pwc-archive/papers-with-abstracts huggingface dataset:
Exploring object-centric and scene-centric CNN features and their complementarity for human rights violations recognition in images
Authors: Grigorios Kalliatakis, Shoaib Ehsan, Ales Leonardis, Klaus McDonald-Maier
Date: 2018-05-12 00:00:00
Tasks: Representation Learning, Transfer Learning

Identifying potential abuses of human rights through imagery is a novel and
challenging task in the field of computer vision, that will enable to expose
human rights violations over large-scale data that may otherwise be impossible.
While standard databases for object and scene categorisation contain hundreds
of different classes, the largest available dataset of human rights violations
contains only 4 classes. Here, we introduce the `Human Rights Archive Database'
(HRA), a verified-by-experts repository of 3050 human rights violations
photographs, labelled with human rights semantic categories, comprising a list
of the types of human rights abuses encountered at present. With the HRA
dataset and a two-phase transfer learning scheme, we fine-tuned the
state-of-the-art deep convolutional neural networks (CNNs) to provide human
rights violations classification CNNs (HRA-CNNs). We also present extensive
experiments refined to evaluate how well object-centric and scene-centric CNN
features can be combined for the task of recognising human rights abuses. With
this, we show that HRA database poses a challenge at a higher level for the
well studied representation learning methods, and provide a benchmark in the
task of human rights violations recognition in visual context. We expect this
dataset can help to open up new horizons on creating systems able of
recognising rich information about human rights violations. Our dataset, codes
and trained models are available online at
https://github.com/GKalliatakis/Human-Rights-Archive-CNNs.
Deep learning evaluation using deep linguistic processing
Authors: Alexander Kuhnle, Ann Copestake
Date: 2017-06-05 00:00:00
Proceeding: WS 2018 6
Tasks: Deep Learning, Multimodal Deep Learning, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

We discuss problems with the standard approaches to evaluation for tasks like
visual question answering, and argue that artificial data can be used to
address these as a complement to current practice. We demonstrate that with the
help of existing 'deep' linguistic processing technology we are able to create
challenging abstract datasets, which enable us to investigate the language
understanding abilities of multimodal deep learning models in detail, as
compared to a single performance value on a static and monolithic dataset.
A probabilistic network for the diagnosis of acute cardiopulmonary diseases
Authors: Alessandro Magrini, Davide Luciani, Federico Mattia Stefanini
Date: 2016-09-22 00:00:00
Tasks: Medical Diagnosis

In this paper, the development of a probabilistic network for the diagnosis
of acute cardiopulmonary diseases is presented. This paper is a draft version
of the article published after peer review in 2018
(https://doi.org/10.1002/bimj.201600206). A panel of expert physicians
collaborated to specify the qualitative part, that is a directed acyclic graph
defining a factorization of the joint probability distribution of domain
variables. The quantitative part, that is the set of all conditional
probability distributions defined by each factor, was estimated in the Bayesian
paradigm: we applied a special formal representation, characterized by a low
number of parameters and a parameterization intelligible for physicians,
elicited the joint prior distribution of parameters from medical experts, and
updated it by conditioning on a dataset of hospital patient records using
Markov Chain Monte Carlo simulation. Refinement was cyclically performed until
the probabilistic network provided satisfactory Concordance Index values for a
selection of acute diseases and reasonable inference on six fictitious patient
cases. The probabilistic network can be employed to perform medical diagnosis
on a total of 63 diseases (38 acute and 25 chronic) on the basis of up to 167
patient findings.
Gaussian Mixture Latent Vector Grammars
Authors: Yanpeng Zhao, Liwen Zhang, Kewei Tu
Date: 2018-05-12 00:00:00
Proceeding: ACL 2018 7
Tasks: Constituency Parsing, Part-Of-Speech Tagging

