pwc-archive/papers-with-abstracts huggingface dataset:
Decomposition into Low-rank plus Additive Matrices for Background/Foreground Separation: A Review for a Comparative Evaluation with a Large-Scale Dataset
Authors: Thierry Bouwmans, Andrews Sobral, Sajid Javed, Soon Ki Jung, El-Hadi Zahzah
Date: 2015-11-04 00:00:00
Tasks: Matrix Completion

Recent research on problem formulations based on decomposition into low-rank
plus sparse matrices shows a suitable framework to separate moving objects from
the background. The most representative problem formulation is the Robust
Principal Component Analysis (RPCA) solved via Principal Component Pursuit
(PCP) which decomposes a data matrix in a low-rank matrix and a sparse matrix.
However, similar robust implicit or explicit decompositions can be made in the
following problem formulations: Robust Non-negative Matrix Factorization
(RNMF), Robust Matrix Completion (RMC), Robust Subspace Recovery (RSR), Robust
Subspace Tracking (RST) and Robust Low-Rank Minimization (RLRM). The main goal
of these similar problem formulations is to obtain explicitly or implicitly a
decomposition into low-rank matrix plus additive matrices. In this context,
this work aims to initiate a rigorous and comprehensive review of the similar
problem formulations in robust subspace learning and tracking based on
decomposition into low-rank plus additive matrices for testing and ranking
existing algorithms for background/foreground separation. For this, we first
provide a preliminary review of the recent developments in the different
problem formulations which allows us to define a unified view that we called
Decomposition into Low-rank plus Additive Matrices (DLAM). Then, we examine
carefully each method in each robust subspace learning/tracking frameworks with
their decomposition, their loss functions, their optimization problem and their
solvers. Furthermore, we investigate if incremental algorithms and real-time
implementations can be achieved for background/foreground separation. Finally,
experimental results on a large-scale dataset called Background Models
Challenge (BMC 2012) show the comparative performance of 32 different robust
subspace learning/tracking methods.
Prediction of Video Popularity in the Absence of Reliable Data from Video Hosting Services: Utility of Traces Left by Users on the Web
Authors: Alexey Drutsa, Gleb Gusev, Pavel Serdyukov
Date: 2016-11-28 00:00:00
Tasks: Prediction

With the growth of user-generated content, we observe the constant rise of
the number of companies, such as search engines, content aggregators, etc.,
that operate with tremendous amounts of web content not being the services
hosting it. Thus, aiming to locate the most important content and promote it to
the users, they face the need of estimating the current and predicting the
future content popularity.
  In this paper, we approach the problem of video popularity prediction not
from the side of a video hosting service, as done in all previous studies, but
from the side of an operating company, which provides a popular video search
service that aggregates content from different video hosting websites. We
investigate video popularity prediction based on features from three primary
sources available for a typical operating company: first, the content hosting
provider may deliver its data via its API, second, the operating company makes
use of its own search and browsing logs, third, the company crawls information
about embeds of a video and links to a video page from publicly available
resources on the Web. We show that video popularity prediction based on the
embed and link data coupled with the internal search and browsing data
significantly improves video popularity prediction based only on the data
provided by the video hosting and can even adequately replace the API data in
the cases when it is partly or completely unavailable.
Social Scene Understanding: End-to-End Multi-Person Action Localization and Collective Activity Recognition
Authors: Timur Bagautdinov, Alexandre Alahi, François Fleuret, Pascal Fua, Silvio Savarese
Date: 2016-11-28 00:00:00
Proceeding: CVPR 2017 7
Tasks: Action Localization, Action Recognition, Activity Recognition, Scene Understanding

