pwc-archive/papers-with-abstracts huggingface dataset:
Angular Triplet-Center Loss for Multi-view 3D Shape Retrieval
Authors: Zhaoqun Li, Cheng Xu, Biao Leng
Date: 2018-11-21 00:00:00
Tasks: 3D Object Retrieval, 3D Shape Classification, 3D Shape Retrieval, Multi-View 3D Shape Retrieval, Retrieval, Triplet

How to obtain the desirable representation of a 3D shape, which is
discriminative across categories and polymerized within classes, is a
significant challenge in 3D shape retrieval. Most existing 3D shape retrieval
methods focus on capturing strong discriminative shape representation with
softmax loss for the classification task, while the shape feature learning with
metric loss is neglected for 3D shape retrieval. In this paper, we address this
problem based on the intuition that the cosine distance of shape embeddings
should be close enough within the same class and far away across categories.
Since most of 3D shape retrieval tasks use cosine distance of shape features
for measuring shape similarity, we propose a novel metric loss named angular
triplet-center loss, which directly optimizes the cosine distances between the
features. It inherits the triplet-center loss property to achieve larger
inter-class distance and smaller intra-class distance simultaneously. Unlike
previous metric loss utilized in 3D shape retrieval methods, where Euclidean
distance is adopted and the margin design is difficult, the proposed method is
more convenient to train feature embeddings and more suitable for 3D shape
retrieval. Moreover, the angle margin is adopted to replace the cosine margin
in order to provide more explicit discriminative constraints on an embedding
space. Extensive experimental results on two popular 3D object retrieval
benchmarks, ModelNet40 and ShapeNetCore 55, demonstrate the effectiveness of
our proposed loss, and our method has achieved state-of-the-art results on
various 3D shape datasets.
Multi Task Deep Morphological Analyzer: Context Aware Joint Morphological Tagging and Lemma Prediction
Authors: Saurav Jha, Akhilesh Sudhakar, Anil Kumar Singh
Date: 2018-11-21 00:00:00
Tasks: Dependency Parsing, LEMMA, Machine Translation, Morphological Analysis, Morphological Tagging, POS, TAG

The ambiguities introduced by the recombination of morphemes constructing several possible inflections for a word makes the prediction of syntactic traits in Morphologically Rich Languages (MRLs) a notoriously complicated task. We propose the Multi Task Deep Morphological analyzer (MT-DMA), a character-level neural morphological analyzer based on multitask learning of word-level tag markers for Hindi and Urdu. MT-DMA predicts a set of six morphological tags for words of Indo-Aryan languages: Parts-of-speech (POS), Gender (G), Number (N), Person (P), Case (C), Tense-Aspect-Modality (TAM) marker as well as the Lemma (L) by jointly learning all these in one trainable framework. We show the effectiveness of training of such deep neural networks by the simultaneous optimization of multiple loss functions and sharing of initial parameters for context-aware morphological analysis. Exploiting character-level features in phonological space optimized for each tag using multi-objective genetic algorithm, our model establishes a new state-of-the-art accuracy score upon all seven of the tasks for both the languages. MT-DMA is publicly accessible: code, models and data are available at https://github.com/Saurav0074/morph_analyzer.
Neural Networks with Activation Networks
Authors: Jinhyeok Jang, Jaehong Kim, Jaeyeon Lee, Seungjoon Yang
Date: 2018-11-21 00:00:00

This work presents an adaptive activation method for neural networks that
exploits the interdependency of features. Each pixel, node, and layer is
assigned with a polynomial activation function, whose coefficients are provided
by an auxiliary activation network. The activation of a feature depends on the
features of neighboring pixels in a convolutional layer and other nodes in a
dense layer. The dependency is learned from data by the activation networks. In
our experiments, networks with activation networks provide significant
performance improvement compared to the baseline networks on which they are
built. The proposed method can be used to improve the network performance as an
alternative to increasing the number of nodes and layers.
Unsupervised Multimodal Representation Learning across Medical Images and Reports
Authors: Tzu-Ming Harry Hsu, Wei-Hung Weng, Willie Boag, Matthew McDermott, Peter Szolovits
Date: 2018-11-21 00:00:00
Tasks: Representation Learning, Retrieval