We introduce Latent Vector Grammars (LVeGs), a new framework that extends
latent variable grammars such that each nonterminal symbol is associated with a
continuous vector space representing the set of (infinitely many) subtypes of
the nonterminal. We show that previous models such as latent variable grammars
and compositional vector grammars can be interpreted as special cases of LVeGs.
We then present Gaussian Mixture LVeGs (GM-LVeGs), a new special case of LVeGs
that uses Gaussian mixtures to formulate the weights of production rules over
subtypes of nonterminals. A major advantage of using Gaussian mixtures is that
the partition function and the expectations of subtype rules can be computed
using an extension of the inside-outside algorithm, which enables efficient
inference and learning. We apply GM-LVeGs to part-of-speech tagging and
constituency parsing and show that GM-LVeGs can achieve competitive accuracies.
Our code is available at https://github.com/zhaoyanpeng/lveg.
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Authors: Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, Trevor Darrell
Date: 2018-05-12 00:00:00
Proceeding: CVPR 2020 6
Tasks: Autonomous Driving, Diversity, Domain Adaptation, Drivable Area Detection, Instance Segmentation, Lane Detection, Multi-Object Tracking, Multi-Object Tracking and Segmentation, Multiple Object Tracking, Panoptic Segmentation, Semantic Segmentation, Semi-Supervised Instance Segmentation

Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to study a small set of problems on one dataset, while real-world computer vision applications require performing tasks of various complexities. We construct BDD100K, the largest driving video dataset with 100K videos and 10 tasks to evaluate the exciting progress of image recognition algorithms on autonomous driving. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models that are less likely to be surprised by new conditions. Based on this diverse dataset, we build a benchmark for heterogeneous multitask learning and study how to solve the tasks together. Our experiments show that special training strategies are needed for existing models to perform such heterogeneous tasks. BDD100K opens the door for future studies in this important venue.
Task Transfer by Preference-Based Cost Learning
Authors: Mingxuan Jing, Xiaojian Ma, Wenbing Huang, Fuchun Sun, Huaping Liu
Date: 2018-05-12 00:00:00
Tasks: Reinforcement Learning

The goal of task transfer in reinforcement learning is migrating the action
policy of an agent to the target task from the source task. Given their
successes on robotic action planning, current methods mostly rely on two
requirements: exactly-relevant expert demonstrations or the explicitly-coded
cost function on target task, both of which, however, are inconvenient to
obtain in practice. In this paper, we relax these two strong conditions by
developing a novel task transfer framework where the expert preference is
applied as a guidance. In particular, we alternate the following two steps:
Firstly, letting experts apply pre-defined preference rules to select related
expert demonstrates for the target task. Secondly, based on the selection
result, we learn the target cost function and trajectory distribution
simultaneously via enhanced Adversarial MaxEnt IRL and generate more
trajectories by the learned target distribution for the next preference
selection. The theoretical analysis on the distribution learning and
convergence of the proposed algorithm are provided. Extensive simulations on
several benchmarks have been conducted for further verifying the effectiveness
of the proposed method.
Mirror Descent Search and its Acceleration
Authors: Megumi Miyashita, Shiro Yano, Toshiyuki Kondo
Date: 2017-09-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In recent years, attention has been focused on the relationship between
black-box optimiza- tion problem and reinforcement learning problem. In this
research, we propose the Mirror Descent Search (MDS) algorithm which is
applicable both for black box optimization prob- lems and reinforcement
learning problems. Our method is based on the mirror descent method, which is a
general optimization algorithm. The contribution of this research is roughly
twofold. We propose two essential algorithms, called MDS and Accelerated Mirror
Descent Search (AMDS), and two more approximate algorithms: Gaussian Mirror
Descent Search (G-MDS) and Gaussian Accelerated Mirror Descent Search (G-AMDS).
This re- search shows that the advanced methods developed in the context of the
mirror descent research can be applied to reinforcement learning problem. We
also clarify the relationship between an existing reinforcement learning
algorithm and our method. With two evaluation experiments, we show our proposed
algorithms converge faster than some state-of-the-art methods.
Huge Automatically Extracted Training Sets for Multilingual Word Sense Disambiguation
Authors: Tommaso Pasini, Francesco Maria Elia, Roberto Navigli
Date: 2018-05-12 00:00:00
Tasks: Word Sense Disambiguation

