the
vocabulary except the single most common answer for the particular question.
However, the VQA evaluation function compares the predicted answer with all the
ground-truth answers for the given question and if there is a matching, a
partial point is given. This causes a discrepancy between the model's cross
entropy loss and the model's accuracy as calculated by the VQA evaluation
function. In this work, we propose a novel loss, termed as soft cross entropy,
that considers all ground-truth answers and thus reduces the loss-accuracy
discrepancy. The proposed loss leads to an improved training convergence of VQA
models and an increase in accuracy as much as 1.6%.
Joint Transmission Map Estimation and Dehazing using Deep Networks
Authors: He Zhang, Vishwanath Sindagi, Vishal M. Patel
Date: 2017-08-02 00:00:00
Tasks: Image Dehazing, Single Image Dehazing, Single Image Haze Removal

Single image haze removal is an extremely challenging problem due to its
inherent ill-posed nature. Several prior-based and learning-based methods have
been proposed in the literature to solve this problem and they have achieved
superior results. However, most of the existing methods assume constant
atmospheric light model and tend to follow a two-step procedure involving
prior-based methods for estimating transmission map followed by calculation of
dehazed image using the closed form solution. In this paper, we relax the
constant atmospheric light assumption and propose a novel unified single image
dehazing network that jointly estimates the transmission map and performs
dehazing. In other words, our new approach provides an end-to-end learning
framework, where the inherent transmission map and dehazed result are learned
directly from the loss function. Extensive experiments on synthetic and real
datasets with challenging hazy images demonstrate that the proposed method
achieves significant improvements over the state-of-the-art methods.
A Novel Neural Network Model Specified for Representing Logical Relations
Authors: Gang Wang
Date: 2017-08-02 00:00:00

With computers to handle more and more complicated things in variable
environments, it becomes an urgent requirement that the artificial intelligence
has the ability of automatic judging and deciding according to numerous
specific conditions so as to deal with the complicated and variable cases. ANNs
inspired by brain is a good candidate. However, most of current numeric ANNs
are not good at representing logical relations because these models still try
to represent logical relations in the form of ratio based on functional
approximation. On the other hand, researchers have been trying to design novel
neural network models to make neural network model represent logical relations.
In this work, a novel neural network model specified for representing logical
relations is proposed and applied. New neurons and multiple kinds of links are
defined. Inhibitory links are introduced besides exciting links. Different from
current numeric ANNs, one end of an inhibitory link connects an exciting link
rather than a neuron. Inhibitory links inhibit the connected exciting links
conditionally to make this neural network model represent logical relations
correctly. This model can simulate the operations of Boolean logic gates, and
construct complex logical relations with the advantages of simpler neural
network structures than recent works in this area. This work provides some
ideas to make neural networks represent logical relations more directly and
efficiently, and the model could be used as the complement to current numeric
ANN to deal with logical issues and expand the application areas of ANN.
Kernalised Multi-resolution Convnet for Visual Tracking
Authors: Di Wu, Wenbin Zou, Xia Li, Yong Zhao
Date: 2017-08-02 00:00:00
Tasks: Object Tracking, Visual Object Tracking, Visual Tracking

Visual tracking is intrinsically a temporal problem. Discriminative
Correlation Filters (DCF) have demonstrated excellent performance for
high-speed generic visual object tracking. Built upon their seminal work, there
has been a plethora of recent improvements relying on convolutional neural
network (CNN) pretrained on ImageNet as a feature extractor for visual
tracking. However, most of their works relying on ad hoc analysis to design the
weights for different layers either using boosting or hedging techniques as an
ensemble tracker. In this paper, we go beyond the conventional DCF framework
and propose a Kernalised Multi-resolution Convnet (KMC) formulation that
utilises hierarchical response maps to directly output the target movement.
When directly deployed the learnt network to predict the unseen challenging UAV
tracking dataset without any weight adjustment, the proposed model consistently
achieves excellent tracking performance. Moreover, the transfered
multi-reslution CNN renders it possible to be integrated into the RNN temporal
learning framework, therefore opening the door on the end-to-end temporal deep
learning (TDL) for visual tracking.
An equation-of-state-meter of QCD transition from deep learning
Authors: Long-Gang Pang, Kai Zhou, Nan Su, Hannah Petersen, Horst Stöcker, Xin-Nian Wang
Date: 2016-12-13 00:00:00

Supervised learning with a deep convolutional neural network is used to
identify the QCD equation of state (EoS) employed in relativistic hydrodynamic
simulations of heavy-ion collisions from the simulated final-state particle
spectra $\rho(p_T,\Phi)$. High-level correlations of $\rho(p_T,\Phi)$ learned
by the neural network act as an effective "EoS-meter" in detecting the nature
of the QCD transition. The EoS-meter is model independent and insensitive to
other simulation inputs, especially the initial conditions. Thus it provides a
powerful direct-connection of heavy-ion collision observables with the bulk
properties of QCD.
Automatic 3D Cardiovascular MR Segmentation with Densely-Connected Volumetric ConvNets
Authors: Lequan Yu, Jie-Zhi Cheng, Qi Dou, Xin Yang, Hao Chen, Jing Qin, Pheng-Ann Heng
Date: 2017-08-02 00:00:00

Automatic and accurate whole-heart and great vessel segmentation from 3D
cardiac magnetic resonance (MR) images plays an important role in the
computer-assisted diagnosis and treatment of cardiovascular disease. However,
this task is very challenging due to ambiguous cardiac borders and large
anatomical variations among different subjects. In this paper, we propose a
novel densely-connected volumetric convolutional neural network, referred as
DenseVoxNet, to automatically segment the cardiac and vascular structures from
3D cardiac MR images. The DenseVoxNet adopts the 3D fully convolutional
architecture for effective volume-to-volume prediction. From the learning
perspective, our DenseVoxNet has three compelling advantages. First, it
preserves the maximum information flow between layers by a densely-connected
mechanism and hence eases the network training. Second, it avoids learning
redundant feature maps by encouraging feature reuse and hence requires fewer
parameters to achieve high performance, which is essential for medical
applications with limited training data. Third, we add auxiliary side paths to
strengthen the gradient propagation and stabilize the learning process. We
demonstrate the effectiveness of DenseVoxNet by comparing it with the
state-of-the-art approaches from HVSMR 2016 challenge in conjunction with
MICCAI, and our network achieves the best dice coefficient. We also show that
our network can achieve better performance than other 3D ConvNets but with
fewer parameters.
On Tensor Train Rank Minimization: Statistical Efficiency and Scalable Algorithm
Authors: Masaaki Imaizumi, Takanori Maehara, Kohei Hayashi
Date: 2017-08-01 00:00:00

Tensor train (TT) decomposition provides a space-efficient representation for
higher-order tensors. Despite its advantage, we face two crucial limitations
when we apply the TT decomposition to machine learning problems: the lack of
statistical theory and of scalable algorithms. In this paper, we address the
limitations. First, we introduce a convex relaxation of the TT decomposition
problem and derive its error bound for the tensor completion task. Next, we
develop an alternating optimization method with a randomization technique, in
which the time complexity is as efficient as the space complexity is. In
experiments, we numerically confirm the derived bounds and empirically
demonstrate the performance of our method with a real higher-order tensor.