pwc-archive/papers-with-abstracts huggingface dataset:
Deep Contextual Recurrent Residual Networks for Scene Labeling
Authors: T. Hoang Ngan Le, Chi Nhan Duong, Ligong Han, Khoa Luu, Marios Savvides, Dipan Pal
Date: 2017-04-12 00:00:00
Tasks: Representation Learning, Scene Labeling

Designed as extremely deep architectures, deep residual networks which
provide a rich visual representation and offer robust convergence behaviors
have recently achieved exceptional performance in numerous computer vision
problems. Being directly applied to a scene labeling problem, however, they
were limited to capture long-range contextual dependence, which is a critical
aspect. To address this issue, we propose a novel approach, Contextual
Recurrent Residual Networks (CRRN) which is able to simultaneously handle rich
visual representation learning and long-range context modeling within a fully
end-to-end deep network. Furthermore, our proposed end-to-end CRRN is
completely trained from scratch, without using any pre-trained models in
contrast to most existing methods usually fine-tuned from the state-of-the-art
pre-trained models, e.g. VGG-16, ResNet, etc. The experiments are conducted on
four challenging scene labeling datasets, i.e. SiftFlow, CamVid, Stanford
background and SUN datasets, and compared against various state-of-the-art
scene labeling methods.
Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation
Authors: Ngan Le, Kha Gia Quach, Khoa Luu, Marios Savvides, Chenchen Zhu
Date: 2017-04-12 00:00:00
Tasks: Segmentation, Semantic Segmentation

Variational Level Set (LS) has been a widely used method in medical
segmentation. However, it is limited when dealing with multi-instance objects
in the real world. In addition, its segmentation results are quite sensitive to
initial settings and highly depend on the number of iterations. To address
these issues and boost the classic variational LS methods to a new level of the
learnable deep learning approaches, we propose a novel definition of contour
evolution named Recurrent Level Set (RLS)} to employ Gated Recurrent Unit under
the energy minimization of a variational LS functional. The curve deformation
process in RLS is formed as a hidden state evolution procedure and updated by
minimizing an energy functional composed of fitting forces and contour length.
By sharing the convolutional features in a fully end-to-end trainable
framework, we extend RLS to Contextual RLS (CRLS) to address semantic
segmentation in the wild. The experimental results have shown that our proposed
RLS improves both computational time and segmentation accuracy against the
classic variations LS-based method, whereas the fully end-to-end system CRLS
achieves competitive performance compared to the state-of-the-art semantic
segmentation approaches.
CASP Solutions for Planning in Hybrid Domains
Authors: Marcello Balduccini, Daniele Magazzeni, Marco Maratea, Emily LeBlanc
Date: 2017-04-12 00:00:00

CASP is an extension of ASP that allows for numerical constraints to be added
in the rules. PDDL+ is an extension of the PDDL standard language of automated
planning for modeling mixed discrete-continuous dynamics.
  In this paper, we present CASP solutions for dealing with PDDL+ problems,
i.e., encoding from PDDL+ to CASP, and extensions to the algorithm of the EZCSP
CASP solver in order to solve CASP programs arising from PDDL+ domains. An
experimental analysis, performed on well-known linear and non-linear variants
of PDDL+ domains, involving various configurations of the EZCSP solver, other
CASP solvers, and PDDL+ planners, shows the viability of our solution.
ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge
Authors: Robyn Speer, Joanna Lowry-Duda
Date: 2017-04-11 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: General Knowledge, Multilingual Word Embeddings, Task 2, Word Embeddings, Word Similarity

This paper describes Luminoso's participation in SemEval 2017 Task 2,
"Multilingual and Cross-lingual Semantic Word Similarity", with a system based
on ConceptNet. ConceptNet is an open, multilingual knowledge graph that focuses
on general knowledge that relates the meanings of words and phrases. Our
submission to SemEval was an update of previous work that builds high-quality,
multilingual word embeddings from a combination of ConceptNet and
distributional semantics. Our system took first place in both subtasks. It
ranked first in 4 out of 5 of the separate languages, and also ranked first in
all 10 of the cross-lingual language pairs.
Quantized Nonparametric Estimation over Sobolev Ellipsoids
Authors: Yuancheng Zhu, John Lafferty
Date: 2015-03-25 00:00:00
Tasks: Quantization

