pwc-archive/papers-with-abstracts huggingface dataset:
Learning non-parametric Markov networks with mutual information
Authors: Janne Leppä-aho, Santeri Räisänen, Xiao Yang, Teemu Roos
Date: 2017-08-08 00:00:00

We propose a method for learning Markov network structures for continuous
data without invoking any assumptions about the distribution of the variables.
The method makes use of previous work on a non-parametric estimator for mutual
information which is used to create a non-parametric test for multivariate
conditional independence. This independence test is then combined with an
efficient constraint-based algorithm for learning the graph structure. The
performance of the method is evaluated on several synthetic data sets and it is
shown to learn considerably more accurate structures than competing methods
when the dependencies between the variables involve non-linearities.
Binary Generative Adversarial Networks for Image Retrieval
Authors: Jingkuan Song
Date: 2017-08-08 00:00:00
Tasks: Deep Hashing, Image Retrieval, Retrieval

The most striking successes in image retrieval using deep hashing have mostly
involved discriminative models, which require labels. In this paper, we use
binary generative adversarial networks (BGAN) to embed images to binary codes
in an unsupervised way. By restricting the input noise variable of generative
adversarial networks (GAN) to be binary and conditioned on the features of each
input image, BGAN can simultaneously learn a binary representation per image,
and generate an image plausibly similar to the original one. In the proposed
framework, we address two main problems: 1) how to directly generate binary
codes without relaxation? 2) how to equip the binary representation with the
ability of accurate image retrieval? We resolve these problems by proposing new
sign-activation strategy and a loss function steering the learning process,
which consists of new models for adversarial loss, a content loss, and a
neighborhood structure loss. Experimental results on standard datasets
(CIFAR-10, NUSWIDE, and Flickr) demonstrate that our BGAN significantly
outperforms existing hashing methods by up to 107\% in terms of~mAP (See Table
tab.res.map.comp) Our anonymous code is available at:
https://github.com/htconquer/BGAN.
Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning
Authors: Thomas M. Moerland, Joost Broekens, Catholijn M. Jonker
Date: 2017-05-01 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Variational Inference

In this paper we study how to learn stochastic, multimodal transition
dynamics in reinforcement learning (RL) tasks. We focus on evaluating
transition function estimation, while we defer planning over this model to
future work. Stochasticity is a fundamental property of many task environments.
However, discriminative function approximators have difficulty estimating
multimodal stochasticity. In contrast, deep generative models do capture
complex high-dimensional outcome distributions. First we discuss why, amongst
such models, conditional variational inference (VI) is theoretically most
appealing for model-based RL. Subsequently, we compare different VI models on
their ability to learn complex stochasticity on simulated functions, as well as
on a typical RL gridworld with multimodal dynamics. Results show VI
successfully predicts multimodal outcomes, but also robustly ignores these for
deterministic parts of the transition dynamics. In summary, we show a robust
method to learn multimodal transitions using function approximation, which is a
key preliminary for model-based RL in stochastic domains.
An Unsupervised Game-Theoretic Approach to Saliency Detection
Authors: Yu Zeng, Huchuan Lu, Ali Borji, Mengyang Feng
Date: 2017-08-08 00:00:00
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection

We propose a novel unsupervised game-theoretic salient object detection
algorithm that does not require labeled training data. First, saliency
detection problem is formulated as a non-cooperative game, hereinafter referred
to as Saliency Game, in which image regions are players who choose to be
"background" or "foreground" as their pure strategies. A payoff function is
constructed by exploiting multiple cues and combining complementary features.
Saliency maps are generated according to each region's strategy in the Nash
equilibrium of the proposed Saliency Game. Second, we explore the complementary
relationship between color and deep features and propose an Iterative Random
Walk algorithm to combine saliency maps produced by the Saliency Game using
different features. Iterative random walk allows sharing information across
feature spaces, and detecting objects that are otherwise very hard to detect.
Extensive experiments over 6 challenging datasets demonstrate the superiority
of our proposed unsupervised algorithm compared to several state of the art
supervised algorithms.
Multiscale Strategies for Computing Optimal Transport
Authors: Samuel Gerber, Mauro Maggioni
Date: 2017-08-08 00:00:00
Tasks: Anatomy

