pwc-archive/papers-with-abstracts huggingface dataset:
Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations
Authors: Jiwoon Ahn, Sunghyun Cho, Suha Kwak
Date: 2019-04-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: image-classification, Image Classification, Image-level Supervised Instance Segmentation, Instance Segmentation, Semantic Segmentation, Weakly-supervised Learning, Weakly-Supervised Semantic Segmentation

This paper presents a novel approach for learning instance segmentation with image-level class labels as supervision. Our approach generates pseudo instance segmentation labels of training images, which are used to train a fully supervised model. For generating the pseudo labels, we first identify confident seed areas of object classes from attention maps of an image classification model, and propagate them to discover the entire instance areas with accurate boundaries. To this end, we propose IRNet, which estimates rough areas of individual instances and detects boundaries between different object classes. It thus enables to assign instance labels to the seeds and to propagate them within the boundaries so that the entire areas of instances can be estimated accurately. Furthermore, IRNet is trained with inter-pixel relations on the attention maps, thus no extra supervision is required. Our method with IRNet achieves an outstanding performance on the PASCAL VOC 2012 dataset, surpassing not only previous state-of-the-art trained with the same level of supervision, but also some of previous models relying on stronger supervision.
ThumbNet: One Thumbnail Image Contains All You Need for Recognition
Authors: Chen Zhao, Bernard Ghanem
Date: 2019-04-10 00:00:00
Tasks: All

Although deep convolutional neural networks (CNNs) have achieved great success in computer vision tasks, its real-world application is still impeded by its voracious demand of computational resources. Current works mostly seek to compress the network by reducing its parameters or parameter-incurred computation, neglecting the influence of the input image on the system complexity. Based on the fact that input images of a CNN contain substantial redundancy, in this paper, we propose a unified framework, dubbed as ThumbNet, to simultaneously accelerate and compress CNN models by enabling them to infer on one thumbnail image. We provide three effective strategies to train ThumbNet. In doing so, ThumbNet learns an inference network that performs equally well on small images as the original-input network on large images. With ThumbNet, not only do we obtain the thumbnail-input inference network that can drastically reduce computation and memory requirements, but also we obtain an image downscaler that can generate thumbnail images for generic classification tasks. Extensive experiments show the effectiveness of ThumbNet, and demonstrate that the thumbnail-input inference network learned by ThumbNet can adequately retain the accuracy of the original-input network even when the input images are downscaled 16 times.
Better Word Embeddings by Disentangling Contextual n-Gram Information
Authors: Prakhar Gupta, Matteo Pagliardini, Martin Jaggi
Date: 2019-04-10 00:00:00
Proceeding: NAACL 2019 6
Tasks: Word Embeddings

Pre-trained word vectors are ubiquitous in Natural Language Processing
applications. In this paper, we show how training word embeddings jointly with
bigram and even trigram embeddings, results in improved unigram embeddings. We
claim that training word embeddings along with higher n-gram embeddings helps
in the removal of the contextual information from the unigrams, resulting in
better stand-alone word embeddings. We empirically show the validity of our
hypothesis by outperforming other competing word representation models by a
significant margin on a wide variety of tasks. We make our models publicly
available.
Classification of Two-channel Signals by Means of Genetic Programming
Authors: Daniel Rivero, Enrique Fernandez-Blanco, Julian Dorado, Alejandro Pazos
Date: 2019-04-10 00:00:00
Tasks: Classification, EEG, Electroencephalogram (EEG), General Classification, Vocal Bursts Valence Prediction

Traditionally, signal classification is a process in which previous knowledge
of the signals is needed. Human experts decide which features are extracted
from the signals, and used as inputs to the classification system. This
requirement can make significant unknown information of the signal be missed by
the experts and not be included in the features. This paper proposes a new
method that automatically analyses the signals and extracts the features
without any human participation. Therefore, there is no need for previous
knowledge about the signals to be classified. The proposed method is based on
Genetic Programming and, in order to test this method, it has been applied to a
well-known EEG database related to epilepsy, a disease suffered by millions of
people. As the results section shows, high accuracies in classification are
obtained
DSNet: An Efficient CNN for Road Scene Segmentation
Authors: Ping-Rong Chen, Hsueh-Ming Hang, Sheng-Wei Chan, Jing-Jhih Lin
Date: 2019-04-10 00:00:00
Tasks: Autonomous Driving, GPU, road scene understanding, Scene Segmentation, Scene Understanding