We formulate the notion of minimax estimation under storage or communication
constraints, and prove an extension to Pinsker's theorem for nonparametric
estimation over Sobolev ellipsoids. Placing limits on the number of bits used
to encode any estimator, we give tight lower and upper bounds on the excess
risk due to quantization in terms of the number of bits, the signal size, and
the noise level. This establishes the Pareto optimal tradeoff between storage
and risk under quantization constraints for Sobolev spaces. Our results and
proof techniques combine elements of rate distortion theory and minimax
analysis. The proposed quantized estimation scheme, which shows achievability
of the lower bounds, is adaptive in the usual statistical sense, achieving the
optimal quantized minimax rate without knowledge of the smoothness parameter of
the Sobolev space. It is also adaptive in a computational sense, as it
constructs the code only after observing the data, to dynamically allocate more
codewords to blocks where the estimated signal size is large. Simulations are
included that illustrate the effect of quantization on statistical risk.
Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification
Authors: Muhammad Zeshan Afzal, Andreas Kölsch, Sheraz Ahmed, Marcus Liwicki
Date: 2017-04-11 00:00:00
Tasks: document-image-classification, Document Image Classification, General Classification, image-classification, Image Classification, Object Recognition, Transfer Learning

We present an exhaustive investigation of recent Deep Learning architectures,
algorithms, and strategies for the task of document image classification to
finally reduce the error by more than half. Existing approaches, such as the
DeepDocClassifier, apply standard Convolutional Network architectures with
transfer learning from the object recognition domain. The contribution of the
paper is threefold: First, it investigates recently introduced very deep neural
network architectures (GoogLeNet, VGG, ResNet) using transfer learning (from
real images). Second, it proposes transfer learning from a huge set of document
images, i.e. 400,000 documents. Third, it analyzes the impact of the amount of
training data (document images) and other parameters to the classification
abilities. We use two datasets, the Tobacco-3482 and the large-scale RVL-CDIP
dataset. We achieve an accuracy of 91.13% for the Tobacco-3482 dataset while
earlier approaches reach only 77.6%. Thus, a relative error reduction of more
than 60% is achieved. For the large dataset RVL-CDIP, an accuracy of 90.97% is
achieved, corresponding to a relative error reduction of 11.5%.
Unsupervised Learning of Long-Term Motion Dynamics for Videos
Authors: Zelun Luo, Boya Peng, De-An Huang, Alexandre Alahi, Li Fei-Fei
Date: 2017-01-07 00:00:00
Proceeding: CVPR 2017 7
Tasks: Decoder, Representation Learning

We present an unsupervised representation learning approach that compactly
encodes the motion dependencies in videos. Given a pair of images from a video
clip, our framework learns to predict the long-term 3D motions. To reduce the
complexity of the learning framework, we propose to describe the motion as a
sequence of atomic 3D flows computed with RGB-D modality. We use a Recurrent
Neural Network based Encoder-Decoder framework to predict these sequences of
flows. We argue that in order for the decoder to reconstruct these sequences,
the encoder must learn a robust video representation that captures long-term
motion dependencies and spatial-temporal relations. We demonstrate the
effectiveness of our learned temporal representations on activity
classification across multiple modalities and datasets such as NTU RGB+D and
MSR Daily Activity 3D. Our framework is generic to any input modality, i.e.,
RGB, Depth, and RGB-D videos.
Leveraging Term Banks for Answering Complex Questions: A Case for Sparse Vectors
Authors: Peter D. Turney
Date: 2017-04-11 00:00:00
Tasks: Open-Domain Question Answering, Question Answering