We release to the community six large-scale sense-annotated datasets in
multiple language to pave the way for supervised multilingual Word Sense
Disambiguation. Our datasets cover all the nouns in the English WordNet and
their translations in other languages for a total of millions of sense-tagged
sentences. Experiments prove that these corpora can be effectively used as
training sets for supervised WSD systems, surpassing the state of the art for
low-resourced languages and providing competitive results for English, where
manually annotated training sets are accessible. The data is available at
trainomatic.org.
AdvEntuRe: Adversarial Training for Textual Entailment with Knowledge-Guided Examples
Authors: Dongyeop Kang, Tushar Khot, Ashish Sabharwal, Eduard Hovy
Date: 2018-05-12 00:00:00
Proceeding: ACL 2018 7
Tasks: Natural Language Inference, Negation

We consider the problem of learning textual entailment models with limited
supervision (5K-10K training examples), and present two complementary
approaches for it. First, we propose knowledge-guided adversarial example
generators for incorporating large lexical resources in entailment models via
only a handful of rule templates. Second, to make the entailment model - a
discriminator - more robust, we propose the first GAN-style approach for
training it using a natural language example generator that iteratively adjusts
based on the discriminator's performance. We demonstrate effectiveness using
two entailment datasets, where the proposed methods increase accuracy by 4.7%
on SciTail and by 2.8% on a 1% training sub-sample of SNLI. Notably, even a
single hand-written rule, negate, improves the accuracy on the negation
examples in SNLI by 6.1%.
I Have Seen Enough: A Teacher Student Network for Video Classification Using Fewer Frames
Authors: Shweta Bhardwaj, Mitesh M. Khapra
Date: 2018-05-12 00:00:00
Tasks: Classification, General Classification, Question Answering, Video Classification

Over the past few years, various tasks involving videos such as
classification, description, summarization and question answering have received
a lot of attention. Current models for these tasks compute an encoding of the
video by treating it as a sequence of images and going over every image in the
sequence. However, for longer videos this is very time consuming. In this
paper, we focus on the task of video classification and aim to reduce the
computational time by using the idea of distillation. Specifically, we first
train a teacher network which looks at all the frames in a video and computes a
representation for the video. We then train a student network whose objective
is to process only a small fraction of the frames in the video and still
produce a representation which is very close to the representation computed by
the teacher network. This smaller student network involving fewer computations
can then be employed at inference time for video classification. We experiment
with the YouTube-8M dataset and show that the proposed student network can
reduce the inference time by upto 30% with a very small drop in the performance
Examining a hate speech corpus for hate speech detection and popularity prediction
Authors: Filip Klubička, Raquel Fernández
Date: 2018-05-12 00:00:00
Tasks: Hate Speech Detection

As research on hate speech becomes more and more relevant every day, most of
it is still focused on hate speech detection. By attempting to replicate a hate
speech detection experiment performed on an existing Twitter corpus annotated
for hate speech, we highlight some issues that arise from doing research in the
field of hate speech, which is essentially still in its infancy. We take a
critical look at the training corpus in order to understand its biases, while
also using it to venture beyond hate speech detection and investigate whether
it can be used to shed light on other facets of research, such as popularity of
hate tweets.
Backpropagating through Structured Argmax using a SPIGOT
Authors: Hao Peng, Sam Thomson, Noah A. Smith
Date: 2018-05-12 00:00:00
Proceeding: ACL 2018 7
Tasks: Dependency Parsing, Reinforcement Learning, Semantic Dependency Parsing, Semantic Parsing, Sentiment Analysis, Sentiment Classification