Road scene understanding is a critical component in an autonomous driving
system. Although the deep learning-based road scene segmentation can achieve
very high accuracy, its complexity is also very high for developing real-time
applications. It is challenging to design a neural net with high accuracy and
low computational complexity. To address this issue, we investigate the
advantages and disadvantages of several popular CNN architectures in terms of
speed, storage and segmentation accuracy. We start from the Fully Convolutional
Network (FCN) with VGG, and then we study ResNet and DenseNet. Through detailed
experiments, we pick up the favorable components from the existing
architectures and at the end, we construct a light-weight network architecture
based on the DenseNet. Our proposed network, called DSNet, demonstrates a
real-time testing (inferencing) ability (on the popular GPU platform) and it
maintains an accuracy comparable with most previous systems. We test our system
on several datasets including the challenging Cityscapes dataset (resolution of
1024x512) with an mIoU of about 69.1 % and runtime of 0.0147 second per image
on a single GTX 1080Ti. We also design a more accurate model but at the price
of a slower speed, which has an mIoU of about 72.6 % on the CamVid dataset.
Imitating Targets from all sides: An Unsupervised Transfer Learning method for Person Re-identification
Authors: Jiajie Tian, Zhu Teng, Rui Li, Yan Li, Baopeng Zhang, Jianping Fan
Date: 2019-04-10 00:00:00
Tasks: All, Person Re-Identification, Transfer Learning

Person re-identification (Re-ID) models usually show a limited performance when they are trained on one dataset and tested on another dataset due to the inter-dataset bias (e.g. completely different identities and backgrounds) and the intra-dataset difference (e.g. camera invariance). In terms of this issue, given a labelled source training set and an unlabelled target training set, we propose an unsupervised transfer learning method characterized by 1) bridging inter-dataset bias and intra-dataset difference via a proposed ImitateModel simultaneously; 2) regarding the unsupervised person Re-ID problem as a semi-supervised learning problem formulated by a dual classification loss to learn a discriminative representation across domains; 3) exploiting the underlying commonality across different domains from the class-style space to improve the generalization ability of re-ID models. Extensive experiments are conducted on two widely employed benchmarks, including Market-1501 and DukeMTMC-reID, and experimental results demonstrate that the proposed method can achieve a competitive performance against other state-of-the-art unsupervised Re-ID approaches.
SOSNet: Second Order Similarity Regularization for Local Descriptor Learning
Authors: Yurun Tian, Xin Yu, Bin Fan, Fuchao Wu, Huub Heijnen, Vassileios Balntas
Date: 2019-04-10 00:00:00
Proceeding: CVPR 2019 6
Tasks: Clustering, Graph Matching, Retrieval

Despite the fact that Second Order Similarity (SOS) has been used with significant success in tasks such as graph matching and clustering, it has not been exploited for learning local descriptors. In this work, we explore the potential of SOS in the field of descriptor learning by building upon the intuition that a positive pair of matching points should exhibit similar distances with respect to other points in the embedding space. Thus, we propose a novel regularization term, named Second Order Similarity Regularization (SOSR), that follows this principle. By incorporating SOSR into training, our learned descriptor achieves state-of-the-art performance on several challenging benchmarks containing distinct tasks ranging from local patch retrieval to structure from motion. Furthermore, by designing a von Mises-Fischer distribution based evaluation method, we link the utilization of the descriptor space to the matching performance, thus demonstrating the effectiveness of our proposed SOSR. Extensive experimental results, empirical evidence, and in-depth analysis are provided, indicating that SOSR can significantly boost the matching performance of the learned descriptor.
An Interactive Musical Prediction System with Mixture Density Recurrent Neural Networks
Authors: Charles P. Martin, Jim Torresen
Date: 2019-04-10 00:00:00