While open-domain question answering (QA) systems have proven effective for
answering simple questions, they struggle with more complex questions. Our goal
is to answer more complex questions reliably, without incurring a significant
cost in knowledge resource construction to support the QA. One readily
available knowledge resource is a term bank, enumerating the key concepts in a
domain. We have developed an unsupervised learning approach that leverages a
term bank to guide a QA system, by representing the terminological knowledge
with thousands of specialized vector spaces. In experiments with complex
science questions, we show that this approach significantly outperforms several
state-of-the-art QA systems, demonstrating that significant leverage can be
gained from continuous vector representations of domain terminology.
FastMask: Segment Multi-scale Object Candidates in One Shot
Authors: Hexiang Hu, Shiyi Lan, Yuning Jiang, Zhimin Cao, Fei Sha
Date: 2016-12-28 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object

Objects appear to scale differently in natural images. This fact requires
methods dealing with object-centric tasks (e.g. object proposal) to have robust
performance over variances in object scales. In the paper, we present a novel
segment proposal framework, namely FastMask, which takes advantage of
hierarchical features in deep convolutional neural networks to segment
multi-scale objects in one shot. Innovatively, we adapt segment proposal
network into three different functional components (body, neck and head). We
further propose a weight-shared residual neck module as well as a
scale-tolerant attentional head module for efficient one-shot inference. On MS
COCO benchmark, the proposed FastMask outperforms all state-of-the-art segment
proposal methods in average recall being 2~5 times faster. Moreover, with a
slight trade-off in accuracy, FastMask can segment objects in near real time
(~13 fps) with 800*600 resolution images, demonstrating its potential in
practical applications. Our implementation is available on
https://github.com/voidrank/FastMask.
Learning Detection with Diverse Proposals
Authors: Samaneh Azadi, Jiashi Feng, Trevor Darrell
Date: 2017-04-11 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, object-detection, Object Detection

To predict a set of diverse and informative proposals with enriched
representations, this paper introduces a differentiable Determinantal Point
Process (DPP) layer that is able to augment the object detection architectures.
Most modern object detection architectures, such as Faster R-CNN, learn to
localize objects by minimizing deviations from the ground-truth but ignore
correlation between multiple proposals and object categories. Non-Maximum
Suppression (NMS) as a widely used proposal pruning scheme ignores label- and
instance-level relations between object candidates resulting in multi-labeled
detections. In the multi-class case, NMS selects boxes with the largest
prediction scores ignoring the semantic relation between categories of
potential election. In contrast, our trainable DPP layer, allowing for Learning
Detection with Diverse Proposals (LDDP), considers both label-level contextual
information and spatial layout relationships between proposals without
increasing the number of parameters of the network, and thus improves location
and category specifications of final detected bounding boxes substantially
during both training and inference schemes. Furthermore, we show that LDDP
keeps it superiority over Faster R-CNN even if the number of proposals
generated by LDPP is only ~30% as many as those for Faster R-CNN.
Shape Completion using 3D-Encoder-Predictor CNNs and Shape Synthesis
Authors: Angela Dai, Charles Ruizhongtai Qi, Matthias Nießner
Date: 2016-12-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D geometry, 3D Shape Generation

We introduce a data-driven approach to complete partial 3D shapes through a
combination of volumetric deep neural networks and 3D shape synthesis. From a
partially-scanned input shape, our method first infers a low-resolution -- but
complete -- output. To this end, we introduce a 3D-Encoder-Predictor Network
(3D-EPN) which is composed of 3D convolutional layers. The network is trained
to predict and fill in missing data, and operates on an implicit surface
representation that encodes both known and unknown space. This allows us to
predict global structure in unknown areas at high accuracy. We then correlate
these intermediary results with 3D geometry from a shape database at test time.
In a final pass, we propose a patch-based 3D shape synthesis method that
imposes the 3D geometry from these retrieved shapes as constraints on the
coarsely-completed mesh. This synthesis process enables us to reconstruct
fine-scale detail and generate high-resolution output while respecting the
global mesh structure obtained by the 3D-EPN. Although our 3D-EPN outperforms
state-of-the-art completion method, the main contribution in our work lies in
the combination of a data-driven shape predictor and analytic 3D shape
synthesis. In our results, we show extensive evaluations on a newly-introduced
shape completion benchmark for both real-world and synthetic data.
Feature Selection Parallel Technique for Remotely Sensed Imagery Classification
Authors: Nhien-An Le-Khac, M-Tahar Kechadi, Bo Wu, C. Chen
Date: 2017-04-11 00:00:00
Tasks: Classification, feature selection, General Classification

