pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learned Frame Prediction for Video Compression
Authors: Serkan Sulun
Date: 2018-11-27 00:00:00
Tasks: Motion Compensation, Prediction, Video Compression

Motion compensation is one of the most essential methods for any video
compression algorithm. Video frame prediction is a task analogous to motion
compensation. In recent years, the task of frame prediction is undertaken by
deep neural networks (DNNs). In this thesis we create a DNN to perform learned
frame prediction and additionally implement a codec that contains our DNN. We
train our network using two methods for two different goals. Firstly we train
our network based on mean square error (MSE) only, aiming to obtain highest
PSNR values at frame prediction and video compression. Secondly we use
adversarial training to produce visually more realistic frame predictions. For
frame prediction, we compare our method with the baseline methods of frame
difference and 16x16 block motion compensation. For video compression we
further include x264 video codec in the comparison. We show that in frame
prediction, adversarial training produces frames that look sharper and more
realistic, compared MSE based training, but in video compression it
consistently performs worse. This proves that even though adversarial training
is useful for generating video frames that are more pleasing to the human eye,
they should not be employed for video compression. Moreover, our network
trained with MSE produces accurate frame predictions, and in quantitative
results, for both tasks, it produces comparable results in all videos and
outperforms other methods on average. More specifically, learned frame
prediction outperforms other methods in terms of rate-distortion performance in
case of high motion video, while the rate-distortion performance of our method
is competitive with that of x264 in low motion video.
Deep Geometric Prior for Surface Reconstruction
Authors: Francis Williams, Teseo Schneider, Claudio Silva, Denis Zorin, Joan Bruna, Daniele Panozzo
Date: 2018-11-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: Surface Reconstruction

The reconstruction of a discrete surface from a point cloud is a fundamental
geometry processing problem that has been studied for decades, with many
methods developed. We propose the use of a deep neural network as a geometric
prior for surface reconstruction. Specifically, we overfit a neural network
representing a local chart parameterization to part of an input point cloud
using the Wasserstein distance as a measure of approximation. By jointly
fitting many such networks to overlapping parts of the point cloud, while
enforcing a consistency condition, we compute a manifold atlas. By sampling
this atlas, we can produce a dense reconstruction of the surface approximating
the input cloud. The entire procedure does not require any training data or
explicit regularization, yet, we show that it is able to perform remarkably
well: not introducing typical overfitting artifacts, and approximating sharp
features closely at the same time. We experimentally show that this geometric
prior produces good results for both man-made objects containing sharp features
and smoother organic objects, as well as noisy inputs. We compare our method
with a number of well-known reconstruction methods on a standard surface
reconstruction benchmark.
Single-Agent Policy Tree Search With Guarantees
Authors: Laurent Orseau, Levi H. S. Lelis, Tor Lattimore, Théophane Weber
Date: 2018-11-27 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Heuristic Search, Sokoban

We introduce two novel tree search algorithms that use a policy to guide
search. The first algorithm is a best-first enumeration that uses a cost
function that allows us to prove an upper bound on the number of nodes to be
expanded before reaching a goal state. We show that this best-first algorithm
is particularly well suited for `needle-in-a-haystack' problems. The second
algorithm is based on sampling and we prove an upper bound on the expected
number of nodes it expands before reaching a set of goal states. We show that
this algorithm is better suited for problems where many paths lead to a goal.
We validate these tree search algorithms on 1,000 computer-generated levels of
Sokoban, where the policy used to guide the search comes from a neural network
trained using A3C. Our results show that the policy tree search algorithms we
introduce are competitive with a state-of-the-art domain-independent planner
that uses heuristic search.
Beyond One Glance: Gated Recurrent Architecture for Hand Segmentation
Authors: Wei Wang, Kaicheng Yu, Joachim Hugonot, Pascal Fua, Mathieu Salzmann
Date: 2018-11-27 00:00:00
Tasks: Decoder, Hand Segmentation, Mixed Reality, Road Segmentation, Segmentation

