pwc-archive/papers-with-abstracts huggingface dataset:
3D Bounding Box Estimation Using Deep Learning and Geometry
Authors: Arsalan Mousavian, Dragomir Anguelov, John Flynn, Jana Kosecka
Date: 2016-12-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Object Detection, Deep Learning, Object, object-detection, Object Detection, Pose Estimation, Semantic Segmentation, Vehicle Pose Estimation, Viewpoint Estimation

We present a method for 3D object detection and pose estimation from a single
image. In contrast to current techniques that only regress the 3D orientation
of an object, our method first regresses relatively stable 3D object properties
using a deep convolutional neural network and then combines these estimates
with geometric constraints provided by a 2D object bounding box to produce a
complete 3D bounding box. The first network output estimates the 3D object
orientation using a novel hybrid discrete-continuous loss, which significantly
outperforms the L2 loss. The second output regresses the 3D object dimensions,
which have relatively little variance compared to alternatives and can often be
predicted for many object types. These estimates, combined with the geometric
constraints on translation imposed by the 2D bounding box, enable us to recover
a stable and accurate 3D object pose. We evaluate our method on the challenging
KITTI object detection benchmark both on the official metric of 3D orientation
estimation and also on the accuracy of the obtained 3D bounding boxes. Although
conceptually simple, our method outperforms more complex and computationally
expensive approaches that leverage semantic segmentation, instance level
segmentation and flat ground priors and sub-category detection. Our
discrete-continuous loss also produces state of the art results for 3D
viewpoint estimation on the Pascal 3D+ dataset.
Automatic semantic role labeling on non-revised syntactic trees of journalistic texts
Authors: Nathan Siegle Hartmann, Magali Sanches Duran, Sandra Maria Aluísio
Date: 2017-04-10 00:00:00
Tasks: Semantic Role Labeling

Semantic Role Labeling (SRL) is a Natural Language Processing task that
enables the detection of events described in sentences and the participants of
these events. For Brazilian Portuguese (BP), there are two studies recently
concluded that perform SRL in journalistic texts. [1] obtained F1-measure
scores of 79.6, using the PropBank.Br corpus, which has syntactic trees
manually revised, [8], without using a treebank for training, obtained
F1-measure scores of 68.0 for the same corpus. However, the use of manually
revised syntactic trees for this task does not represent a real scenario of
application. The goal of this paper is to evaluate the performance of SRL on
revised and non-revised syntactic trees using a larger and balanced corpus of
BP journalistic texts. First, we have shown that [1]'s system also performs
better than [8]'s system on the larger corpus. Second, the SRL system trained
on non-revised syntactic trees performs better over non-revised trees than a
system trained on gold-standard data.
Automatic Classification of the Complexity of Nonfiction Texts in Portuguese for Early School Years
Authors: Nathan Siegle Hartmann, Livia Cucatto, Danielle Brants, Sandra Aluísio
Date: 2017-04-10 00:00:00
Tasks: Blocking, General Classification, Reading Comprehension

Recent research shows that most Brazilian students have serious problems
regarding their reading skills. The full development of this skill is key for
the academic and professional future of every citizen. Tools for classifying
the complexity of reading materials for children aim to improve the quality of
the model of teaching reading and text comprehension. For English, Fengs work
[11] is considered the state-of-art in grade level prediction and achieved 74%
of accuracy in automatically classifying 4 levels of textual complexity for
close school grades. There are no classifiers for nonfiction texts for close
grades in Portuguese. In this article, we propose a scheme for manual
annotation of texts in 5 grade levels, which will be used for customized
reading to avoid the lack of interest by students who are more advanced in
reading and the blocking of those that still need to make further progress. We
obtained 52% of accuracy in classifying texts into 5 levels and 74% in 3
levels. The results prove to be promising when compared to the state-of-art
work.9
Stochastic Neural Networks for Hierarchical Reinforcement Learning
Authors: Carlos Florensa, Yan Duan, Pieter Abbeel
Date: 2017-04-10 00:00:00
Tasks: Deep Reinforcement Learning, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning has achieved many impressive results in recent
years. However, tasks with sparse rewards or long horizons continue to pose
significant challenges. To tackle these important problems, we propose a
general framework that first learns useful skills in a pre-training
environment, and then leverages the acquired skills for learning faster in
downstream tasks. Our approach brings together some of the strengths of
intrinsic motivation and hierarchical methods: the learning of useful skill is
guided by a single proxy reward, the design of which requires very minimal
domain knowledge about the downstream tasks. Then a high-level policy is
trained on top of these skills, providing a significant improvement of the
exploration and allowing to tackle sparse rewards in the downstream tasks. To
efficiently pre-train a large span of skills, we use Stochastic Neural Networks
combined with an information-theoretic regularizer. Our experiments show that
this combination is effective in learning a wide span of interpretable skills
in a sample-efficient way, and can significantly boost the learning performance
uniformly across a wide range of downstream tasks.
Automated Curriculum Learning for Neural Networks
Authors: Alex Graves, Marc G. Bellemare, Jacob Menick, Remi Munos, Koray Kavukcuoglu
Date: 2017-04-10 00:00:00
Proceeding: ICML 2017 8