Remote sensing research focusing on feature selection has long attracted the
attention of the remote sensing community because feature selection is a
prerequisite for image processing and various applications. Different feature
selection methods have been proposed to improve the classification accuracy.
They vary from basic search techniques to clonal selections, and various
optimal criteria have been investigated. Recently, methods using
dependence-based measures have attracted much attention due to their ability to
deal with very high dimensional datasets. However, these methods are based on
Cramers V test, which has performance issues with large datasets. In this
paper, we propose a parallel approach to improve their performance. We evaluate
our approach on hyper-spectral and high spatial resolution images and compare
it to the proposed methods with a centralized version as preliminary results.
The results are very promising.
Generating Holistic 3D Scene Abstractions for Text-based Image Retrieval
Authors: Ang Li, Jin Sun, Joe Yue-Hei Ng, Ruichi Yu, Vlad I. Morariu, Larry S. Davis
Date: 2016-11-28 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Retrieval, Object, object-detection, Object Detection, Retrieval

Spatial relationships between objects provide important information for
text-based image retrieval. As users are more likely to describe a scene from a
real world perspective, using 3D spatial relationships rather than 2D
relationships that assume a particular viewing direction, one of the main
challenges is to infer the 3D structure that bridges images with users' text
descriptions. However, direct inference of 3D structure from images requires
learning from large scale annotated data. Since interactions between objects
can be reduced to a limited set of atomic spatial relations in 3D, we study the
possibility of inferring 3D structure from a text description rather than an
image, applying physical relation models to synthesize holistic 3D abstract
object layouts satisfying the spatial constraints present in a textual
description. We present a generic framework for retrieving images from a
textual description of a scene by matching images with these generated abstract
object layouts. Images are ranked by matching object detection outputs
(bounding boxes) to 2D layout candidates (also represented by bounding boxes)
which are obtained by projecting the 3D scenes with sampled camera directions.
We validate our approach using public indoor scene datasets and show that our
method outperforms baselines built upon object occurrence histograms and
learned 2D pairwise relations.
Toward a new approach for massive LiDAR data processing
Authors: V-H Cao, K-X Chu, Nhien-An Le-Khac, M-T. Kechadi, Debra F. Laefer, Linh Truong-Hong
Date: 2017-04-11 00:00:00
Tasks: GPU

Laser scanning (also known as Light Detection And Ranging) has been widely
applied in various application. As part of that, aerial laser scanning (ALS)
has been used to collect topographic data points for a large area, which
triggers to million points to be acquired. Furthermore, today, with integrating
full wareform (FWF) technology during ALS data acquisition, all return
information of laser pulse is stored. Thus, ALS data are to be massive and
complexity since the FWF of each laser pulse can be stored up to 256 samples
and density of ALS data is also increasing significantly. Processing LiDAR data
demands heavy operations and the traditional approaches require significant
hardware and running time. On the other hand, researchers have recently
proposed parallel approaches for analysing LiDAR data. These approaches are
normally based on parallel architecture of target systems such as multi-core
processors, GPU, etc. However, there is still missing efficient
approaches/tools supporting the analysis of LiDAR data due to the lack of a
deep study on both library tools and algorithms used in processing this data.
In this paper, we present a comparative study of software libraries and
algorithms to optimise the processing of LiDAR data. We also propose new method
to improve this process with experiments on large LiDAR data. Finally, we
discuss on a parallel solution of our approach where we integrate parallel
computing in processing LiDAR data.
Improving Fitness Functions in Genetic Programming for Classification on Unbalanced Credit Card Datasets
Authors: Van Loi Cao, Nhien-An Le-Khac, Miguel Nicolau, Michael ONeill, James McDermott
Date: 2017-04-11 00:00:00
Tasks: Fraud Detection, General Classification

