pwc-archive/papers-with-abstracts huggingface dataset:
Joint & Progressive Learning from High-Dimensional Data for Multi-Label Classification
Authors: Danfeng Hong, Naoto Yokoya, Jian Xu, Xiaoxiang Zhu
Date: 2018-08-15 00:00:00
Proceeding: ECCV 2018 9
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Vocal Bursts Intensity Prediction

Despite the fact that nonlinear subspace learning techniques (e.g. manifold
learning) have successfully applied to data representation, there is still room
for improvement in explainability (explicit mapping), generalization
(out-of-samples), and cost-effectiveness (linearization). To this end, a novel
linearized subspace learning technique is developed in a joint and progressive
way, called \textbf{j}oint and \textbf{p}rogressive \textbf{l}earning
str\textbf{a}teg\textbf{y} (J-Play), with its application to multi-label
classification. The J-Play learns high-level and semantically meaningful
feature representation from high-dimensional data by 1) jointly performing
multiple subspace learning and classification to find a latent subspace where
samples are expected to be better classified; 2) progressively learning
multi-coupled projections to linearly approach the optimal mapping bridging the
original space with the most discriminative subspace; 3) locally embedding
manifold structure in each learnable latent subspace. Extensive experiments are
performed to demonstrate the superiority and effectiveness of the proposed
method in comparison with previous state-of-the-art methods.
Temporal Sequence Distillation: Towards Few-Frame Action Recognition in Videos
Authors: Zhaoyang Zhang, Zhanghui Kuang, Ping Luo, Litong Feng, Wei zhang
Date: 2018-08-15 00:00:00
Tasks: Action Recognition, Action Recognition In Videos, Temporal Action Localization

Video Analytics Software as a Service (VA SaaS) has been rapidly growing in
recent years. VA SaaS is typically accessed by users using a lightweight
client. Because the transmission bandwidth between the client and cloud is
usually limited and expensive, it brings great benefits to design cloud video
analysis algorithms with a limited data transmission requirement. Although
considerable research has been devoted to video analysis, to our best
knowledge, little of them has paid attention to the transmission bandwidth
limitation in SaaS. As the first attempt in this direction, this work
introduces a problem of few-frame action recognition, which aims at maintaining
high recognition accuracy, when accessing only a few frames during both
training and test. Unlike previous work that processed dense frames, we present
Temporal Sequence Distillation (TSD), which distills a long video sequence into
a very short one for transmission. By end-to-end training with 3D CNNs for
video action recognition, TSD learns a compact and discriminative temporal and
spatial representation of video frames. On Kinetics dataset, TSD+I3D typically
requires only 50\% of the number of frames compared to I3D, a state-of-the-art
video action recognition algorithm, to achieve almost the same accuracies. The
proposed TSD has three appealing advantages. Firstly, TSD has a lightweight
architecture and can be deployed in the client, eg. mobile devices, to produce
compressed representative frames to save transmission bandwidth. Secondly, TSD
significantly reduces the computations to run video action recognition with
compressed frames on the cloud, while maintaining high recognition accuracies.
Thirdly, TSD can be plugged in as a preprocessing module of any existing 3D
CNNs. Extensive experiments show the effectiveness and characteristics of TSD.
SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis
Authors: Imane Guellil, Ahsan Adeel, Faical Azouaou, Amir Hussain
Date: 2018-08-15 00:00:00
Tasks: Sentiment Analysis, Transliteration

Data annotation is an important but time-consuming and costly procedure. To
sort a text into two classes, the very first thing we need is a good annotation
guideline, establishing what is required to qualify for each class. In the
literature, the difficulties associated with an appropriate data annotation has
been underestimated. In this paper, we present a novel approach to
automatically construct an annotated sentiment corpus for Algerian dialect (a
Maghrebi Arabic dialect). The construction of this corpus is based on an
Algerian sentiment lexicon that is also constructed automatically. The
presented work deals with the two widely used scripts on Arabic social media:
Arabic and Arabizi. The proposed approach automatically constructs a sentiment
corpus containing 8000 messages (where 4000 are dedicated to Arabic and 4000 to
Arabizi). The achieved F1-score is up to 72% and 78% for an Arabic and Arabizi
test sets, respectively. Ongoing work is aimed at integrating transliteration
process for Arabizi messages to further improve the obtained results.
Exploiting Deep Learning for Persian Sentiment Analysis
Authors: Kia Dashtipour, Mandar Gogate, Ahsan Adeel, Cosimo Ieracitano, Hadi Larijani, Amir Hussain
Date: 2018-08-15 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Persian Sentiment Analysis, Sentiment Analysis