Joint embeddings between medical imaging modalities and associated radiology
reports have the potential to offer significant benefits to the clinical
community, ranging from cross-domain retrieval to conditional generation of
reports to the broader goals of multimodal representation learning. In this
work, we establish baseline joint embedding results measured via both local and
global retrieval methods on the soon to be released MIMIC-CXR dataset
consisting of both chest X-ray images and the associated radiology reports. We
examine both supervised and unsupervised methods on this task and show that for
document retrieval tasks with the learned representations, only a limited
amount of supervision is needed to yield results comparable to those of
fully-supervised methods.
A Novel Integrated Framework for Learning both Text Detection and Recognition
Authors: Wanchen Sui, Qing Zhang, Jun Yang, Wei Chu
Date: 2018-11-21 00:00:00
Tasks: Text Detection

In this paper, we propose a novel integrated framework for learning both text
detection and recognition. For most of the existing methods, detection and
recognition are treated as two isolated tasks and trained separately, since
parameters of detection and recognition models are different and two models
target to optimize their own loss functions during individual training
processes. In contrast to those methods, by sharing model parameters, we merge
the detection model and recognition model into a single end-to-end trainable
model and train the joint model for two tasks simultaneously. The shared
parameters not only help effectively reduce the computational load in inference
process, but also improve the end-to-end text detection-recognition accuracy.
In addition, we design a simpler and faster sequence learning method for the
recognition network based on a succession of stacked convolutional layers
without any recurrent structure, this is proved feasible and dramatically
improves inference speed. Extensive experiments on different datasets
demonstrate that the proposed method achieves very promising results.
Convolutional Spatial Attention Model for Reading Comprehension with Multiple-Choice Questions
Authors: Zhipeng Chen, Yiming Cui, Wentao Ma, Shijin Wang, Guoping Hu
Date: 2018-11-21 00:00:00
Tasks: Machine Reading Comprehension, Multiple-choice, Reading Comprehension

Machine Reading Comprehension (MRC) with multiple-choice questions requires
the machine to read given passage and select the correct answer among several
candidates. In this paper, we propose a novel approach called Convolutional
Spatial Attention (CSA) model which can better handle the MRC with
multiple-choice questions. The proposed model could fully extract the mutual
information among the passage, question, and the candidates, to form the
enriched representations. Furthermore, to merge various attention results, we
propose to use convolutional operation to dynamically summarize the attention
values within the different size of regions. Experimental results show that the
proposed model could give substantial improvements over various
state-of-the-art systems on both RACE and SemEval-2018 Task11 datasets.
Scene Text Detection with Supervised Pyramid Context Network
Authors: Enze Xie, Yuhang Zang, Shuai Shao, Gang Yu, Cong Yao, Guangyao Li
Date: 2018-11-21 00:00:00
Tasks: Diversity, Instance Segmentation, Scene Text Detection, Semantic Segmentation, Text Detection

Scene text detection methods based on deep learning have achieved remarkable
results over the past years. However, due to the high diversity and complexity
of natural scenes, previous state-of-the-art text detection methods may still
produce a considerable amount of false positives, when applied to images
captured in real-world environments. To tackle this issue, mainly inspired by
Mask R-CNN, we propose in this paper an effective model for scene text
detection, which is based on Feature Pyramid Network (FPN) and instance
segmentation. We propose a supervised pyramid context network (SPCNET) to
precisely locate text regions while suppressing false positives. Benefited from
the guidance of semantic information and sharing FPN, SPCNET obtains
significantly enhanced performance while introducing marginal extra
computation. Experiments on standard datasets demonstrate that our SPCNET
clearly outperforms start-of-the-art methods. Specifically, it achieves an
F-measure of 92.1% on ICDAR2013, 87.2% on ICDAR2015, 74.1% on ICDAR2017 MLT and
82.9% on Total-Text.
Neural Collective Entity Linking
Authors: Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu
Date: 2018-11-21 00:00:00
Proceeding: COLING 2018 8
Tasks: Entity Linking