Credit card fraud detection based on machine learning has recently attracted
considerable interest from the research community. One of the most important
tasks in this area is the ability of classifiers to handle the imbalance in
credit card data. In this scenario, classifiers tend to yield poor accuracy on
the fraud class (minority class) despite realizing high overall accuracy. This
is due to the influence of the majority class on traditional training criteria.
In this paper, we aim to apply genetic programming to address this issue by
adapting existing fitness functions. We examine two fitness functions from
previous studies and develop two new fitness functions to evolve GP classifier
with superior accuracy on the minority class and overall. Two UCI credit card
datasets are used to evaluate the effectiveness of the proposed fitness
functions. The results demonstrate that the proposed fitness functions augment
GP classifiers, encouraging fitter solutions on both the minority and the
majority classes.
UC Merced Submission to the ActivityNet Challenge 2016
Authors: Yi Zhu, Shawn Newsam, Zaikun Xu
Date: 2017-04-11 00:00:00
Tasks: Action Recognition, General Classification, Temporal Action Localization

This notebook paper describes our system for the untrimmed classification
task in the ActivityNet challenge 2016. We investigate multiple
state-of-the-art approaches for action recognition in long, untrimmed videos.
We exploit hand-crafted motion boundary histogram features as well feature
activations from deep networks such as VGG16, GoogLeNet, and C3D. These
features are separately fed to linear, one-versus-rest support vector machine
classifiers to produce confidence scores for each action class. These
predictions are then fused along with the softmax scores of the recent
ultra-deep ResNet-101 using weighted averaging.
Creativity: Generating Diverse Questions using Variational Autoencoders
Authors: Unnat Jain, Ziyu Zhang, Alexander Schwing
Date: 2017-04-11 00:00:00
Proceeding: CVPR 2017 7
Tasks: Question Generation, Question-Generation

Generating diverse questions for given images is an important task for
computational education, entertainment and AI assistants. Different from many
conventional prediction techniques is the need for algorithms to generate a
diverse set of plausible questions, which we refer to as "creativity". In this
paper we propose a creative algorithm for visual question generation which
combines the advantages of variational autoencoders with long short-term memory
networks. We demonstrate that our framework is able to generate a large set of
varying questions given a single input image.
Quaternion Based Camera Pose Estimation From Matched Feature Points
Authors: Kaveh Fathian, J. Pablo Ramirez-Paredes, Emily A. Doucette, J. Willard Curtis, Nicholas R. Gans
Date: 2017-04-09 00:00:00
Tasks: Camera Pose Estimation, Pose Estimation, Translation

We present a novel solution to the camera pose estimation problem, where
rotation and translation of a camera between two views are estimated from
matched feature points in the images. The camera pose estimation problem is
traditionally solved via algorithms that are based on the essential matrix or
the Euclidean homography. With six or more feature points in general positions
in the space, essential matrix based algorithms can recover a unique solution.
However, such algorithms fail when points are on critical surfaces (e.g.,
coplanar points) and homography should be used instead. By formulating the
problem in quaternions and decoupling the rotation and translation estimation,
our proposed algorithm works for all point configurations. Using both simulated
and real world images, we compare the estimation accuracy of our algorithm with
some of the most commonly used algorithms. Our method is shown to be more
robust to noise and outliers. For the benefit of community, we have made the
implementation of our algorithm available online and free.
CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction
Authors: Keisuke Tateno, Federico Tombari, Iro Laina, Nassir Navab
Date: 2017-04-11 00:00:00
Proceeding: CVPR 2017 7
Tasks: Depth Estimation, Depth Prediction, Monocular Reconstruction, Prediction

Given the recent advances in depth prediction from Convolutional Neural
Networks (CNNs), this paper investigates how predicted depth maps from a deep
neural network can be deployed for accurate and dense monocular reconstruction.
We propose a method where CNN-predicted dense depth maps are naturally fused
together with depth measurements obtained from direct monocular SLAM. Our
fusion scheme privileges depth prediction in image locations where monocular
SLAM approaches tend to fail, e.g. along low-textured regions, and vice-versa.
We demonstrate the use of depth prediction for estimating the absolute scale of
the reconstruction, hence overcoming one of the major limitations of monocular
SLAM. Finally, we propose a framework to efficiently fuse semantic labels,
obtained from a single frame, with dense SLAM, yielding semantically coherent
scene reconstruction from a single view. Evaluation results on two benchmark
datasets show the robustness and accuracy of our approach.