This paper is about creating digital musical instruments where a predictive
neural network model is integrated into the interactive system. Rather than
predicting symbolic music (e.g., MIDI notes), we suggest that predicting future
control data from the user and precise temporal information can lead to new and
interesting interactive possibilities. We propose that a mixture density
recurrent neural network (MDRNN) is an appropriate model for this task. The
predictions can be used to fill-in control data when the user stops performing,
or as a kind of filter on the user's own input. We present an interactive MDRNN
prediction server that allows rapid prototyping of new NIMEs featuring
predictive musical interaction by recording datasets, training MDRNN models,
and experimenting with interaction modes. We illustrate our system with several
example NIMEs applying this idea. Our evaluation shows that real-time
predictive interaction is viable even on single-board computers and that small
models are appropriate for small datasets.
Efficient Retrieval of Logos Using Rough Set Reducts
Authors: Ushasi Chaudhuri, Partha Bhowmick, Jayanta Mukhopadhyay
Date: 2019-04-10 00:00:00
Tasks: Retrieval

Searching for similar logos in the registered logo database is a very
important and tedious task at the trademark office. Speed and accuracy are two
aspects that one must attend to while developing a system for retrieval of
logos. In this paper, we propose a rough-set based method to quantify the
structural information in a logo image that can be used to efficiently index an
image. A logo is split into a number of polygons, and for each polygon, we
compute the tight upper and lower approximations based on the principles of a
rough set. This representation is used for forming feature vectors for
retrieval of logos. Experimentation on a standard data set shows the usefulness
of the proposed technique. It is computationally efficient and also provides
retrieval results at high accuracy.
Person Re-identification with Metric Learning using Privileged Information
Authors: Xun Yang, Meng Wang, DaCheng Tao
Date: 2019-04-10 00:00:00
Tasks: Decision Making, Metric Learning, Person Re-Identification

Despite the promising progress made in recent years, person re-identification
remains a challenging task due to complex variations in human appearances from
different camera views. This paper presents a logistic discriminant metric
learning method for this challenging problem. Different with most existing
metric learning algorithms, it exploits both original data and auxiliary data
during training, which is motivated by the new machine learning paradigm -
Learning Using Privileged Information. Such privileged information is a kind of
auxiliary knowledge which is only available during training. Our goal is to
learn an optimal distance function by constructing a locally adaptive decision
rule with the help of privileged information. We jointly learn two distance
metrics by minimizing the empirical loss penalizing the difference between the
distance in the original space and that in the privileged space. In our
setting, the distance in the privileged space functions as a local decision
threshold, which guides the decision making in the original space like a
teacher. The metric learned from the original space is used to compute the
distance between a probe image and a gallery image during testing. In addition,
we extend the proposed approach to a multi-view setting which is able to
explore the complementation of multiple feature representations. In the
multi-view setting, multiple metrics corresponding to different original
features are jointly learned, guided by the same privileged information.
Besides, an effective iterative optimization scheme is introduced to
simultaneously optimize the metrics and the assigned metric weights. Experiment
results on several widely-used datasets demonstrate that the proposed approach
is superior to global decision threshold based methods and outperforms most
state-of-the-art results.
Semi-Supervised Graph Classification: A Hierarchical Graph Perspective
Authors: Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, Junzhou Huang
Date: 2019-04-10 00:00:00
Tasks: Classification, General Classification, Graph Classification, Graph Embedding, Graph Learning, Node Classification