As mixed reality is gaining increased momentum, the development of effective
and efficient solutions to egocentric hand segmentation is becoming critical.
Traditional segmentation techniques typically follow a one-shot approach, where
the image is passed forward only once through a model that produces a
segmentation mask. This strategy, however, does not reflect the perception of
humans, who continuously refine their representation of the world. In this
paper, we therefore introduce a novel gated recurrent architecture. It goes
beyond both iteratively passing the predicted segmentation mask through the
network and adding a standard recurrent unit to it. Instead, it incorporates
multiple encoder-decoder layers of the segmentation network, so as to keep
track of its internal state in the refinement process. As evidenced by our
results on standard hand segmentation benchmarks and on our own dataset, our
approach outperforms these other, simpler recurrent segmentation techniques, as
well as the state-of-the-art hand segmentation one. Furthermore, we demonstrate
the generality of our approach by applying it to road segmentation, where it
also outperforms other baseline methods.
Efficient Image Retrieval via Decoupling Diffusion into Online and Offline Processing
Authors: Fan Yang, Ryota Hinami, Yusuke Matsui, Steven Ly, Shin'ichi Satoh
Date: 2018-11-27 00:00:00
Tasks: Image Retrieval, Re-Ranking, Retrieval

Diffusion is commonly used as a ranking or re-ranking method in retrieval
tasks to achieve higher retrieval performance, and has attracted lots of
attention in recent years. A downside to diffusion is that it performs slowly
in comparison to the naive k-NN search, which causes a non-trivial online
computational cost on large datasets. To overcome this weakness, we propose a
novel diffusion technique in this paper. In our work, instead of applying
diffusion to the query, we pre-compute the diffusion results of each element in
the database, making the online search a simple linear combination on top of
the k-NN search process. Our proposed method becomes 10~ times faster in terms
of online search speed. Moreover, we propose to use late truncation instead of
early truncation in previous works to achieve better retrieval performance.
Kernel-based Multi-Task Contextual Bandits in Cellular Network Configuration
Authors: Xiaoxiao Wang, Xueying Guo, Jie Chuai, Zhitang Chen, Xin Liu
Date: 2018-11-27 00:00:00
Tasks: Multi-Armed Bandits, Multi-Task Learning

Cellular network configuration plays a critical role in network performance. In current practice, network configuration depends heavily on field experience of engineers and often remains static for a long period of time. This practice is far from optimal. To address this limitation, online-learning-based approaches have great potentials to automate and optimize network configuration. Learning-based approaches face the challenges of learning a highly complex function for each base station and balancing the fundamental exploration-exploitation tradeoff while minimizing the exploration cost. Fortunately, in cellular networks, base stations (BSs) often have similarities even though they are not identical. To leverage such similarities, we propose kernel-based multi-BS contextual bandit algorithm based on multi-task learning. In the algorithm, we leverage the similarity among different BSs defined by conditional kernel embedding. We present theoretical analysis of the proposed algorithm in terms of regret and multi-task-learning efficiency. We evaluate the effectiveness of our algorithm based on a simulator built by real traces.
Making Agents' Abilities Explicit
Authors: Yedi Zhang, Fu Song, Taolue Chen
Date: 2018-11-27 00:00:00

Alternating-time temporal logics (ATL/ATL*) represent a family of modal
logics for reasoning about agents' strategic abilities in multiagent systems
(MAS). The interpretations of ATL/ATL* over the semantic model Concurrent Game
Structures (CGS) usually vary depending on the agents' abilities, for instance,
perfect vs. imperfect information, perfect vs. imperfect recall, resulting in a
variety of variants which have been studied extensively in literature. However,
they are defined at the semantic level, which may limit modeling flexibilities
and may give counter-intuitive interpretations. To mitigate these issues, in
this work, we propose to extend CGS with agents' abilities and study the new
semantics of ATL/ATL* under this model. We give PSACE/2EXPTIME model-checking
algorithms for ATL/ATL* and implement them as a prototype tool. Experiment
results show the practical feasibility of the approach.
Beta Distribution Drift Detection for Adaptive Classifiers
Authors: Lukas Fleckenstein, Sebastian Kauschke, Johannes Fürnkranz
Date: 2018-11-27 00:00:00
Tasks: Drift Detection, General Classification