This paper presents a multiscale approach to efficiently compute approximate
optimal transport plans between point sets. It is particularly well-suited for
point sets that are in high-dimensions, but are close to being intrinsically
low-dimensional. The approach is based on an adaptive multiscale decomposition
of the point sets. The multiscale decomposition yields a sequence of optimal
transport problems, that are solved in a top-to-bottom fashion from the
coarsest to the finest scale. We provide numerical evidence that this
multiscale approach scales approximately linearly, in time and memory, in the
number of nodes, instead of quadratically or worse for a direct solution.
Empirically, the multiscale approach results in less than one percent relative
error in the objective function. Furthermore, the multiscale plans constructed
are of interest by themselves as they may be used to introduce novel features
and notions of distances between point sets. An analysis of sets of brain MRI
based on optimal transport distances illustrates the effectiveness of the
proposed method on a real world data set. The application demonstrates that
multiscale optimal transport distances have the potential to improve on
state-of-the-art metrics currently used in computational anatomy.
Weakly Supervised Image Annotation and Segmentation with Objects and Attributes
Authors: Zhiyuan Shi, Yongxin Yang, Timothy M. Hospedales, Tao Xiang
Date: 2017-08-08 00:00:00
Tasks: Attribute, Object, object-detection, Object Detection, Retrieval, Semantic Segmentation, Superpixels

We propose to model complex visual scenes using a non-parametric Bayesian
model learned from weakly labelled images abundant on media sharing sites such
as Flickr. Given weak image-level annotations of objects and attributes without
locations or associations between them, our model aims to learn the appearance
of object and attribute classes as well as their association on each object
instance. Once learned, given an image, our model can be deployed to tackle a
number of vision problems in a joint and coherent manner, including recognising
objects in the scene (automatic object annotation), describing objects using
their attributes (attribute prediction and association), and localising and
delineating the objects (object detection and semantic segmentation). This is
achieved by developing a novel Weakly Supervised Markov Random Field Stacked
Indian Buffet Process (WS-MRF-SIBP) that models objects and attributes as
latent factors and explicitly captures their correlations within and across
superpixels. Extensive experiments on benchmark datasets demonstrate that our
weakly supervised model significantly outperforms weakly supervised
alternatives and is often comparable with existing strongly supervised models
on a variety of tasks including semantic segmentation, automatic image
annotation and retrieval based on object-attribute associations.
Learning and Refining of Privileged Information-based RNNs for Action Recognition from Depth Sequences
Authors: Zhiyuan Shi, Tae-Kyun Kim
Date: 2017-03-28 00:00:00
Proceeding: CVPR 2017 7
Tasks: Action Recognition, General Classification, Temporal Action Localization

Existing RNN-based approaches for action recognition from depth sequences
require either skeleton joints or hand-crafted depth features as inputs. An
end-to-end manner, mapping from raw depth maps to action classes, is
non-trivial to design due to the fact that: 1) single channel map lacks texture
thus weakens the discriminative power; 2) relatively small set of depth
training data. To address these challenges, we propose to learn an RNN driven
by privileged information (PI) in three-steps: An encoder is pre-trained to
learn a joint embedding of depth appearance and PI (i.e. skeleton joints). The
learned embedding layers are then tuned in the learning step, aiming to
optimize the network by exploiting PI in a form of multi-task loss. However,
exploiting PI as a secondary task provides little help to improve the
performance of a primary task (i.e. classification) due to the gap between
them. Finally, a bridging matrix is defined to connect two tasks by discovering
latent PI in the refining step. Our PI-based classification loss maintains a
consistency between latent PI and predicted distribution. The latent PI and
network are iteratively estimated and updated in an expectation-maximization
procedure. The proposed learning process provides greater discriminative power
to model subtle depth difference, while helping avoid overfitting the scarcer
training data. Our experiments show significant performance gains over
state-of-the-art methods on three public benchmark datasets and our newly
collected Blanket dataset.
An Effective Feature Selection Method Based on Pair-Wise Feature Proximity for High Dimensional Low Sample Size Data
Authors: S. L. Happy, Ramanarayan Mohanty, Aurobinda Routray
Date: 2017-08-08 00:00:00
Tasks: feature selection

Feature selection has been studied widely in the literature. However, the
efficacy of the selection criteria for low sample size applications is
neglected in most cases. Most of the existing feature selection criteria are
based on the sample similarity. However, the distance measures become
insignificant for high dimensional low sample size (HDLSS) data. Moreover, the
variance of a feature with a few samples is pointless unless it represents the
data distribution efficiently. Instead of looking at the samples in groups, we
evaluate their efficiency based on pairwise fashion. In our investigation, we
noticed that considering a pair of samples at a time and selecting the features
that bring them closer or put them far away is a better choice for feature
selection. Experimental results on benchmark data sets demonstrate the
effectiveness of the proposed method with low sample size, which outperforms
many other state-of-the-art feature selection methods.
Prune the Convolutional Neural Networks with Sparse Shrink
Authors: Xin Li, Changsong Liu
Date: 2017-08-08 00:00:00