We present a unified framework for understanding human social behaviors in
raw image sequences. Our model jointly detects multiple individuals, infers
their social actions, and estimates the collective actions with a single
feed-forward pass through a neural network. We propose a single architecture
that does not rely on external detection algorithms but rather is trained
end-to-end to generate dense proposal maps that are refined via a novel
inference scheme. The temporal consistency is handled via a person-level
matching Recurrent Neural Network. The complete model takes as input a sequence
of frames and outputs detections along with the estimates of individual actions
and collective activities. We demonstrate state-of-the-art performance of our
algorithm on multiple publicly available benchmarks.
Bidirectional Multirate Reconstruction for Temporal Modeling in Videos
Authors: Linchao Zhu, Zhongwen Xu, Yi Yang
Date: 2016-11-28 00:00:00
Proceeding: CVPR 2017 7
Tasks: Event Detection, Video Captioning

Despite the recent success of neural networks in image feature learning, a
major problem in the video domain is the lack of sufficient labeled data for
learning to model temporal information. In this paper, we propose an
unsupervised temporal modeling method that learns from untrimmed videos. The
speed of motion varies constantly, e.g., a man may run quickly or slowly. We
therefore train a Multirate Visual Recurrent Model (MVRM) by encoding frames of
a clip with different intervals. This learning process makes the learned model
more capable of dealing with motion speed variance. Given a clip sampled from a
video, we use its past and future neighboring clips as the temporal context,
and reconstruct the two temporal transitions, i.e., present$\rightarrow$past
transition and present$\rightarrow$future transition, reflecting the temporal
information in different views. The proposed method exploits the two
transitions simultaneously by incorporating a bidirectional reconstruction
which consists of a backward reconstruction and a forward reconstruction. We
apply the proposed method to two challenging video tasks, i.e., complex event
detection and video captioning, in which it achieves state-of-the-art
performance. Notably, our method generates the best single feature for event
detection with a relative improvement of 10.4% on the MEDTest-13 dataset and
achieves the best performance in video captioning across all evaluation metrics
on the YouTube2Text dataset.
Deep, Dense, and Low-Rank Gaussian Conditional Random Fields
Authors: Siddhartha Chandra, Iasonas Kokkinos
Date: 2016-11-28 00:00:00
Tasks: GPU, Saliency Prediction, Segmentation, Semantic Segmentation

In this work we introduce a fully-connected graph structure in the Deep
Gaussian Conditional Random Field (G-CRF) model. For this we express the
pairwise interactions between pixels as the inner-products of low-dimensional
embeddings, delivered by a new subnetwork of a deep architecture. We
efficiently minimize the resulting energy by solving the resulting low-rank
linear system with conjugate gradients, and derive an analytic expression for
the gradient of our embeddings which allows us to train them end-to-end with
backpropagation.
  We demonstrate the merit of our approach by achieving state of the art
results on three challenging Computer Vision benchmarks, namely semantic
segmentation, human parts segmentation, and saliency estimation. Our
implementation is fully GPU based, built on top of the Caffe library, and will
be made publicly available.
Person Re-Identification by Unsupervised Video Matching
Authors: Xiaolong Ma, Xiatian Zhu, Shaogang Gong, Xudong Xie, Jianming Hu, Kin-Man Lam, Yisheng Zhong
Date: 2016-11-25 00:00:00
Tasks: Benchmarking, Dynamic Time Warping, Person Re-Identification, Time Series Analysis

Most existing person re-identification (ReID) methods rely only on the
spatial appearance information from either one or multiple person images,
whilst ignore the space-time cues readily available in video or image-sequence
data. Moreover, they often assume the availability of exhaustively labelled
cross-view pairwise data for every camera pair, making them non-scalable to
ReID applications in real-world large scale camera networks. In this work, we
introduce a novel video based person ReID method capable of accurately matching
people across views from arbitrary unaligned image-sequences without any
labelled pairwise data. Specifically, we introduce a new space-time person
representation by encoding multiple granularities of spatio-temporal dynamics
in form of time series. Moreover, a Time Shift Dynamic Time Warping (TS-DTW)
model is derived for performing automatically alignment whilst achieving data
selection and matching between inherently inaccurate and incomplete sequences
in a unified way. We further extend the TS-DTW model for accommodating multiple
feature-sequences of an image-sequence in order to fuse information from
different descriptions. Crucially, this model does not require pairwise
labelled training data (i.e. unsupervised) therefore readily scalable to large
scale camera networks of arbitrary camera pairs without the need for exhaustive
data annotation for every camera pair. We show the effectiveness and advantages
of the proposed method by extensive comparisons with related state-of-the-art
approaches using two benchmarking ReID datasets, PRID2011 and iLIDS-VID.
Analyzing Features for the Detection of Happy Endings in German Novels
Authors: Fotis Jannidis, Isabella Reger, Albin Zehe, Martin Becker, Lena Hettinger, Andreas Hotho
Date: 2016-11-28 00:00:00
Tasks: Sentiment Analysis