The rise of social media is enabling people to freely express their opinions
about products and services. The aim of sentiment analysis is to automatically
determine subject's sentiment (e.g., positive, negative, or neutral) towards a
particular aspect such as topic, product, movie, news etc. Deep learning has
recently emerged as a powerful machine learning technique to tackle a growing
demand of accurate sentiment analysis. However, limited work has been conducted
to apply deep learning algorithms to languages other than English, such as
Persian. In this work, two deep learning models (deep autoencoders and deep
convolutional neural networks (CNNs)) are developed and applied to a novel
Persian movie reviews dataset. The proposed deep learning models are analyzed
and compared with the state-of-the-art shallow multilayer perceptron (MLP)
based machine learning model. Simulation results demonstrate the enhanced
performance of deep learning over state-of-the-art MLP.
Multi-feature Fusion for Image Retrieval Using Constrained Dominant Sets
Authors: Leulseged Tesfaye Alemu, Marcello Pelillo
Date: 2018-08-15 00:00:00
Tasks: Image Retrieval, Retrieval

Aggregating different image features for image retrieval has recently shown
its effectiveness. While highly effective, though, the question of how to
uplift the impact of the best features for a specific query image persists as
an open computer vision problem. In this paper, we propose a computationally
efficient approach to fuse several hand-crafted and deep features, based on the
probabilistic distribution of a given membership score of a constrained cluster
in an unsupervised manner. First, we introduce an incremental nearest neighbor
(NN) selection method, whereby we dynamically select k-NN to the query. We then
build several graphs from the obtained NN sets and employ constrained dominant
sets (CDS) on each graph G to assign edge weights which consider the intrinsic
manifold structure of the graph, and detect false matches to the query.
Finally, we elaborate the computation of feature positive-impact weight (PIW)
based on the dispersive degree of the characteristics vector. To this end, we
exploit the entropy of a cluster membership-score distribution. In addition,
the final NN set bypasses a heuristic voting scheme. Experiments on several
retrieval benchmark datasets show that our method can improve the
state-of-the-art result.
Ensemble of Convolutional Neural Networks for Dermoscopic Images Classification
Authors: Tomáš Majtner, Buda Bajić, Sule Yildirim, Jon Yngve Hardeberg, Joakim Lindblad, Nataša Sladoje
Date: 2018-08-15 00:00:00
Tasks: Classification, General Classification, Image Augmentation, Transfer Learning

In this report, we are presenting our automated prediction system for disease
classification within dermoscopic images. The proposed solution is based on
deep learning, where we employed transfer learning strategy on VGG16 and
GoogLeNet architectures. The key feature of our solution is preprocessing based
primarily on image augmentation and colour normalization. The solution was
evaluated on Task 3: Lesion Diagnosis of the ISIC 2018: Skin Lesion Analysis
Towards Melanoma Detection.
Shedding Light on Black Box Machine Learning Algorithms: Development of an Axiomatic Framework to Assess the Quality of Methods that Explain Individual Predictions
Authors: Milo Honegger
Date: 2018-08-15 00:00:00
Tasks: BIG-bench Machine Learning