We introduce a method for automatically selecting the path, or syllabus, that
a neural network follows through a curriculum so as to maximise learning
efficiency. A measure of the amount that the network learns from each data
sample is provided as a reward signal to a nonstationary multi-armed bandit
algorithm, which then determines a stochastic syllabus. We consider a range of
signals derived from two distinct indicators of learning progress: rate of
increase in prediction accuracy, and rate of increase in network complexity.
Experimental results for LSTM networks on three curricula demonstrate that our
approach can significantly accelerate learning, in some cases halving the time
required to attain a satisfactory performance level.
Weakly-Supervised Spatial Context Networks
Authors: Zuxuan Wu, Larry S. Davis, Leonid Sigal
Date: 2017-04-10 00:00:00
Tasks: Object, Object Categorization

We explore the power of spatial context as a self-supervisory signal for
learning visual representations. In particular, we propose spatial context
networks that learn to predict a representation of one image patch from another
image patch, within the same image, conditioned on their real-valued relative
spatial offset. Unlike auto-encoders, that aim to encode and reconstruct
original image patches, our network aims to encode and reconstruct intermediate
representations of the spatially offset patches. As such, the network learns a
spatially conditioned contextual representation. By testing performance with
various patch selection mechanisms we show that focusing on object-centric
patches is important, and that using object proposal as a patch selection
mechanism leads to the highest improvement in performance. Further, unlike
auto-encoders, context encoders [21], or other forms of unsupervised feature
learning, we illustrate that contextual supervision (with pre-trained model
initialization) can improve on existing pre-trained model performance. We build
our spatial context networks on top of standard VGG_19 and CNN_M architectures
and, among other things, show that we can achieve improvements (with no
additional explicit supervision) over the original ImageNet pre-trained VGG_19
and CNN_M models in object categorization and detection on VOC2007.
ECO: Efficient Convolution Operators for Tracking
Authors: Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg
Date: 2016-11-28 00:00:00
Proceeding: CVPR 2017 7
Tasks: CPU, Diversity, Visual Object Tracking

In recent years, Discriminative Correlation Filter (DCF) based methods have
significantly advanced the state-of-the-art in tracking. However, in the
pursuit of ever increasing tracking performance, their characteristic speed and
real-time capability have gradually faded. Further, the increasingly complex
models, with massive number of trainable parameters, have introduced the risk
of severe over-fitting. In this work, we tackle the key causes behind the
problems of computational complexity and over-fitting, with the aim of
simultaneously improving both speed and performance.
  We revisit the core DCF formulation and introduce: (i) a factorized
convolution operator, which drastically reduces the number of parameters in the
model; (ii) a compact generative model of the training sample distribution,
that significantly reduces memory and time complexity, while providing better
diversity of samples; (iii) a conservative model update strategy with improved
robustness and reduced complexity. We perform comprehensive experiments on four
benchmarks: VOT2016, UAV123, OTB-2015, and TempleColor. When using expensive
deep features, our tracker provides a 20-fold speedup and achieves a 13.0%
relative gain in Expected Average Overlap compared to the top ranked method in
the VOT2016 challenge. Moreover, our fast variant, using hand-crafted features,
operates at 60 Hz on a single CPU, while obtaining 65.0% AUC on OTB-2015.
A Hierarchical Approach for Generating Descriptive Image Paragraphs
Authors: Jonathan Krause, Justin Johnson, Ranjay Krishna, Li Fei-Fei
Date: 2016-11-20 00:00:00
Proceeding: CVPR 2017 7
Tasks: Dense Captioning, Descriptive, Image Captioning, Image Paragraph Captioning, Sentence

Recent progress on image captioning has made it possible to generate novel
sentences describing images in natural language, but compressing an image into
a single sentence can describe visual content in only coarse detail. While one
new captioning approach, dense captioning, can potentially describe images in
finer levels of detail by captioning many regions within an image, it in turn
is unable to produce a coherent story for an image. In this paper we overcome
these limitations by generating entire paragraphs for describing images, which
can tell detailed, unified stories. We develop a model that decomposes both
images and paragraphs into their constituent parts, detecting semantic regions
in images and using a hierarchical recurrent neural network to reason about
language. Linguistic analysis confirms the complexity of the paragraph
generation task, and thorough experiments on a new dataset of image and
paragraph pairs demonstrate the effectiveness of our approach.
Loss Max-Pooling for Semantic Image Segmentation
Authors: Samuel Rota Bulò, Gerhard Neuhold, Peter Kontschieder
Date: 2017-04-10 00:00:00
Proceeding: CVPR 2017 7
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