Nowadays, it is still difficult to adapt Convolutional Neural Network (CNN)
based models for deployment on embedded devices. The heavy computation and
large memory footprint of CNN models become the main burden in real
application. In this paper, we propose a "Sparse Shrink" algorithm to prune an
existing CNN model. By analyzing the importance of each channel via sparse
reconstruction, the algorithm is able to prune redundant feature maps
accordingly. The resulting pruned model thus directly saves computational
resource. We have evaluated our algorithm on CIFAR-100. As shown in our
experiments, we can reduce 56.77% parameters and 73.84% multiplication in total
with only minor decrease in accuracy. These results have demonstrated the
effectiveness of our "Sparse Shrink" algorithm.
Predicting Deeper into the Future of Semantic Segmentation
Authors: Pauline Luc, Natalia Neverova, Camille Couprie, Jakob Verbeek, Yann Lecun
Date: 2017-03-22 00:00:00
Proceeding: ICCV 2017 10
Tasks: Attribute, Autonomous Driving, Decision Making, Optical Flow Estimation, Scene Understanding, Semantic Segmentation, Video Prediction

The ability to predict and therefore to anticipate the future is an important
attribute of intelligence. It is also of utmost importance in real-time
systems, e.g. in robotics or autonomous driving, which depend on visual scene
understanding for decision making. While prediction of the raw RGB pixel values
in future video frames has been studied in previous work, here we introduce the
novel task of predicting semantic segmentations of future frames. Given a
sequence of video frames, our goal is to predict segmentation maps of not yet
observed video frames that lie up to a second or further in the future. We
develop an autoregressive convolutional neural network that learns to
iteratively generate multiple frames. Our results on the Cityscapes dataset
show that directly predicting future segmentations is substantially better than
predicting and then segmenting future RGB frames. Prediction results up to half
a second in the future are visually convincing and are much more accurate than
those of a baseline based on warping semantic segmentations using optical flow.
UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones
Authors: Daniela Micucci, Marco Mobilio, Paolo Napoletano
Date: 2016-11-23 00:00:00
Tasks: Activity Recognition, General Classification, Human Activity Recognition

Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are
being increasingly used to monitor human activities. Data acquired by the
hosted sensors are usually processed by machine-learning-based algorithms to
classify human activities. The success of those algorithms mostly depends on
the availability of training (labeled) data that, if made publicly available,
would allow researchers to make objective comparisons between techniques.
Nowadays, publicly available data sets are few, often contain samples from
subjects with too similar characteristics, and very often lack of specific
information so that is not possible to select subsets of samples according to
specific criteria. In this article, we present a new dataset of acceleration
samples acquired with an Android smartphone designed for human activity
recognition and fall detection. The dataset includes 11,771 samples of both
human activities and falls performed by 30 subjects of ages ranging from 18 to
60 years. Samples are divided in 17 fine grained classes grouped in two coarse
grained classes: one containing samples of 9 types of activities of daily
living (ADL) and the other containing samples of 8 types of falls. The dataset
has been stored to include all the information useful to select samples
according to different criteria, such as the type of ADL, the age, the gender,
and so on. Finally, the dataset has been benchmarked with four different
classifiers and with two different feature vectors. We evaluated four different
classification tasks: fall vs no fall, 9 activities, 8 falls, 17 activities and
falls. For each classification task we performed a subject-dependent and
independent evaluation. The major findings of the evaluation are the following:
i) it is more difficult to distinguish between types of falls than types of
activities; ii) subject-dependent evaluation outperforms the
subject-independent one
Visual descriptors for content-based retrieval of remote sensing images
Authors: Paolo Napoletano
Date: 2016-02-02 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

In this paper we present an extensive evaluation of visual descriptors for
the content-based retrieval of remote sensing (RS) images. The evaluation
includes global hand-crafted, local hand-crafted, and Convolutional Neural
Network (CNNs) features coupled with four different Content-Based Image
Retrieval schemes. We conducted all the experiments on two publicly available
datasets: the 21-class UC Merced Land Use/Land Cover (LandUse) dataset and
19-class High-resolution Satellite Scene dataset (SceneSat). The content of RS
images might be quite heterogeneous, ranging from images containing fine
grained textures, to coarse grained ones or to images containing objects. It is
therefore not obvious in this domain, which descriptor should be employed to
describe images having such a variability. Results demonstrate that CNN-based
features perform better than both global and and local hand-crafted features
whatever is the retrieval scheme adopted. Features extracted from SatResNet-50,
a residual CNN suitable fine-tuned on the RS domain, shows much better
performance than a residual CNN pre-trained on multimedia scene and object
images. Features extracted from NetVLAD, a CNN that considers both CNN and
local features, works better than others CNN solutions on those images that
contain fine-grained textures and objects.
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs
Authors: Martin Simonovsky, Nikos Komodakis
Date: 2017-04-10 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Object Classification, 3D Point Cloud Classification, Classification, General Classification, Graph Classification, Point Cloud Classification