With regard to a computational representation of literary plot, this paper
looks at the use of sentiment analysis for happy ending detection in German
novels. Its focus lies on the investigation of previously proposed sentiment
features in order to gain insight about the relevance of specific features on
the one hand and the implications of their performance on the other hand.
Therefore, we study various partitionings of novels, considering the highly
variable concept of "ending". We also show that our approach, even though still
rather simple, can potentially lead to substantial findings relevant to
literary studies.
Multi-Modality Fusion based on Consensus-Voting and 3D Convolution for Isolated Gesture Recognition
Authors: Jiali Duan, Shuai Zhou, Jun Wan, Xiaoyuan Guo, Stan Z. Li
Date: 2016-11-21 00:00:00
Tasks: Gesture Recognition

Recently, the popularity of depth-sensors such as Kinect has made depth
videos easily available while its advantages have not been fully exploited.
This paper investigates, for gesture recognition, to explore the spatial and
temporal information complementarily embedded in RGB and depth sequences. We
propose a convolutional twostream consensus voting network (2SCVN) which
explicitly models both the short-term and long-term structure of the RGB
sequences. To alleviate distractions from background, a 3d depth-saliency
ConvNet stream (3DDSN) is aggregated in parallel to identify subtle motion
characteristics. These two components in an unified framework significantly
improve the recognition accuracy. On the challenging Chalearn IsoGD benchmark,
our proposed method outperforms the first place on the leader-board by a large
margin (10.29%) while also achieving the best result on RGBD-HuDaAct dataset
(96.74%). Both quantitative experiments and qualitative analysis shows the
effectiveness of our proposed framework and codes will be released to
facilitate future research.
3D Human Pose Estimation from a Single Image via Distance Matrix Regression
Authors: Francesc Moreno-Noguer
Date: 2016-11-28 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Human Pose Estimation, Pose Estimation, Position, regression

This paper addresses the problem of 3D human pose estimation from a single
image. We follow a standard two-step pipeline by first detecting the 2D
position of the $N$ body joints, and then using these observations to infer 3D
pose. For the first step, we use a recent CNN-based detector. For the second
step, most existing approaches perform 2$N$-to-3$N$ regression of the Cartesian
joint coordinates. We show that more precise pose estimates can be obtained by
representing both the 2D and 3D human poses using $N\times N$ distance
matrices, and formulating the problem as a 2D-to-3D distance matrix regression.
For learning such a regressor we leverage on simple Neural Network
architectures, which by construction, enforce positivity and symmetry of the
predicted matrices. The approach has also the advantage to naturally handle
missing observations and allowing to hypothesize the position of non-observed
joints. Quantitative results on Humaneva and Human3.6M datasets demonstrate
consistent performance gains over state-of-the-art. Qualitative evaluation on
the images in-the-wild of the LSP dataset, using the regressor learned on
Human3.6M, reveals very promising generalization results.
Hyperspectral CNN Classification with Limited Training Samples
Authors: Lloyd Windrim, Rishi Ramakrishnan, Arman Melkumyan, Richard Murphy
Date: 2016-11-28 00:00:00
Tasks: Classification, Data Augmentation, General Classification