From self-driving vehicles and back-flipping robots to virtual assistants who
book our next appointment at the hair salon or at that restaurant for dinner -
machine learning systems are becoming increasingly ubiquitous. The main reason
for this is that these methods boast remarkable predictive capabilities.
However, most of these models remain black boxes, meaning that it is very
challenging for humans to follow and understand their intricate inner workings.
Consequently, interpretability has suffered under this ever-increasing
complexity of machine learning models. Especially with regards to new
regulations, such as the General Data Protection Regulation (GDPR), the
necessity for plausibility and verifiability of predictions made by these black
boxes is indispensable. Driven by the needs of industry and practice, the
research community has recognised this interpretability problem and focussed on
developing a growing number of so-called explanation methods over the past few
years. These methods explain individual predictions made by black box machine
learning models and help to recover some of the lost interpretability. With the
proliferation of these explanation methods, it is, however, often unclear,
which explanation method offers a higher explanation quality, or is generally
better-suited for the situation at hand. In this thesis, we thus propose an
axiomatic framework, which allows comparing the quality of different
explanation methods amongst each other. Through experimental validation, we
find that the developed framework is useful to assess the explanation quality
of different explanation methods and reach conclusions that are consistent with
independent research.
Deep RTS: A Game Environment for Deep Reinforcement Learning in Real-Time Strategy Games
Authors: Per-Arne Andersen, Morten Goodwin, Ole-Christoffer Granmo
Date: 2018-08-15 00:00:00
Tasks: Deep Reinforcement Learning, Real-Time Strategy Games, Reinforcement Learning, Reinforcement Learning (RL), Starcraft, Starcraft II

Reinforcement learning (RL) is an area of research that has blossomed
tremendously in recent years and has shown remarkable potential for artificial
intelligence based opponents in computer games. This success is primarily due
to the vast capabilities of convolutional neural networks, that can extract
useful features from noisy and complex data. Games are excellent tools to test
and push the boundaries of novel RL algorithms because they give valuable
insight into how well an algorithm can perform in isolated environments without
the real-life consequences. Real-time strategy games (RTS) is a genre that has
tremendous complexity and challenges the player in short and long-term
planning. There is much research that focuses on applied RL in RTS games, and
novel advances are therefore anticipated in the not too distant future.
However, there are to date few environments for testing RTS AIs. Environments
in the literature are often either overly simplistic, such as microRTS, or
complex and without the possibility for accelerated learning on consumer
hardware like StarCraft II. This paper introduces the Deep RTS game environment
for testing cutting-edge artificial intelligence algorithms for RTS games. Deep
RTS is a high-performance RTS game made specifically for artificial
intelligence research. It supports accelerated learning, meaning that it can
learn at a magnitude of 50 000 times faster compared to existing RTS games.
Deep RTS has a flexible configuration, enabling research in several different
RTS scenarios, including partially observable state-spaces and map complexity.
We show that Deep RTS lives up to our promises by comparing its performance
with microRTS, ELF, and StarCraft II on high-end consumer hardware. Using Deep
RTS, we show that a Deep Q-Network agent beats random-play agents over 70% of
the time. Deep RTS is publicly available at https://github.com/cair/DeepRTS.
A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval
Authors: Federico Magliani, Tomaso Fontanini, Andrea Prati
Date: 2018-08-15 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

The recent advances brought by deep learning allowed to improve the
performance in image retrieval tasks. Through the many convolutional layers,
available in a Convolutional Neural Network (CNN), it is possible to obtain a
hierarchy of features from the evaluated image. At every step, the patches
extracted are smaller than the previous levels and more representative.
Following this idea, this paper introduces a new detector applied on the
feature maps extracted from pre-trained CNN. Specifically, this approach lets
to increase the number of features in order to increase the performance of the
aggregation algorithms like the most famous and used VLAD embedding. The
proposed approach is tested on different public datasets: Holidays, Oxford5k,
Paris6k and UKB.
Statistical Piano Reduction Controlling Performance Difficulty
Authors: Eita Nakamura, Kazuyoshi Yoshii
Date: 2018-08-15 00:00:00