We introduce the structured projection of intermediate gradients optimization
technique (SPIGOT), a new method for backpropagating through neural networks
that include hard-decision structured predictions (e.g., parsing) in
intermediate layers. SPIGOT requires no marginal inference, unlike structured
attention networks (Kim et al., 2017) and some reinforcement learning-inspired
solutions (Yogatama et al., 2017). Like so-called straight-through estimators
(Hinton, 2012), SPIGOT defines gradient-like quantities associated with
intermediate nondifferentiable operations, allowing backpropagation before and
after them; SPIGOT's proxy aims to ensure that, after a parameter update, the
intermediate structure will remain well-formed.
  We experiment on two structured NLP pipelines: syntactic-then-semantic
dependency parsing, and semantic parsing followed by sentiment classification.
We show that training with SPIGOT leads to a larger improvement on the
downstream task than a modularly-trained pipeline, the straight-through
estimator, and structured attention, reaching a new state of the art on
semantic dependency parsing.
MojiTalk: Generating Emotional Responses at Scale
Authors: Xianda Zhou, William Yang Wang
Date: 2017-11-11 00:00:00
Proceeding: ACL 2018 7
Tasks: Sentence

Generating emotional language is a key step towards building empathetic
natural language processing agents. However, a major challenge for this line of
research is the lack of large-scale labeled training data, and previous studies
are limited to only small sets of human annotated sentiment labels.
Additionally, explicitly controlling the emotion and sentiment of generated
text is also difficult. In this paper, we take a more radical approach: we
exploit the idea of leveraging Twitter data that are naturally labeled with
emojis. More specifically, we collect a large corpus of Twitter conversations
that include emojis in the response, and assume the emojis convey the
underlying emotions of the sentence. We then introduce a reinforced conditional
variational encoder approach to train a deep generative model on these
conversations, which allows us to use emojis to control the emotion of the
generated text. Experimentally, we show in our quantitative and qualitative
analyses that the proposed models can successfully generate high-quality
abstractive conversation responses in accordance with designated emotions.
Fast Symbolic 3D Registration Solution
Authors: Jin Wu, Ming Liu, Zebo Zhou, Rui Li
Date: 2018-05-12 00:00:00

3D registration has always been performed invoking singular value decomposition (SVD) or eigenvalue decomposition (EIG) in real engineering practices. However, these numerical algorithms suffer from uncertainty of convergence in many cases. A novel fast symbolic solution is proposed in this paper by following our recent publication in this journal. The equivalence analysis shows that our previous solver can be converted to deal with the 3D registration problem. Rather, the computation procedure is studied for further simplification of computing without complex-number support. Experimental results show that the proposed solver does not loose accuracy and robustness but improves the execution speed to a large extent by almost %50 to %80, on both personal computer and embedded processor.
Weight Initialization in Neural Language Models
Authors: Ameet Deshpande, Vedant Somani
Date: 2018-05-12 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Semantic Similarity is an important application which finds its use in many
downstream NLP applications. Though the task is mathematically defined,
semantic similarity's essence is to capture the notions of similarity
impregnated in humans. Machines use some heuristics to calculate the similarity
between words, but these are typically corpus dependent or are useful for
specific domains. The difference between Semantic Similarity and Semantic
Relatedness motivates the development of new algorithms. For a human, the word
car and road are probably as related as car and bus. But this may not be the
case for computational methods. Ontological methods are good at encoding
Semantic Similarity and Vector Space models are better at encoding Semantic
Relatedness. There is a dearth of methods which leverage ontologies to create
better vector representations. The aim of this proposal is to explore in the
direction of a hybrid method which combines statistical/vector space methods
like Word2Vec and Ontological methods like WordNet to leverage the advantages
provided by both.
A Graph-to-Sequence Model for AMR-to-Text Generation
Authors: Linfeng Song, Yue Zhang, Zhiguo Wang, Daniel Gildea
Date: 2018-05-07 00:00:00
Proceeding: ACL 2018 7
Tasks: AMR-to-Text Generation, Graph-to-Sequence, Text Generation

The problem of AMR-to-text generation is to recover a text representing the
same meaning as an input AMR graph. The current state-of-the-art method uses a
sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR
structure. Although being able to model non-local semantic information, a
sequence LSTM can lose information from the AMR graph structure, and thus faces
challenges with large graphs, which result in long sequences. We introduce a
neural graph-to-sequence model, using a novel LSTM structure for directly
encoding graph-level semantics. On a standard benchmark, our model shows
superior results to existing methods in the literature.
Direction-aware Spatial Context Features for Shadow Detection and Removal
Authors: Xiaowei Hu, Chi-Wing Fu, Lei Zhu, Jing Qin, Pheng-Ann Heng
Date: 2018-05-12 00:00:00
Tasks: Shadow Detection, Shadow Detection And Removal, Shadow Removal

