pwc-archive/papers-with-abstracts huggingface dataset:
Effective and Efficient Dropout for Deep Convolutional Neural Networks
Authors: Shaofeng Cai, Yao Shu, Gang Chen, Beng Chin Ooi, Wei Wang, Meihui Zhang
Date: 2019-04-06 00:00:00
Tasks: Attribute

Convolutional Neural networks (CNNs) based applications have become ubiquitous, where proper regularization is greatly needed. To prevent large neural network models from overfitting, dropout has been widely used as an efficient regularization technique in practice. However, many recent works show that the standard dropout is ineffective or even detrimental to the training of CNNs. In this paper, we revisit this issue and examine various dropout variants in an attempt to improve existing dropout-based regularization techniques for CNNs. We attribute the failure of standard dropout to the conflict between the stochasticity of dropout and its following Batch Normalization (BN), and propose to reduce the conflict by placing dropout operations right before the convolutional operation instead of BN, or totally address this issue by replacing BN with Group Normalization (GN). We further introduce a structurally more suited dropout variant Drop-Conv2d, which provides more efficient and effective regularization for deep CNNs. These dropout variants can be readily integrated into the building blocks of CNNs and implemented in existing deep learning platforms. Extensive experiments on benchmark datasets including CIFAR, SVHN and ImageNet are conducted to compare the existing building blocks and the proposed ones with dropout training. Results show that our building blocks improve over state-of-the-art CNNs significantly, which is mainly due to the better regularization and implicit model ensemble effect.
KNN and ANN-based Recognition of Handwritten Pashto Letters using Zoning Features
Authors: Sulaiman Khan, Hazrat Ali, Zahid Ullah, Nasru Minallah, Shahid Maqsood, Abdul Hafeez
Date: 2019-04-06 00:00:00
Tasks: General Classification

This paper presents a recognition system for handwritten Pashto letters. However, handwritten character recognition is a challenging task. These letters not only differ in shape and style but also vary among individuals. The recognition becomes further daunting due to the lack of standard datasets for inscribed Pashto letters. In this work, we have designed a database of moderate size, which encompasses a total of 4488 images, stemming from 102 distinguishing samples for each of the 44 letters in Pashto. The recognition framework uses zoning feature extractor followed by K-Nearest Neighbour (KNN) and Neural Network (NN) classifiers for classifying individual letter. Based on the evaluation of the proposed system, an overall classification accuracy of approximately 70.05% is achieved by using KNN while 72% is achieved by using NN.
Re-Identification Supervised Texture Generation
Authors: Jian Wang, Yunshan Zhong, Yachun Li, Chi Zhang, Yichen Wei
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Person Re-Identification, Texture Synthesis

The estimation of 3D human body pose and shape from a single image has been
extensively studied in recent years. However, the texture generation problem
has not been fully discussed. In this paper, we propose an end-to-end learning
strategy to generate textures of human bodies under the supervision of person
re-identification. We render the synthetic images with textures extracted from
the inputs and maximize the similarity between the rendered and input images by
using the re-identification network as the perceptual metrics. Experiment
results on pedestrian images show that our model can generate the texture from
a single image and demonstrate that our textures are of higher quality than
those generated by other available methods. Furthermore, we extend the
application scope to other categories and explore the possible utilization of
our generated textures.
Visualization of Convolutional Neural Networks for Monocular Depth Estimation
Authors: Junjie Hu, Yan Zhang, Takayuki Okatani
Date: 2019-04-06 00:00:00
Proceeding: ICCV 2019 10
Tasks: Depth Estimation, Interpretable Machine Learning