Entity Linking aims to link entity mentions in texts to knowledge bases, and
neural models have achieved recent success in this task. However, most existing
methods rely on local contexts to resolve entities independently, which may
usually fail due to the data sparsity of local information. To address this
issue, we propose a novel neural model for collective entity linking, named as
NCEL. NCEL applies Graph Convolutional Network to integrate both local
contextual features and global coherence information for entity linking. To
improve the computation efficiency, we approximately perform graph convolution
on a subgraph of adjacent entity mentions instead of those in the entire text.
We further introduce an attention scheme to improve the robustness of NCEL to
data noise and train the model on Wikipedia hyperlinks to avoid overfitting and
domain bias. In experiments, we evaluate NCEL on five publicly available
datasets to verify the linking performance as well as generalization ability.
We also conduct an extensive analysis of time complexity, the impact of key
modules, and qualitative results, which demonstrate the effectiveness and
efficiency of our proposed method.
Contextualized Non-local Neural Networks for Sequence Learning
Authors: Pengfei Liu, Shuaichen Chang, Xuanjing Huang, Jian Tang, Jackie Chi Kit Cheung
Date: 2018-11-21 00:00:00
Tasks: General Classification, Sentence, text-classification, Text Classification

Recently, a large number of neural mechanisms and models have been proposed
for sequence learning, of which self-attention, as exemplified by the
Transformer model, and graph neural networks (GNNs) have attracted much
attention. In this paper, we propose an approach that combines and draws on the
complementary strengths of these two methods. Specifically, we propose
contextualized non-local neural networks (CN$^{\textbf{3}}$), which can both
dynamically construct a task-specific structure of a sentence and leverage rich
local dependencies within a particular neighborhood.
  Experimental results on ten NLP tasks in text classification, semantic
matching, and sequence labeling show that our proposed model outperforms
competitive baselines and discovers task-specific dependency structures, thus
providing better interpretability to users.
M2E-Try On Net: Fashion from Model to Everyone
Authors: Zhonghua Wu, Guosheng Lin, Qingyi Tao, Jianfei Cai
Date: 2018-11-21 00:00:00
Tasks: Virtual Try-on

Most existing virtual try-on applications require clean clothes images. Instead, we present a novel virtual Try-On network, M2E-Try On Net, which transfers the clothes from a model image to a person image without the need of any clean product images. To obtain a realistic image of person wearing the desired model clothes, we aim to solve the following challenges: 1) non-rigid nature of clothes - we need to align poses between the model and the user; 2) richness in textures of fashion items - preserving the fine details and characteristics of the clothes is critical for photo-realistic transfer; 3) variation of identity appearances - it is required to fit the desired model clothes to the person identity seamlessly. To tackle these challenges, we introduce three key components, including the pose alignment network (PAN), the texture refinement network (TRN) and the fitting network (FTN). Since it is unlikely to gather image pairs of input person image and desired output image (i.e. person wearing the desired clothes), our framework is trained in a self-supervised manner to gradually transfer the poses and textures of the model's clothes to the desired appearance. In the experiments, we verify on the Deep Fashion dataset and MVC dataset that our method can generate photo-realistic images for the person to try-on the model clothes. Furthermore, we explore the model capability for different fashion items, including both upper and lower garments.
Learning to Attend Relevant Regions in Videos from Eye Fixations
Authors: Thanh T. Nguyen, Dung Nguyen
Date: 2018-11-21 00:00:00