With today's abundant streams of data, the only constant we can rely on is
change. For stream classification algorithms, it is necessary to adapt to
concept drift. This can be achieved by monitoring the model error, and
triggering counter measures as changes occur. In this paper, we propose a drift
detection mechanism that fits a beta distribution to the model error, and
treats abnormal behavior as drift. It works with any given model, leverages
prior knowledge about this model, and allows to set application-specific
confidence thresholds. Experiments confirm that it performs well, in particular
when drift occurs abruptly.
Are 2D-LSTM really dead for offline text recognition?
Authors: Bastien Moysset, Ronaldo Messina
Date: 2018-11-27 00:00:00
Tasks: GPU, Handwritten Text Recognition, Language Modeling, Language Modelling

There is a recent trend in handwritten text recognition with deep neural
networks to replace 2D recurrent layers with 1D, and in some cases even
completely remove the recurrent layers, relying on simple feed-forward
convolutional only architectures. The most used type of recurrent layer is the
Long-Short Term Memory (LSTM). The motivations to do so are many: there are few
open-source implementations of 2D-LSTM, even fewer supporting GPU
implementations (currently cuDNN only implements 1D-LSTM); 2D recurrences
reduce the amount of computations that can be parallelized, and thus possibly
increase the training/inference time; recurrences create global dependencies
with respect to the input, and sometimes this may not be desirable.
  Many recent competitions were won by systems that employed networks that use
2D-LSTM layers. Most previous work that compared 1D or pure feed-forward
architectures to 2D recurrent models have done so on simple datasets or did not
fully optimize the "baseline" 2D model compared to the challenger model, which
was dully optimized.
  In this work, we aim at a fair comparison between 2D and competing models and
also extensively evaluate them on more complex datasets that are more
representative of challenging "real-world" data, compared to "academic"
datasets that are more restricted in their complexity. We aim at determining
when and why the 1D and 2D recurrent models have different results. We also
compare the results with a language model to assess if linguistic constraints
do level the performance of the different networks.
  Our results show that for challenging datasets, 2D-LSTM networks still seem
to provide the highest performances and we propose a visualization strategy to
explain it.
DSBI: Double-Sided Braille Image Dataset and Algorithm Evaluation for Braille Dots Detection
Authors: Renqiang Li, Hong Liu, Xiangdong Wan, Yueliang Qian
Date: 2018-11-27 00:00:00

Braille is an effective way for the visually impaired to learn knowledge and
obtain information. Braille image recognition aims to automatically detect
Braille dots in the whole Braille image. There is no available public datasets
for Braille image recognition to push relevant research and evaluate
algorithms. This paper constructs a large-scale Double-Sided Braille Image
dataset DSBI with detailed Braille recto dots, verso dots and Braille cells
annotation. To quickly annotate Braille images, an auxiliary annotation
strategy is proposed, which adopts initial automatic detection of Braille dots
and modifies annotation results by convenient human-computer interaction
method. This labeling strategy can averagely increase label efficiency by six
times for recto dots annotation in one Braille image. Braille dots detection is
the core and basic step for Braille image recognition. This paper also
evaluates some Braille dots detection methods on our dataset DSBI and gives the
benchmark performance of recto dots detection. We have released our Braille
images dataset on the GitHub website.
Chasing the Echo State Property
Authors: Claudio Gallicchio
Date: 2018-11-27 00:00:00

Reservoir Computing (RC) provides an efficient way for designing dynamical recurrent neural models. While training is restricted to a simple output component, the recurrent connections are left untrained after initialization, subject to stability constraints specified by the Echo State Property (ESP). Literature conditions for the ESP typically fail to properly account for the effects of driving input signals, often limiting the potentialities of the RC approach. In this paper, we study the fundamental aspect of asymptotic stability of RC models in presence of driving input, introducing an empirical ESP index that enables to easily analyze the stability regimes of reservoirs. Results on two benchmark datasets reveal interesting insights on the dynamical properties of input-driven reservoirs, suggesting that the actual domain of ESP validity is much wider than what covered by literature conditions commonly used in RC practice.
UnDEMoN 2.0: Improved Depth and Ego Motion Estimation through Deep Image Sampling
Authors: Madhu Babu V, Swagat Kumar, Anima Majumder, Kaushik Das
Date: 2018-11-27 00:00:00
Tasks: Depth Estimation, Image Reconstruction, Motion Estimation, Pose Estimation