Recently, convolutional neural networks (CNNs) have shown great success on
the task of monocular depth estimation. A fundamental yet unanswered question
is: how CNNs can infer depth from a single image. Toward answering this
question, we consider visualization of inference of a CNN by identifying
relevant pixels of an input image to depth estimation. We formulate it as an
optimization problem of identifying the smallest number of image pixels from
which the CNN can estimate a depth map with the minimum difference from the
estimate from the entire image. To cope with a difficulty with optimization
through a deep CNN, we propose to use another network to predict those relevant
image pixels in a forward computation. In our experiments, we first show the
effectiveness of this approach, and then apply it to different depth estimation
networks on indoor and outdoor scene datasets. The results provide several
findings that help exploration of the above question.
Unsupervised Person Image Generation with Semantic Parsing Transformation
Authors: Sijie Song, Wei zhang, Jiaying Liu, Tao Mei
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Generation, Image Manipulation, Semantic Parsing

In this paper, we address unsupervised pose-guided person image generation,
which is known challenging due to non-rigid deformation. Unlike previous
methods learning a rock-hard direct mapping between human bodies, we propose a
new pathway to decompose the hard mapping into two more accessible subtasks,
namely, semantic parsing transformation and appearance generation. Firstly, a
semantic generative network is proposed to transform between semantic parsing
maps, in order to simplify the non-rigid deformation learning. Secondly, an
appearance generative network learns to synthesize semantic-aware textures.
Thirdly, we demonstrate that training our framework in an end-to-end manner
further refines the semantic maps and final results accordingly. Our method is
generalizable to other semantic-aware person image generation tasks, eg,
clothing texture transfer and controlled image manipulation. Experimental
results demonstrate the superiority of our method on DeepFashion and
Market-1501 datasets, especially in keeping the clothing attributes and better
body shapes.
Camera Lens Super-Resolution
Authors: Chang Chen, Zhiwei Xiong, Xinmei Tian, Zheng-Jun Zha, Feng Wu
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Super-Resolution, Super-Resolution

Existing methods for single image super-resolution (SR) are typically
evaluated with synthetic degradation models such as bicubic or Gaussian
downsampling. In this paper, we investigate SR from the perspective of camera
lenses, named as CameraSR, which aims to alleviate the intrinsic tradeoff
between resolution (R) and field-of-view (V) in realistic imaging systems.
Specifically, we view the R-V degradation as a latent model in the SR process
and learn to reverse it with realistic low- and high-resolution image pairs. To
obtain the paired images, we propose two novel data acquisition strategies for
two representative imaging systems (i.e., DSLR and smartphone cameras),
respectively. Based on the obtained City100 dataset, we quantitatively analyze
the performance of commonly-used synthetic degradation models, and demonstrate
the superiority of CameraSR as a practical solution to boost the performance of
existing SR methods. Moreover, CameraSR can be readily generalized to different
content and devices, which serves as an advanced digital zoom tool in realistic
imaging systems. Codes and datasets are available at
https://github.com/ngchc/CameraSR.
Blind Super-Resolution With Iterative Kernel Correction
Authors: Jinjin Gu, Hannan Lu, WangMeng Zuo, Chao Dong
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Blind Super-Resolution, Image Super-Resolution, Super-Resolution

Deep learning based methods have dominated super-resolution (SR) field due to their remarkable performance in terms of effectiveness and efficiency. Most of these methods assume that the blur kernel during downsampling is predefined/known (e.g., bicubic). However, the blur kernels involved in real applications are complicated and unknown, resulting in severe performance drop for the advanced SR methods. In this paper, we propose an Iterative Kernel Correction (IKC) method for blur kernel estimation in blind SR problem, where the blur kernels are unknown. We draw the observation that kernel mismatch could bring regular artifacts (either over-sharpening or over-smoothing), which can be applied to correct inaccurate blur kernels. Thus we introduce an iterative correction scheme -- IKC that achieves better results than direct kernel estimation. We further propose an effective SR network architecture using spatial feature transform (SFT) layers to handle multiple blur kernels, named SFTMD. Extensive experiments on synthetic and real-world images show that the proposed IKC method with SFTMD can provide visually favorable SR results and the state-of-the-art performance in blind SR problem.
Modeling Point Clouds with Self-Attention and Gumbel Subset Sampling
Authors: Jiancheng Yang, Qiang Zhang, Bingbing Ni, Linguo Li, Jinxian Liu, Mengdie Zhou, Qi Tian
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6