We present a statistical-modelling method for piano reduction, i.e.
converting an ensemble score into piano scores, that can control performance
difficulty. While previous studies have focused on describing the condition for
playable piano scores, it depends on player's skill and can change continuously
with the tempo. We thus computationally quantify performance difficulty as well
as musical fidelity to the original score, and formulate the problem as
optimization of musical fidelity under constraints on difficulty values. First,
performance difficulty measures are developed by means of probabilistic
generative models for piano scores and the relation to the rate of performance
errors is studied. Second, to describe musical fidelity, we construct a
probabilistic model integrating a prior piano-score model and a model
representing how ensemble scores are likely to be edited. An iterative
optimization algorithm for piano reduction is developed based on statistical
inference of the model. We confirm the effect of the iterative procedure; we
find that subjective difficulty and musical fidelity monotonically increase
with controlled difficulty values; and we show that incorporating sequential
dependence of pitches and fingering motion in the piano-score model improves
the quality of reduction scores in high-difficulty cases.
Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization
Authors: Xiaotian Li, Juha Ylioinas, Jakob Verbeek, Juho Kannala
Date: 2018-08-15 00:00:00
Tasks: Camera Relocalization, regression

Image-based camera relocalization is an important problem in computer vision
and robotics. Recent works utilize convolutional neural networks (CNNs) to
regress for pixels in a query image their corresponding 3D world coordinates in
the scene. The final pose is then solved via a RANSAC-based optimization scheme
using the predicted coordinates. Usually, the CNN is trained with ground truth
scene coordinates, but it has also been shown that the network can discover 3D
scene geometry automatically by minimizing single-view reprojection loss.
However, due to the deficiencies of the reprojection loss, the network needs to
be carefully initialized. In this paper, we present a new angle-based
reprojection loss, which resolves the issues of the original reprojection loss.
With this new loss function, the network can be trained without careful
initialization, and the system achieves more accurate results. The new loss
also enables us to utilize available multi-view constraints, which further
improve performance.
Pairwise Relational Networks for Face Recognition
Authors: Bong-Nam Kang, Yonghyun Kim, Daijin Kim
Date: 2018-08-15 00:00:00
Proceeding: ECCV 2018 9
Tasks: Face Identification, Face Recognition, Face Verification

Existing face recognition using deep neural networks is difficult to know
what kind of features are used to discriminate the identities of face images
clearly. To investigate the effective features for face recognition, we propose
a novel face recognition method, called a pairwise relational network (PRN),
that obtains local appearance patches around landmark points on the feature
map, and captures the pairwise relation between a pair of local appearance
patches. The PRN is trained to capture unique and discriminative pairwise
relations among different identities. Because the existence and meaning of
pairwise relations should be identity dependent, we add a face identity state
feature, which obtains from the long short-term memory (LSTM) units network
with the sequential local appearance patches on the feature maps, to the PRN.
To further improve accuracy of face recognition, we combined the global
appearance representation with the pairwise relational feature. Experimental
results on the LFW show that the PRN using only pairwise relations achieved
99.65% accuracy and the PRN using both pairwise relations and face identity
state feature achieved 99.76% accuracy. On the YTF, both the PRN using only
pairwise relations and the PRN using pairwise relations and the face identity
state feature achieved the state-of-the-art (95.7% and 96.3%). The PRN also
achieved comparable results to the state-of-the-art for both face verification
and face identification tasks on the IJB-A, and the state-of-the-art on the
IJB-B.
SAN: Learning Relationship between Convolutional Features for Multi-Scale Object Detection
Authors: Yonghyun Kim, Bong-Nam Kang, Daijin Kim
Date: 2018-08-15 00:00:00
Proceeding: ECCV 2018 9
Tasks: object-detection, Object Detection

Most of the recent successful methods in accurate object detection build on
the convolutional neural networks (CNN). However, due to the lack of scale
normalization in CNN-based detection methods, the activated channels in the
feature space can be completely different according to a scale and this
difference makes it hard for the classifier to learn samples. We propose a
Scale Aware Network (SAN) that maps the convolutional features from the
different scales onto a scale-invariant subspace to make CNN-based detection
methods more robust to the scale variation, and also construct a unique
learning method which considers purely the relationship between channels
without the spatial information for the efficient learning of SAN. To show the
validity of our method, we visualize how convolutional features change
according to the scale through a channel activation matrix and experimentally
show that SAN reduces the feature differences in the scale space. We evaluate
our method on VOC PASCAL and MS COCO dataset. We demonstrate SAN by conducting
several experiments on structures and parameters. The proposed SAN can be
generally applied to many CNN-based detection methods to enhance the detection
accuracy with a slight increase in the computing time.
Multiple Character Embeddings for Chinese Word Segmentation
Authors: Jingkang Wang, Jianing Zhou, Jie zhou, Gongshen Liu
Date: 2018-08-15 00:00:00
Proceeding: ACL 2019 7
Tasks: Chinese Word Segmentation