Node classification and graph classification are two graph learning problems
that predict the class label of a node and the class label of a graph
respectively. A node of a graph usually represents a real-world entity, e.g., a
user in a social network, or a protein in a protein-protein interaction
network. In this work, we consider a more challenging but practically useful
setting, in which a node itself is a graph instance. This leads to a
hierarchical graph perspective which arises in many domains such as social
network, biological network and document collection. For example, in a social
network, a group of people with shared interests forms a user group, whereas a
number of user groups are interconnected via interactions or common members. We
study the node classification problem in the hierarchical graph where a `node'
is a graph instance, e.g., a user group in the above example. As labels are
usually limited in real-world data, we design two novel semi-supervised
solutions named \underline{SE}mi-supervised gr\underline{A}ph
c\underline{L}assification via \underline{C}autious/\underline{A}ctive
\underline{I}teration (or SEAL-C/AI in short). SEAL-C/AI adopt an iterative
framework that takes turns to build or update two classifiers, one working at
the graph instance level and the other at the hierarchical graph level. To
simplify the representation of the hierarchical graph, we propose a novel
supervised, self-attentive graph embedding method called SAGE, which embeds
graph instances of arbitrary size into fixed-length vectors. Through
experiments on synthetic data and Tencent QQ group data, we demonstrate that
SEAL-C/AI not only outperform competing methods by a significant margin in
terms of accuracy/Macro-F1, but also generate meaningful interpretations of the
learned representations.
Depth from Videos in the Wild: Unsupervised Monocular Depth Learning from Unknown Cameras
Authors: Ariel Gordon, Hanhan Li, Rico Jonschkowski, Anelia Angelova
Date: 2019-04-10 00:00:00
Proceeding: ICCV 2019 10
Tasks: Depth Estimation, Depth Prediction, Monocular Depth Estimation, Unsupervised Monocular Depth Estimation

We present a novel method for simultaneous learning of depth, egomotion,
object motion, and camera intrinsics from monocular videos, using only
consistency across neighboring video frames as supervision signal. Similarly to
prior work, our method learns by applying differentiable warping to frames and
comparing the result to adjacent ones, but it provides several improvements: We
address occlusions geometrically and differentiably, directly using the depth
maps as predicted during training. We introduce randomized layer normalization,
a novel powerful regularizer, and we account for object motion relative to the
scene. To the best of our knowledge, our work is the first to learn the camera
intrinsic parameters, including lens distortion, from video in an unsupervised
manner, thereby allowing us to extract accurate depth and motion from arbitrary
videos of unknown origin at scale. We evaluate our results on the Cityscapes,
KITTI and EuRoC datasets, establishing new state of the art on depth prediction
and odometry, and demonstrate qualitatively that depth prediction can be
learned from a collection of YouTube videos.
AMRec: An Intelligent System for Academic Method Recommendation
Authors: Shanshan Huang, Xiaojun Wan, Xuewei Tang
Date: 2019-04-10 00:00:00

Finding new academic Methods for research problems is the key task in a
researcher's research career. It is usually very difficult for new researchers
to find good Methods for their research problems since they lack of research
experiences. In order to help researchers carry out their researches in a more
convenient way, we describe a novel recommendation system called AMRec to
recommend new academic Methods for research problems in this paper. Our
proposed system first extracts academic concepts (Tasks and Methods) and their
relations from academic literatures, and then leverages the regularized matrix
factorization Method for academic Method recommendation. Preliminary evaluation
results verify the effectiveness of our proposed system.
Discovering patterns of online popularity from time series
Authors: Mert Ozer, Anna Sapienza, Andrés Abeliuk, Goran Muric, Emilio Ferrara
Date: 2019-04-10 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis, Time Series Clustering

How is popularity gained online? Is being successful strictly related to
rapidly becoming viral in an online platform or is it possible to acquire
popularity in a steady and disciplined fashion? What are other temporal
characteristics that can unveil the popularity of online content? To answer
these questions, we leverage a multi-faceted temporal analysis of the evolution
of popular online contents. Here, we present dipm-SC: a multi-dimensional
shape-based time-series clustering algorithm with a heuristic to find the
optimal number of clusters. First, we validate the accuracy of our algorithm on
synthetic datasets generated from benchmark time series models. Second, we show
that dipm-SC can uncover meaningful clusters of popularity behaviors in a
real-world Twitter dataset. By clustering the multidimensional time-series of
the popularity of contents coupled with other domain-specific dimensions, we
uncover two main patterns of popularity: bursty and steady temporal behaviors.
Moreover, we find that the way popularity is gained over time has no
significant impact on the final cumulative popularity.
Spatiotemporal Knowledge Distillation for Efficient Estimation of Aerial Video Saliency
Authors: Jia Li, Kui Fu, Shengwei Zhao, Shiming Ge
Date: 2019-04-10 00:00:00
Tasks: GPU, Knowledge Distillation, Saliency Prediction