In this paper, we provide an improved version of UnDEMoN model for depth and
ego motion estimation from monocular images. The improvement is achieved by
combining the standard bi-linear sampler with a deep network based image
sampling model (DIS-NET) to provide better image reconstruction capabilities on
which the depth estimation accuracy depends in un-supervised learning models.
While DIS-NET provides higher order regression and larger input search space,
the bi-linear sampler provides geometric constraints necessary for reducing the
size of the solution space for an ill-posed problem of this kind. This
combination is shown to provide significant improvement in depth and pose
estimation accuracy outperforming all existing state-of-the-art methods in this
category. In addition, the modified network uses far less number of tunable
parameters making it one of the lightest deep network model for depth
estimation. The proposed model is labeled as "UnDEMoN 2.0" indicating an
improvement over the existing UnDEMoN model. The efficacy of the proposed model
is demonstrated through rigorous experimental analysis on the standard KITTI
dataset.
Automatic Image Stylization Using Deep Fully Convolutional Networks
Authors: Feida Zhu, Yizhou Yu
Date: 2018-11-27 00:00:00
Tasks: Deep Learning, Image Stylization

Color and tone stylization strives to enhance unique themes with artistic
color and tone adjustments. It has a broad range of applications from
professional image postprocessing to photo sharing over social networks.
Mainstream photo enhancement softwares provide users with predefined styles,
which are often hand-crafted through a trial-and-error process. Such photo
adjustment tools lack a semantic understanding of image contents and the
resulting global color transform limits the range of artistic styles it can
represent. On the other hand, stylistic enhancement needs to apply distinct
adjustments to various semantic regions. Such an ability enables a broader
range of visual styles. In this paper, we propose a novel deep learning
architecture for automatic image stylization, which learns local enhancement
styles from image pairs. Our deep learning architecture is an end-to-end deep
fully convolutional network performing semantics-aware feature extraction as
well as automatic image adjustment prediction. Image stylization can be
efficiently accomplished with a single forward pass through our deep network.
Experiments on existing datasets for image stylization demonstrate the
effectiveness of our deep learning architecture.
Affinity Derivation and Graph Merge for Instance Segmentation
Authors: Yiding Liu, Siyu Yang, Bin Li, Wengang Zhou, Jizheng Xu, Houqiang Li, Yan Lu
Date: 2018-11-27 00:00:00
Proceeding: ECCV 2018 9
Tasks: Instance Segmentation, Semantic Segmentation

We present an instance segmentation scheme based on pixel affinity
information, which is the relationship of two pixels belonging to a same
instance. In our scheme, we use two neural networks with similar structure. One
is to predict pixel level semantic score and the other is designed to derive
pixel affinities.
  Regarding pixels as the vertexes and affinities as edges, we then propose a
simple yet effective graph merge algorithm to cluster pixels into instances.
Experimental results show that our scheme can generate fine-grained instance
mask.
  With Cityscapes training data, the proposed scheme achieves 27.3 AP on test
set.
Efficient non-uniform quantizer for quantized neural network targeting reconfigurable hardware
Authors: Natan Liss, Chaim Baskin, Avi Mendelson, Alex M. Bronstein, Raja Giryes
Date: 2018-11-27 00:00:00
Tasks: image-classification, Image Classification, speech-recognition, Speech Recognition

Convolutional Neural Networks (CNN) has become more popular choice for
various tasks such as computer vision, speech recognition and natural language
processing. Thanks to their large computational capability and throughput, GPUs
,which are not power efficient and therefore does not suit low power systems
such as mobile devices, are the most common platform for both training and
inferencing tasks. Recent studies has shown that FPGAs can provide a good
alternative to GPUs as a CNN accelerator, due to their re-configurable nature,
low power and small latency. In order for FPGA-based accelerators outperform
GPUs in inference task, both the parameters of the network and the activations
must be quantized. While most works use uniform quantizers for both parameters
and activations, it is not always the optimal one, and a non-uniform quantizer
need to be considered. In this work we introduce a custom hardware-friendly
approach to implement non-uniform quantizers. In addition, we use a single
scale integer representation of both parameters and activations, for both
training and inference. The combined method yields a hardware efficient
non-uniform quantizer, fit for real-time applications. We have tested our
method on CIFAR-10 and CIFAR-100 image classification datasets with ResNet-18
and VGG-like architectures, and saw little degradation in accuracy.
Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression
Authors: Neha Gupta, Aaron Sidford
Date: 2018-11-27 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: regression