Hyperspectral imaging sensors are becoming increasingly popular in robotics
applications such as agriculture and mining, and allow per-pixel thematic
classification of materials in a scene based on their unique spectral
signatures. Recently, convolutional neural networks have shown remarkable
performance for classification tasks, but require substantial amounts of
labelled training data. This data must sufficiently cover the variability
expected to be encountered in the environment. For hyperspectral data, one of
the main variations encountered outdoors is due to incident illumination, which
can change in spectral shape and intensity depending on the scene geometry. For
example, regions occluded from the sun have a lower intensity and their
incident irradiance skewed towards shorter wavelengths.
  In this work, a data augmentation strategy based on relighting is used during
training of a hyperspectral convolutional neural network. It allows training to
occur in the outdoor environment given only a small labelled region, which does
not need to sufficiently represent the geometric variability of the entire
scene. This is important for applications where obtaining large amounts of
training data is labourious, hazardous or difficult, such as labelling pixels
within shadows. Radiometric normalisation approaches for pre-processing the
hyperspectral data are analysed and it is shown that methods based on the raw
pixel data are sufficient to be used as input for the classifier. This removes
the need for external hardware such as calibration boards, which can restrict
the application of hyperspectral sensors in robotics applications. Experiments
to evaluate the classification system are carried out on two datasets captured
from a field-based platform.
Improving Fully Convolution Network for Semantic Segmentation
Authors: Bing Shuai, Ting Liu, Gang Wang
Date: 2016-11-28 00:00:00
Tasks: Scene Parsing, Segmentation, Semantic Segmentation

Fully Convolution Networks (FCN) have achieved great success in dense
prediction tasks including semantic segmentation. In this paper, we start from
discussing FCN by understanding its architecture limitations in building a
strong segmentation network. Next, we present our Improved Fully Convolution
Network (IFCN). In contrast to FCN, IFCN introduces a context network that
progressively expands the receptive fields of feature maps. In addition, dense
skip connections are added so that the context network can be effectively
optimized. More importantly, these dense skip connections enable IFCN to fuse
rich-scale context to make reliable predictions. Empirically, those
architecture modifications are proven to be significant to enhance the
segmentation performance. Without engaging any contextual post-processing, IFCN
significantly advances the state-of-the-arts on ADE20K (ImageNet scene
parsing), Pascal Context, Pascal VOC 2012 and SUN-RGBD segmentation datasets.
Range Loss for Deep Face Recognition with Long-tail
Authors: Xiao Zhang, Zhiyuan Fang, Yandong Wen, Zhifeng Li, Yu Qiao
Date: 2016-11-28 00:00:00
Tasks: Face Recognition

Convolutional neural networks have achieved great improvement on face
recognition in recent years because of its extraordinary ability in learning
discriminative features of people with different identities. To train such a
well-designed deep network, tremendous amounts of data is indispensable. Long
tail distribution specifically refers to the fact that a small number of
generic entities appear frequently while other objects far less existing.
Considering the existence of long tail distribution of the real world data,
large but uniform distributed data are usually hard to retrieve. Empirical
experiences and analysis show that classes with more samples will pose greater
impact on the feature learning process and inversely cripple the whole models
feature extracting ability on tail part data. Contrary to most of the existing
works that alleviate this problem by simply cutting the tailed data for uniform
distributions across the classes, this paper proposes a new loss function
called range loss to effectively utilize the whole long tailed data in training
process. More specifically, range loss is designed to reduce overall
intra-personal variations while enlarging inter-personal differences within one
mini-batch simultaneously when facing even extremely unbalanced data. The
optimization objective of range loss is the $k$ greatest range's harmonic mean
values in one class and the shortest inter-class distance within one batch.
Extensive experiments on two famous and challenging face recognition benchmarks
(Labeled Faces in the Wild (LFW) and YouTube Faces (YTF) not only demonstrate
the effectiveness of the proposed approach in overcoming the long tail effect
but also show the good generalization ability of the proposed approach.
Semantic Scene Completion from a Single Depth Image
Authors: Shuran Song, Fisher Yu, Andy Zeng, Angel X. Chang, Manolis Savva, Thomas Funkhouser
Date: 2016-11-28 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Semantic Scene Completion