The performance of video saliency estimation techniques has achieved
significant advances along with the rapid development of Convolutional Neural
Networks (CNNs). However, devices like cameras and drones may have limited
computational capability and storage space so that the direct deployment of
complex deep saliency models becomes infeasible. To address this problem, this
paper proposes a dynamic saliency estimation approach for aerial videos via
spatiotemporal knowledge distillation. In this approach, five components are
involved, including two teachers, two students and the desired spatiotemporal
model. The knowledge of spatial and temporal saliency is first separately
transferred from the two complex and redundant teachers to their simple and
compact students, and the input scenes are also degraded from high-resolution
to low-resolution to remove the probable data redundancy so as to greatly speed
up the feature extraction process. After that, the desired spatiotemporal model
is further trained by distilling and encoding the spatial and temporal saliency
knowledge of two students into a unified network. In this manner, the
inter-model redundancy can be further removed for the effective estimation of
dynamic saliency on aerial videos. Experimental results show that the proposed
approach outperforms ten state-of-the-art models in estimating visual saliency
on aerial videos, while its speed reaches up to 28,738 FPS on the GPU platform.
Identifying Sub-Phenotypes of Acute Kidney Injury using Structured and Unstructured Electronic Health Record Data with Memory Networks
Authors: Zhen-Xing Xu, Jingyuan Chou, Xi Sheryl Zhang, Yuan Luo, Tamara Isakova, Prakash Adekkanattu, Jessica S. Ancker, Guoqian Jiang, Richard C. Kiefer, Jennifer A. Pacheco, Luke V. Rasmussen, Jyotishman Pathak, Fei Wang
Date: 2019-04-10 00:00:00

Acute Kidney Injury (AKI) is a common clinical syndrome characterized by the rapid loss of kidney excretory function, which aggravates the clinical severity of other diseases in a large number of hospitalized patients. Accurate early prediction of AKI can enable in-time interventions and treatments. However, AKI is highly heterogeneous, thus identification of AKI sub-phenotypes can lead to an improved understanding of the disease pathophysiology and development of more targeted clinical interventions. This study used a memory network-based deep learning approach to discover AKI sub-phenotypes using structured and unstructured electronic health record (EHR) data of patients before AKI diagnosis. We leveraged a real world critical care EHR corpus including 37,486 ICU stays. Our approach identified three distinct sub-phenotypes: sub-phenotype I is with an average age of 63.03$ \pm 17.25 $ years, and is characterized by mild loss of kidney excretory function (Serum Creatinine (SCr) $1.55\pm 0.34$ mg/dL, estimated Glomerular Filtration Rate Test (eGFR) $107.65\pm 54.98$ mL/min/1.73$m^2$). These patients are more likely to develop stage I AKI. Sub-phenotype II is with average age 66.81$ \pm 10.43 $ years, and was characterized by severe loss of kidney excretory function (SCr $1.96\pm 0.49$ mg/dL, eGFR $82.19\pm 55.92$ mL/min/1.73$m^2$). These patients are more likely to develop stage III AKI. Sub-phenotype III is with average age 65.07$ \pm 11.32 $ years, and was characterized moderate loss of kidney excretory function and thus more likely to develop stage II AKI (SCr $1.69\pm 0.32$ mg/dL, eGFR $93.97\pm 56.53$ mL/min/1.73$m^2$). Both SCr and eGFR are significantly different across the three sub-phenotypes with statistical testing plus postdoc analysis, and the conclusion still holds after age adjustment.