In this paper, we obtain improved running times for regression and top
eigenvector computation for numerically sparse matrices. Given a data matrix $A
\in \mathbb{R}^{n \times d}$ where every row $a \in \mathbb{R}^d$ has
$\|a\|_2^2 \leq L$ and numerical sparsity at most $s$, i.e. $\|a\|_1^2 /
\|a\|_2^2 \leq s$, we provide faster algorithms for these problems in many
parameter settings.
  For top eigenvector computation, we obtain a running time of $\tilde{O}(nd +
r(s + \sqrt{r s}) / \mathrm{gap}^2)$ where $\mathrm{gap} > 0$ is the relative
gap between the top two eigenvectors of $A^\top A$ and $r$ is the stable rank
of $A$. This running time improves upon the previous best unaccelerated running
time of $O(nd + r d / \mathrm{gap}^2)$ as it is always the case that $r \leq d$
and $s \leq d$.
  For regression, we obtain a running time of $\tilde{O}(nd + (nL / \mu)
\sqrt{s nL / \mu})$ where $\mu > 0$ is the smallest eigenvalue of $A^\top A$.
This running time improves upon the previous best unaccelerated running time of
$\tilde{O}(nd + n L d / \mu)$. This result expands the regimes where regression
can be solved in nearly linear time from when $L/\mu = \tilde{O}(1)$ to when $L
/ \mu = \tilde{O}(d^{2/3} / (sn)^{1/3})$.
  Furthermore, we obtain similar improvements even when row norms and numerical
sparsities are non-uniform and we show how to achieve even faster running times
by accelerating using approximate proximal point [Frostig et. al. 2015] /
catalyst [Lin et. al. 2015]. Our running times depend only on the size of the
input and natural numerical measures of the matrix, i.e. eigenvalues and
$\ell_p$ norms, making progress on a key open problem regarding optimal running
times for efficient large-scale learning.
Object Tracking by Reconstruction with View-Specific Discriminative Correlation Filters
Authors: Ugur Kart, Alan Lukezic, Matej Kristan, Joni-Kristian Kamarainen, Jiri Matas
Date: 2018-11-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Reconstruction, Object Tracking

Standard RGB-D trackers treat the target as an inherently 2D structure, which
makes modelling appearance changes related even to simple out-of-plane rotation
highly challenging. We address this limitation by proposing a novel long-term
RGB-D tracker - Object Tracking by Reconstruction (OTR). The tracker performs
online 3D target reconstruction to facilitate robust learning of a set of
view-specific discriminative correlation filters (DCFs). The 3D reconstruction
supports two performance-enhancing features: (i) generation of accurate spatial
support for constrained DCF learning from its 2D projection and (ii) point
cloud based estimation of 3D pose change for selection and storage of
view-specific DCFs which are used to robustly localize the target after
out-of-view rotation or heavy occlusion. Extensive evaluation of OTR on the
challenging Princeton RGB-D tracking and STC Benchmarks shows it outperforms
the state-of-the-art by a large margin.
Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects
Authors: Yusuke Niitani, Takuya Akiba, Tommi Kerola, Toru Ogawa, Shotaro Sano, Shuji Suzuki
Date: 2018-11-27 00:00:00
Proceeding: CVPR 2019 6
Tasks: object-detection, Object Detection

Efficient and reliable methods for training of object detectors are in higher
demand than ever, and more and more data relevant to the field is becoming
available. However, large datasets like Open Images Dataset v4 (OID) are
sparsely annotated, and some measure must be taken in order to ensure the
training of a reliable detector. In order to take the incompleteness of these
datasets into account, one possibility is to use pretrained models to detect
the presence of the unverified objects. However, the performance of such a
strategy depends largely on the power of the pretrained model. In this study,
we propose part-aware sampling, a method that uses human intuition for the
hierarchical relation between objects. In terse terms, our method works by
making assumptions like "a bounding box for a car should contain a bounding box
for a tire". We demonstrate the power of our method on OID and compare the
performance against a method based on a pretrained model. Our method also won
the first and second place on the public and private test sets of the Google AI
Open Images Competition 2018.