Shadow detection and shadow removal are fundamental and challenging tasks, requiring an understanding of the global image semantics. This paper presents a novel deep neural network design for shadow detection and removal by analyzing the spatial image context in a direction-aware manner. To achieve this, we first formulate the direction-aware attention mechanism in a spatial recurrent neural network (RNN) by introducing attention weights when aggregating spatial context features in the RNN. By learning these weights through training, we can recover direction-aware spatial context (DSC) for detecting and removing shadows. This design is developed into the DSC module and embedded in a convolutional neural network (CNN) to learn the DSC features at different levels. Moreover, we design a weighted cross entropy loss to make effective the training for shadow detection and further adopt the network for shadow removal by using a Euclidean loss function and formulating a color transfer function to address the color and luminosity inconsistencies in the training pairs. We employed two shadow detection benchmark datasets and two shadow removal benchmark datasets, and performed various experiments to evaluate our method. Experimental results show that our method performs favorably against the state-of-the-art methods for both shadow detection and shadow removal.
Image-derived generative modeling of pseudo-macromolecular structures - towards the statistical assessment of Electron CryoTomography template matching
Authors: Kai Wen Wang, Xiangrui Zeng, Xiaodan Liang, Zhiguang Huo, Eric P. Xing, Min Xu
Date: 2018-05-12 00:00:00
Tasks: Generative Adversarial Network, Template Matching, Two-sample testing

Cellular Electron CryoTomography (CECT) is a 3D imaging technique that
captures information about the structure and spatial organization of
macromolecular complexes within single cells, in near-native state and at
sub-molecular resolution. Although template matching is often used to locate
macromolecules in a CECT image, it is insufficient as it only measures the
relative structural similarity. Therefore, it is preferable to assess the
statistical credibility of the decision through hypothesis testing, requiring
many templates derived from a diverse population of macromolecular structures.
Due to the very limited number of known structures, we need a generative model
to efficiently and reliably sample pseudo-structures from the complex
distribution of macromolecular structures. To address this challenge, we
propose a novel image-derived approach for performing hypothesis testing for
template matching by constructing generative models using the generative
adversarial network. Finally, we conducted hypothesis testing experiments for
template matching on both simulated and experimental subtomograms, allowing us
to conclude the identity of subtomograms with high statistical credibility and
significantly reducing false positives.
Semi-Supervised Multi-Organ Segmentation via Deep Multi-Planar Co-Training
Authors: Yuyin Zhou, Yan Wang, Peng Tang, Song Bai, Wei Shen, Elliot K. Fishman, Alan L. Yuille
Date: 2018-04-07 00:00:00
Tasks: Image Segmentation, Organ Segmentation, Segmentation, Semantic Segmentation

In multi-organ segmentation of abdominal CT scans, most existing fully
supervised deep learning algorithms require lots of voxel-wise annotations,
which are usually difficult, expensive, and slow to obtain. In comparison,
massive unlabeled 3D CT volumes are usually easily accessible. Current
mainstream works to address the semi-supervised biomedical image segmentation
problem are mostly graph-based. By contrast, deep network based semi-supervised
learning methods have not drawn much attention in this field. In this work, we
propose Deep Multi-Planar Co-Training (DMPCT), whose contributions can be
divided into two folds: 1) The deep model is learned in a co-training style
which can mine consensus information from multiple planes like the sagittal,
coronal, and axial planes; 2) Multi-planar fusion is applied to generate more
reliable pseudo-labels, which alleviates the errors occurring in the
pseudo-labels and thus can help to train better segmentation networks.
Experiments are done on our newly collected large dataset with 100 unlabeled
cases as well as 210 labeled cases where 16 anatomical structures are manually
annotated by four radiologists and confirmed by a senior expert. The results
suggest that DMPCT significantly outperforms the fully supervised method by
more than 4% especially when only a small set of annotations is used.