Geometric deep learning is increasingly important thanks to the popularity of
3D sensors. Inspired by the recent advances in NLP domain, the self-attention
transformer is introduced to consume the point clouds. We develop Point
Attention Transformers (PATs), using a parameter-efficient Group Shuffle
Attention (GSA) to replace the costly Multi-Head Attention. We demonstrate its
ability to process size-varying inputs, and prove its permutation equivariance.
Besides, prior work uses heuristics dependence on the input data (e.g.,
Furthest Point Sampling) to hierarchically select subsets of input points.
Thereby, we for the first time propose an end-to-end learnable and
task-agnostic sampling operation, named Gumbel Subset Sampling (GSS), to select
a representative subset of input points. Equipped with Gumbel-Softmax, it
produces a "soft" continuous subset in training phase, and a "hard" discrete
subset in test phase. By selecting representative subsets in a hierarchical
fashion, the networks learn a stronger representation of the input sets with
lower computation cost. Experiments on classification and segmentation
benchmarks show the effectiveness and efficiency of our methods. Furthermore,
we propose a novel application, to process event camera stream as point clouds,
and achieve a state-of-the-art performance on DVS128 Gesture Dataset.
Towards Locally Consistent Object Counting with Constrained Multi-stage Convolutional Neural Networks
Authors: Muming Zhao, Jian Zhang, Chongyang Zhang, Wenjun Zhang
Date: 2019-04-06 00:00:00
Tasks: Object, Object Counting

High-density object counting in surveillance scenes is challenging mainly due
to the drastic variation of object scales. The prevalence of deep learning has
largely boosted the object counting accuracy on several benchmark datasets.
However, does the global counts really count? Armed with this question we dive
into the predicted density map whose summation over the whole regions reports
the global counts for more in-depth analysis. We observe that the object
density map generated by most existing methods usually lacks of local
consistency, i.e., counting errors in local regions exist unexpectedly even
though the global count seems to well match with the ground-truth. Towards this
problem, in this paper we propose a constrained multi-stage Convolutional
Neural Networks (CNNs) to jointly pursue locally consistent density map from
two aspects. Different from most existing methods that mainly rely on the
multi-column architectures of plain CNNs, we exploit a stacking formulation of
plain CNNs. Benefited from the internal multi-stage learning process, the
feature map could be repeatedly refined, allowing the density map to approach
the ground-truth density distribution. For further refinement of the density
map, we also propose a grid loss function. With finer local-region-based
supervisions, the underlying model is constrained to generate locally
consistent density values to minimize the training errors considering both the
global and local counts accuracy. Experiments on two widely-tested object
counting benchmarks with overall significant results compared with
state-of-the-art methods demonstrate the effectiveness of our approach.
Evaluating Coherence in Dialogue Systems using Entailment
Authors: Nouha Dziri, Ehsan Kamalloo, Kory W. Mathewson, Osmar Zaiane
Date: 2019-04-06 00:00:00
Proceeding: NAACL 2019 6
Tasks: Dialogue Evaluation, Diversity, Natural Language Inference, Open-Domain Dialog, Sentence

Evaluating open-domain dialogue systems is difficult due to the diversity of possible correct answers. Automatic metrics such as BLEU correlate weakly with human annotations, resulting in a significant bias across different models and datasets. Some researchers resort to human judgment experimentation for assessing response quality, which is expensive, time consuming, and not scalable. Moreover, judges tend to evaluate a small number of dialogues, meaning that minor differences in evaluation configuration may lead to dissimilar results. In this paper, we present interpretable metrics for evaluating topic coherence by making use of distributed sentence representations. Furthermore, we introduce calculable approximations of human judgment based on conversational coherence by adopting state-of-the-art entailment techniques. Results show that our metrics can be used as a surrogate for human judgment, making it easy to evaluate dialogue systems on large-scale datasets and allowing an unbiased estimate for the quality of the responses.
A Novel Neural Network-Based Symbolic Regression Method: Neuro-Encoded Expression Programming
Authors: Aftab Anjum, Fengyang Sun, Lin Wang, Jeff Orchard
Date: 2019-04-06 00:00:00
Tasks: Evolutionary Algorithms, regression, Symbolic Regression