This paper focuses on semantic scene completion, a task for producing a
complete 3D voxel representation of volumetric occupancy and semantic labels
for a scene from a single-view depth map observation. Previous work has
considered scene completion and semantic labeling of depth maps separately.
However, we observe that these two problems are tightly intertwined. To
leverage the coupled nature of these two tasks, we introduce the semantic scene
completion network (SSCNet), an end-to-end 3D convolutional network that takes
a single depth image as input and simultaneously outputs occupancy and semantic
labels for all voxels in the camera view frustum. Our network uses a
dilation-based 3D context module to efficiently expand the receptive field and
enable 3D context learning. To train our network, we construct SUNCG - a
manually created large-scale dataset of synthetic 3D scenes with dense
volumetric annotations. Our experiments demonstrate that the joint model
outperforms methods addressing each task in isolation and outperforms
alternative approaches on the semantic scene completion task.
Nonparametric General Reinforcement Learning
Authors: Jan Leike
Date: 2016-11-28 00:00:00
Tasks: General Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Thompson Sampling

Reinforcement learning (RL) problems are often phrased in terms of Markov
decision processes (MDPs). In this thesis we go beyond MDPs and consider RL in
environments that are non-Markovian, non-ergodic and only partially observable.
Our focus is not on practical algorithms, but rather on the fundamental
underlying problems: How do we balance exploration and exploitation? How do we
explore optimally? When is an agent optimal? We follow the nonparametric
realizable paradigm.
  We establish negative results on Bayesian RL agents, in particular AIXI. We
show that unlucky or adversarial choices of the prior cause the agent to
misbehave drastically. Therefore Legg-Hutter intelligence and balanced Pareto
optimality, which depend crucially on the choice of the prior, are entirely
subjective. Moreover, in the class of all computable environments every policy
is Pareto optimal. This undermines all existing optimality properties for AIXI.
However, there are Bayesian approaches to general RL that satisfy objective
optimality guarantees: We prove that Thompson sampling is asymptotically
optimal in stochastic environments in the sense that its value converges to the
value of the optimal policy. We connect asymptotic optimality to regret given a
recoverability assumption on the environment that allows the agent to recover
from mistakes. Hence Thompson sampling achieves sublinear regret in these
environments.
  Our results culminate in a formal solution to the grain of truth problem: A
Bayesian agent acting in a multi-agent environment learns to predict the other
agents' policies if its prior assigns positive probability to them (the prior
contains a grain of truth). We construct a large but limit computable class
containing a grain of truth and show that agents based on Thompson sampling
over this class converge to play Nash equilibria in arbitrary unknown
computable multi-agent environments.
Block-Diagonal Sparse Representation by Learning a Linear Combination Dictionary for Recognition
Authors: Xinglin Piao, Yongli Hu, Yanfeng Sun, Junbin Gao, Bao-Cai Yin
Date: 2016-01-07 00:00:00
Tasks: Dictionary Learning

In a sparse representation based recognition scheme, it is critical to learn
a desired dictionary, aiming both good representational power and
discriminative performance. In this paper, we propose a new dictionary learning
model for recognition applications, in which three strategies are adopted to
achieve these two objectives simultaneously. First, a block-diagonal constraint
is introduced into the model to eliminate the correlation between classes and
enhance the discriminative performance. Second, a low-rank term is adopted to
model the coherence within classes for refining the sparse representation of
each class. Finally, instead of using the conventional over-complete
dictionary, a specific dictionary constructed from the linear combination of
the training samples is proposed to enhance the representational power of the
dictionary and to improve the robustness of the sparse representation model.
The proposed method is tested on several public datasets. The experimental
results show the method outperforms most state-of-the-art methods.