Attentively important regions in video frames account for a majority part of the semantics in each frame. This information is helpful in many applications not only for entertainment (such as auto generating commentary and tourist guide) but also for robotic control which holds a larascope supported for laparoscopic surgery. However, it is not always straightforward to define and locate such semantic regions in videos. In this work, we attempt to address the problem of attending relevant regions in videos by leveraging the eye fixations labels with a RNN-based visual attention model. Our experimental results suggest that this approach holds a good potential to learn to attend semantic regions in videos while its performance also heavily relies on the quality of eye fixations labels.
Measuring Depression Symptom Severity from Spoken Language and 3D Facial Expressions
Authors: Albert Haque, Michelle Guo, Adam S. Miner, Li Fei-Fei
Date: 2018-11-21 00:00:00
Tasks: Diagnostic, Specificity, speech-recognition, Speech Recognition

With more than 300 million people depressed worldwide, depression is a global
problem. Due to access barriers such as social stigma, cost, and treatment
availability, 60% of mentally-ill adults do not receive any mental health
services. Effective and efficient diagnosis relies on detecting clinical
symptoms of depression. Automatic detection of depressive symptoms would
potentially improve diagnostic accuracy and availability, leading to faster
intervention. In this work, we present a machine learning method for measuring
the severity of depressive symptoms. Our multi-modal method uses 3D facial
expressions and spoken language, commonly available from modern cell phones. It
demonstrates an average error of 3.67 points (15.3% relative) on the
clinically-validated Patient Health Questionnaire (PHQ) scale. For detecting
major depressive disorder, our model demonstrates 83.3% sensitivity and 82.6%
specificity. Overall, this paper shows how speech recognition, computer vision,
and natural language processing can be combined to assist mental health
patients and practitioners. This technology could be deployed to cell phones
worldwide and facilitate low-cost universal access to mental health care.
Graph-Adaptive Pruning for Efficient Inference of Convolutional Neural Networks
Authors: Mengdi Wang, Qing Zhang, Jun Yang, Xiaoyuan Cui, Wei. Lin
Date: 2018-11-21 00:00:00
Tasks: Knowledge Distillation, Model Compression

In this work, we propose a graph-adaptive pruning (GAP) method for efficient
inference of convolutional neural networks (CNNs). In this method, the network
is viewed as a computational graph, in which the vertices denote the
computation nodes and edges represent the information flow. Through topology
analysis, GAP is capable of adapting to different network structures,
especially the widely used cross connections and multi-path data flow in recent
novel convolutional models. The models can be adaptively pruned at vertex-level
as well as edge-level without any post-processing, thus GAP can directly get
practical model compression and inference speed-up. Moreover, it does not need
any customized computation library or hardware support. Finetuning is conducted
after pruning to restore the model performance. In the finetuning step, we
adopt a self-taught knowledge distillation (KD) strategy by utilizing
information from the original model, through which, the performance of the
optimized model can be sufficiently improved, without introduction of any other
teacher model. Experimental results show the proposed GAP can achieve promising
result to make inference more efficient, e.g., for ResNeXt-29 on CIFAR10, it
can get 13X model compression and 4.3X practical speed-up with marginal loss of
accuracy.
Real-Time 6D Object Pose Estimation on CPU
Authors: Yoshinori Konishi, Kosuke Hattori, Manabu Hashimoto
Date: 2018-11-21 00:00:00
Tasks: 6D Pose Estimation using RGB, CPU, Object, Pose Estimation, Template Matching