A number of problems can be formulated as prediction on graph-structured
data. In this work, we generalize the convolution operator from regular grids
to arbitrary graphs while avoiding the spectral domain, which allows us to
handle graphs of varying size and connectivity. To move beyond a simple
diffusion, filter weights are conditioned on the specific edge labels in the
neighborhood of a vertex. Together with the proper choice of graph coarsening,
we explore constructing deep neural networks for graph classification. In
particular, we demonstrate the generality of our formulation in point cloud
classification, where we set the new state of the art, and on a graph
classification dataset, where we outperform other deep learning approaches. The
source code is available at https://github.com/mys007/ecc
FoveaNet: Perspective-aware Urban Scene Parsing
Authors: Xin Li, Zequn Jie, Wei Wang, Changsong Liu, Jimei Yang, Xiaohui Shen, Zhe Lin, Qiang Chen, Shuicheng Yan, Jiashi Feng
Date: 2017-08-08 00:00:00
Proceeding: ICCV 2017 10
Tasks: Scene Parsing

Parsing urban scene images benefits many applications, especially
self-driving. Most of the current solutions employ generic image parsing models
that treat all scales and locations in the images equally and do not consider
the geometry property of car-captured urban scene images. Thus, they suffer
from heterogeneous object scales caused by perspective projection of cameras on
actual scenes and inevitably encounter parsing failures on distant objects as
well as other boundary and recognition errors. In this work, we propose a new
FoveaNet model to fully exploit the perspective geometry of scene images and
address the common failures of generic parsing models. FoveaNet estimates the
perspective geometry of a scene image through a convolutional network which
integrates supportive evidence from contextual objects within the image. Based
on the perspective geometry information, FoveaNet "undoes" the camera
perspective projection analyzing regions in the space of the actual scene, and
thus provides much more reliable parsing results. Furthermore, to effectively
address the recognition errors, FoveaNet introduces a new dense CRFs model that
takes the perspective geometry as a prior potential. We evaluate FoveaNet on
two urban scene parsing datasets, Cityspaces and CamVid, which demonstrates
that FoveaNet can outperform all the well-established baselines and provide new
state-of-the-art performance.
Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN
Authors: Edison Marrese-Taylor, Jorge A. Balazs, Yutaka Matsuo
Date: 2017-08-08 00:00:00
Proceeding: WS 2017 9
Tasks: Aspect Extraction, Domain Adaptation, Opinion Mining, Sentiment Analysis, Sentiment Classification

Video reviews are the natural evolution of written product reviews. In this
paper we target this phenomenon and introduce the first dataset created from
closed captions of YouTube product review videos as well as a new attention-RNN
model for aspect extraction and joint aspect extraction and sentiment
classification. Our model provides state-of-the-art performance on aspect
extraction without requiring the usage of hand-crafted features on the SemEval
ABSA corpus, while it outperforms the baseline on the joint task. In our
dataset, the attention-RNN model outperforms the baseline for both tasks, but
we observe important performance drops for all models in comparison to SemEval.
These results, as well as further experiments on domain adaptation for aspect
extraction, suggest that differences between speech and written text, which
have been discussed extensively in the literature, also extend to the domain of
product reviews, where they are relevant for fine-grained opinion mining.
Deep Learning Markov Random Field for Semantic Segmentation
Authors: Ziwei Liu, Xiaoxiao Li, Ping Luo, Chen Change Loy, Xiaoou Tang
Date: 2016-06-23 00:00:00
Tasks: Deep Learning, Segmentation, Semantic Segmentation, Video Segmentation, Video Semantic Segmentation

Semantic segmentation tasks can be well modeled by Markov Random Field (MRF).
This paper addresses semantic segmentation by incorporating high-order
relations and mixture of label contexts into MRF. Unlike previous works that
optimized MRFs using iterative algorithm, we solve MRF by proposing a
Convolutional Neural Network (CNN), namely Deep Parsing Network (DPN), which
enables deterministic end-to-end computation in a single forward pass.
Specifically, DPN extends a contemporary CNN to model unary terms and
additional layers are devised to approximate the mean field (MF) algorithm for
pairwise terms. It has several appealing properties. First, different from the
recent works that required many iterations of MF during back-propagation, DPN
is able to achieve high performance by approximating one iteration of MF.
Second, DPN represents various types of pairwise terms, making many existing
models as its special cases. Furthermore, pairwise terms in DPN provide a
unified framework to encode rich contextual information in high-dimensional
data, such as images and videos. Third, DPN makes MF easier to be parallelized
and speeded up, thus enabling efficient inference. DPN is thoroughly evaluated
on standard semantic image/video segmentation benchmarks, where a single DPN
model yields state-of-the-art segmentation accuracies on PASCAL VOC 2012,
Cityscapes dataset and CamVid dataset.