Neuro-encoded expression programming(NEEP) that aims to offer a novel continuous representation of combinatorial encoding for genetic programming methods is proposed in this paper. Genetic programming with linear representation uses nature-inspired operators (e.g., crossover, mutation) to tune expressions and finally search out the best explicit function to simulate data. The encoding mechanism is essential for genetic programmings to find a desirable solution efficiently. However, the linear representation methods manipulate the expression tree in discrete solution space, where a small change of the input can cause a large change of the output. The unsmooth landscapes destroy the local information and make difficulty in searching. The neuro-encoded expression programming constructs the gene string with recurrent neural network (RNN) and the weights of the network are optimized by powerful continuous evolutionary algorithms. The neural network mappings smoothen the sharp fitness landscape and provide rich neighborhood information to find the best expression. The experiments indicate that the novel approach improves training efficiency and reduces test errors on several well-known symbolic regression problems.
Reinforcement Learning with Attention that Works: A Self-Supervised Approach
Authors: Anthony Manchin, Ehsan Abbasnejad, Anton Van Den Hengel
Date: 2019-04-06 00:00:00
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Attention models have had a significant positive impact on deep learning
across a range of tasks. However previous attempts at integrating attention
with reinforcement learning have failed to produce significant improvements. We
propose the first combination of self attention and reinforcement learning that
is capable of producing significant improvements, including new state of the
art results in the Arcade Learning Environment. Unlike the selective attention
models used in previous attempts, which constrain the attention via
preconceived notions of importance, our implementation utilises the Markovian
properties inherent in the state input. Our method produces a faithful
visualisation of the policy, focusing on the behaviour of the agent. Our
experiments demonstrate that the trained policies use multiple simultaneous
foci of attention, and are able to modulate attention over time to deal with
situations of partial observability.
The Steep Road to Happily Ever After: An Analysis of Current Visual Storytelling Models
Authors: Yatri Modi, Natalie Parde
Date: 2019-04-06 00:00:00
Proceeding: WS 2019 6
Tasks: Survey, Visual Storytelling

Visual storytelling is an intriguing and complex task that only recently
entered the research arena. In this work, we survey relevant work to date, and
conduct a thorough error analysis of three very recent approaches to visual
storytelling. We categorize and provide examples of common types of errors, and
identify key shortcomings in current work. Finally, we make recommendations for
addressing these limitations in the future.
ReMASC: Realistic Replay Attack Corpus for Voice Controlled Systems
Authors: Yuan Gong, Jian Yang, Jacob Huber, Mitchell MacKnight, Christian Poellabauer
Date: 2019-04-06 00:00:00
Tasks: Voice Anti-spoofing

This paper introduces a new database of voice recordings with the goal of supporting research on vulnerabilities and protection of voice-controlled systems (VCSs). In contrast to prior efforts, the proposed database contains both genuine voice commands and replayed recordings of such commands, collected in realistic VCSs usage scenarios and using modern voice assistant development kits. Specifically, the database contains recordings from four systems (each with a different microphone array) in a variety of environmental conditions with different forms of background noise and relative positions between speaker and device. To the best of our knowledge, this is the first publicly available database that has been specifically designed for the protection of state-of-the-art voice-controlled systems against various replay attacks in various conditions and environments.
BridgeNet: A Continuity-Aware Probabilistic Network for Age Estimation
Authors: Wanhua Li, Jiwen Lu, Jianjiang Feng, Chunjing Xu, Jie zhou, Qi Tian
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Age Estimation, MORPH