Chinese word segmentation (CWS) is often regarded as a character-based sequence labeling task in most current works which have achieved great success with the help of powerful neural networks. However, these works neglect an important clue: Chinese characters incorporate both semantic and phonetic meanings. In this paper, we introduce multiple character embeddings including Pinyin Romanization and Wubi Input, both of which are easily accessible and effective in depicting semantics of characters. We propose a novel shared Bi-LSTM-CRF model to fuse linguistic features efficiently by sharing the LSTM network during the training procedure. Extensive experiments on five corpora show that extra embeddings help obtain a significant improvement in labeling accuracy. Specifically, we achieve the state-of-the-art performance in AS and CityU corpora with F1 scores of 96.9 and 97.3, respectively without leveraging any external lexical resources.
Putting the Horse Before the Cart:A Generator-Evaluator Framework for Question Generation from Text
Authors: Vishwajeet Kumar, Ganesh Ramakrishnan, Yuan-Fang Li
Date: 2018-08-15 00:00:00
Tasks: Question Generation, Question-Generation

Automatic question generation (QG) is a useful yet challenging task in NLP. Recent neural network-based approaches represent the state-of-the-art in this task. In this work, we attempt to strengthen them significantly by adopting a holistic and novel generator-evaluator framework that directly optimizes objectives that reward semantics and structure. The {\it generator} is a sequence-to-sequence model that incorporates the {\it structure} and {\it semantics} of the question being generated. The generator predicts an answer in the passage that the question can pivot on. Employing the copy and coverage mechanisms, it also acknowledges other contextually important (and possibly rare) keywords in the passage that the question needs to conform to, while not redundantly repeating words. The {\it evaluator} model evaluates and assigns a reward to each predicted question based on its conformity to the {\it structure} of ground-truth questions. We propose two novel QG-specific reward functions for text conformity and answer conformity of the generated question. The evaluator also employs structure-sensitive rewards based on evaluation measures such as BLEU, GLEU, and ROUGE-L, which are suitable for QG. In contrast, most of the previous works only optimize the cross-entropy loss, which can induce inconsistencies between training (objective) and testing (evaluation) measures. Our evaluation shows that our approach significantly outperforms state-of-the-art systems on the widely-used SQuAD benchmark as per both automatic and human evaluation.
PFCNN: Convolutional Neural Networks on 3D Surfaces Using Parallel Frames
Authors: Yu-Qi Yang, Shilin Liu, Hao Pan, Yang Liu, Xin Tong
Date: 2018-08-15 00:00:00
Proceeding: CVPR 2020 6
Tasks: Scene Segmentation, Segmentation, Semantic Segmentation

Surface meshes are widely used shape representations and capture finer geometry data than point clouds or volumetric grids, but are challenging to apply CNNs directly due to their non-Euclidean structure. We use parallel frames on surface to define PFCNNs that enable effective feature learning on surface meshes by mimicking standard convolutions faithfully. In particular, the convolution of PFCNN not only maps local surface patches onto flat tangent planes, but also aligns the tangent planes such that they locally form a flat Euclidean structure, thus enabling recovery of standard convolutions. The alignment is achieved by the tool of locally flat connections borrowed from discrete differential geometry, which can be efficiently encoded and computed by parallel frame fields. In addition, the lack of canonical axis on surface is handled by sampling with the frame directions. Experiments show that for tasks including classification, segmentation and registration on deformable geometric domains, as well as semantic scene segmentation on rigid domains, PFCNNs achieve robust and superior performances without using sophisticated input features than state-of-the-art surface based CNNs.