We propose a fast and accurate 6D object pose estimation from a RGB-D image. Our proposed method is template matching based and consists of three main technical components, PCOF-MOD (multimodal PCOF), balanced pose tree (BPT) and optimum memory rearrangement for a coarse-to-fine search. Our model templates on densely sampled viewpoints and PCOF-MOD which explicitly handles a certain range of 3D object pose improve the robustness against background clutters. BPT which is an efficient tree-based data structures for a large number of templates and template matching on rearranged feature maps where nearby features are linearly aligned accelerate the pose estimation. The experimental evaluation on tabletop and bin-picking dataset showed that our method achieved higher accuracy and faster speed in comparison with state-of-the-art techniques including recent CNN based approaches. Moreover, our model templates can be trained only from 3D CAD in a few minutes and the pose estimation run in near real-time (23 fps) on CPU. These features are suitable for any real applications.
Urban Driving with Multi-Objective Deep Reinforcement Learning
Authors: Changjian Li, Krzysztof Czarnecki
Date: 2018-11-21 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Autonomous driving is a challenging domain that entails multiple aspects: a
vehicle should be able to drive to its destination as fast as possible while
avoiding collision, obeying traffic rules and ensuring the comfort of
passengers. In this paper, we present a deep learning variant of thresholded
lexicographic Q-learning for the task of urban driving. Our multi-objective DQN
agent learns to drive on multi-lane roads and intersections, yielding and
changing lanes according to traffic rules. We also propose an extension for
factored Markov Decision Processes to the DQN architecture that provides
auxiliary features for the Q function. This is shown to significantly improve
data efficiency. We then show that the learned policy is able to zero-shot
transfer to a ring road without sacrificing performance.
Progressive Feature Alignment for Unsupervised Domain Adaptation
Authors: Chaoqi Chen, Weiping Xie, Wenbing Huang, Yu Rong, Xinghao Ding, Yue Huang, Tingyang Xu, Junzhou Huang
Date: 2018-11-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a fully-unlabeled target domain. To tackle this task, recent approaches resort to discriminative domain transfer in virtue of pseudo-labels to enforce the class-level distribution alignment across the source and target domains. These methods, however, are vulnerable to the error accumulation and thus incapable of preserving cross-domain category consistency, as the pseudo-labeling accuracy is not guaranteed explicitly. In this paper, we propose the Progressive Feature Alignment Network (PFAN) to align the discriminative features across domains progressively and effectively, via exploiting the intra-class variation in the target domain. To be specific, we first develop an Easy-to-Hard Transfer Strategy (EHTS) and an Adaptive Prototype Alignment (APA) step to train our model iteratively and alternatively. Moreover, upon observing that a good domain adaptation usually requires a non-saturated source classifier, we consider a simple yet efficient way to retard the convergence speed of the source classification loss by further involving a temperature variate into the soft-max function. The extensive experimental results reveal that the proposed PFAN exceeds the state-of-the-art performance on three UDA datasets.
Recent Advances in Open Set Recognition: A Survey
Authors: Chuanxing Geng, Sheng-Jun Huang, Songcan Chen
Date: 2018-11-21 00:00:00
Tasks: General Classification, Open Set Learning, Survey

In real-world recognition/classification tasks, limited by various objective factors, it is usually difficult to collect training samples to exhaust all classes when training a recognizer or classifier. A more realistic scenario is open set recognition (OSR), where incomplete knowledge of the world exists at training time, and unknown classes can be submitted to an algorithm during testing, requiring the classifiers to not only accurately classify the seen classes, but also effectively deal with the unseen ones. This paper provides a comprehensive survey of existing open set recognition techniques covering various aspects ranging from related definitions, representations of models, datasets, evaluation criteria, and algorithm comparisons. Furthermore, we briefly analyze the relationships between OSR and its related tasks including zero-shot, one-shot (few-shot) recognition/learning techniques, classification with reject option, and so forth. Additionally, we also overview the open world recognition which can be seen as a natural extension of OSR. Importantly, we highlight the limitations of existing approaches and point out some promising subsequent research directions in this field.
Population-aware Hierarchical Bayesian Domain Adaptation
Authors: Vishwali Mhasawade, Nabeel Abdur Rehman, Rumi Chunara
Date: 2018-11-21 00:00:00
Tasks: Domain Adaptation

Population attributes are essential in health for understanding who the data
represents and precision medicine efforts. Even within disease infection
labels, patients can exhibit significant variability; "fever" may mean
something different when reported in a doctor's office versus from an online
app, precluding directly learning across different datasets for the same
prediction task. This problem falls into the domain adaptation paradigm.
However, research in this area has to-date not considered who generates the
data; symptoms reported by a woman versus a man, for example, could also have
different implications. We propose a novel population-aware domain adaptation
approach by formulating the domain adaptation task as a multi-source
hierarchical Bayesian framework. The model improves prediction in the case of
largely unlabelled target data by harnessing both domain and population
invariant information.