Age estimation is an important yet very challenging problem in computer
vision. Existing methods for age estimation usually apply a divide-and-conquer
strategy to deal with heterogeneous data caused by the non-stationary aging
process. However, the facial aging process is also a continuous process, and
the continuity relationship between different components has not been
effectively exploited. In this paper, we propose BridgeNet for age estimation,
which aims to mine the continuous relation between age labels effectively. The
proposed BridgeNet consists of local regressors and gating networks. Local
regressors partition the data space into multiple overlapping subspaces to
tackle heterogeneous data and gating networks learn continuity aware weights
for the results of local regressors by employing the proposed bridge-tree
structure, which introduces bridge connections into tree models to enforce the
similarity between neighbor nodes. Moreover, these two components of BridgeNet
can be jointly learned in an end-to-end way. We show experimental results on
the MORPH II, FG-NET and Chalearn LAP 2015 datasets and find that BridgeNet
outperforms the state-of-the-art methods.
3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI
Authors: Chen Chen, Xiaopeng Liu, Meng Ding, Junfeng Zheng, Jiangyun Li
Date: 2019-04-06 00:00:00
Tasks: Brain Tumor Segmentation, Segmentation, Tumor Segmentation

Brain tumor segmentation plays a pivotal role in medical image processing. In this work, we aim to segment brain MRI volumes. 3D convolution neural networks (CNN) such as 3D U-Net and V-Net employing 3D convolutions to capture the correlation between adjacent slices have achieved impressive segmentation results. However, these 3D CNN architectures come with high computational overheads due to multiple layers of 3D convolutions, which may make these models prohibitive for practical large-scale applications. To this end, we propose a highly efficient 3D CNN to achieve real-time dense volumetric segmentation. The network leverages the 3D multi-fiber unit which consists of an ensemble of lightweight 3D convolutional networks to significantly reduce the computational cost. Moreover, 3D dilated convolutions are used to build multi-scale feature representations. Extensive experimental results on the BraTS-2018 challenge dataset show that the proposed architecture greatly reduces computation cost while maintaining high accuracy for brain tumor segmentation. The source code can be found at https://github.com/China-LiuXiaopeng/BraTS-DMFNet
Progressive Pose Attention Transfer for Person Image Generation
Authors: Zhen Zhu, Tengteng Huang, Baoguang Shi, Miao Yu, Bofei Wang, Xiang Bai
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Generative Adversarial Network, Image Generation, Person Re-Identification, Pose Transfer

This paper proposes a new generative adversarial network for pose transfer, i.e., transferring the pose of a given person to a target pose. The generator of the network comprises a sequence of Pose-Attentional Transfer Blocks that each transfers certain regions it attends to, generating the person image progressively. Compared with those in previous works, our generated person images possess better appearance consistency and shape consistency with the input images, thus significantly more realistic-looking. The efficacy and efficiency of the proposed network are validated both qualitatively and quantitatively on Market-1501 and DeepFashion. Furthermore, the proposed architecture can generate training images for person re-identification, alleviating data insufficiency. Codes and models are available at: https://github.com/tengteng95/Pose-Transfer.git.
Toward Universal Testing of Dynamic Network Models
Authors: Abram Magner, Wojciech Szpankowski
Date: 2019-04-06 00:00:00

Numerous networks in the real world change over time, in the sense that nodes and edges enter and leave the networks. Various dynamic random graph models have been proposed to explain the macroscopic properties of these systems and to provide a foundation for statistical inferences and predictions. It is of interest to have a rigorous way to determine how well these models match observed networks. We thus ask the following goodness of fit question: given a sequence of observations/snapshots of a growing random graph, along with a candidate model M, can we determine whether the snapshots came from M or from some arbitrary alternative model that is well-separated from M in some natural metric? We formulate this problem precisely and boil it down to goodness of fit testing for graph-valued, infinite-state Markov processes and exhibit and analyze a universal test based on non-stationary sampling for a natural class of models.