We introduce a novel loss max-pooling concept for handling imbalanced
training data distributions, applicable as alternative loss layer in the
context of deep neural networks for semantic image segmentation. Most
real-world semantic segmentation datasets exhibit long tail distributions with
few object categories comprising the majority of data and consequently biasing
the classifiers towards them. Our method adaptively re-weights the
contributions of each pixel based on their observed losses, targeting
under-performing classification results as often encountered for
under-represented object classes. Our approach goes beyond conventional
cost-sensitive learning attempts through adaptive considerations that allow us
to indirectly address both, inter- and intra-class imbalances. We provide a
theoretical justification of our approach, complementary to experimental
analyses on benchmark datasets. In our experiments on the Cityscapes and Pascal
VOC 2012 segmentation datasets we find consistently improved results,
demonstrating the efficacy of our approach.
Exploring Word Embeddings for Unsupervised Textual User-Generated Content Normalization
Authors: Thales Felipe Costa Bertaglia, Maria das Graças Volpe Nunes
Date: 2017-04-10 00:00:00
Proceeding: WS 2016 12
Tasks: Semantic Similarity, Semantic Textual Similarity, Text Normalization, Word Embeddings

Text normalization techniques based on rules, lexicons or supervised training
requiring large corpora are not scalable nor domain interchangeable, and this
makes them unsuitable for normalizing user-generated content (UGC). Current
tools available for Brazilian Portuguese make use of such techniques. In this
work we propose a technique based on distributed representation of words (or
word embeddings). It generates continuous numeric vectors of
high-dimensionality to represent words. The vectors explicitly encode many
linguistic regularities and patterns, as well as syntactic and semantic word
relationships. Words that share semantic similarity are represented by similar
vectors. Based on these features, we present a totally unsupervised, expandable
and language and domain independent method for learning normalization lexicons
from word embeddings. Our approach obtains high correction rate of orthographic
errors and internet slang in product reviews, outperforming the current
available tools for Brazilian Portuguese.
VRFP: On-the-fly Video Retrieval using Web Images and Fast Fisher Vector Products
Authors: Xintong Han, Bharat Singh, Vlad I. Morariu, Larry S. Davis
Date: 2015-12-10 00:00:00
Tasks: Re-Ranking, Retrieval, Video Retrieval, Zero-Shot Learning

VRFP is a real-time video retrieval framework based on short text input
queries, which obtains weakly labeled training images from the web after the
query is known. The retrieved web images representing the query and each
database video are treated as unordered collections of images, and each
collection is represented using a single Fisher Vector built on CNN features.
Our experiments show that a Fisher Vector is robust to noise present in web
images and compares favorably in terms of accuracy to other standard
representations. While a Fisher Vector can be constructed efficiently for a new
query, matching against the test set is slow due to its high dimensionality. To
perform matching in real-time, we present a lossless algorithm that accelerates
the inner product computation between high dimensional Fisher Vectors. We prove
that the expected number of multiplications required decreases quadratically
with the sparsity of Fisher Vectors. We are not only able to construct and
apply query models in real-time, but with the help of a simple re-ranking
scheme, we also outperform state-of-the-art automatic retrieval methods by a
significant margin on TRECVID MED13 (3.5%), MED14 (1.3%) and CCV datasets
(5.2%). We also provide a direct comparison on standard datasets between two
different paradigms for automatic video retrieval - zero-shot learning and
on-the-fly retrieval.
Riemannian stochastic variance reduced gradient algorithm with retraction and vector transport
Authors: Hiroyuki Sato, Hiroyuki Kasai, Bamdev Mishra
Date: 2017-02-18 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion, Riemannian optimization

In recent years, stochastic variance reduction algorithms have attracted considerable attention for minimizing the average of a large but finite number of loss functions. This paper proposes a novel Riemannian extension of the Euclidean stochastic variance reduced gradient (R-SVRG) algorithm to a manifold search space. The key challenges of averaging, adding, and subtracting multiple gradients are addressed with retraction and vector transport. For the proposed algorithm, we present a global convergence analysis with a decaying step size as well as a local convergence rate analysis with a fixed step size under some natural assumptions. In addition, the proposed algorithm is applied to the computation problem of the Riemannian centroid on the symmetric positive definite (SPD) manifold as well as the principal component analysis and low-rank matrix completion problems on the Grassmann manifold. The results show that the proposed algorithm outperforms the standard Riemannian stochastic gradient descent algorithm in each case.
On the Fine-Grained Complexity of Empirical Risk Minimization: Kernel Methods and Neural Networks
Authors: Arturs Backurs, Piotr Indyk, Ludwig Schmidt
Date: 2017-04-10 00:00:00
Proceeding: NeurIPS 2017 12

Empirical risk minimization (ERM) is ubiquitous in machine learning and
underlies most supervised learning methods. While there has been a large body
of work on algorithms for various ERM problems, the exact computational
complexity of ERM is still not understood. We address this issue for multiple
popular ERM problems including kernel SVMs, kernel ridge regression, and
training the final layer of a neural network. In particular, we give
conditional hardness results for these problems based on complexity-theoretic
assumptions such as the Strong Exponential Time Hypothesis. Under these
assumptions, we show that there are no algorithms that solve the aforementioned
ERM problems to high accuracy in sub-quadratic time. We also give similar
hardness results for computing the gradient of the empirical loss, which is the
main computational burden in many non-convex learning tasks.
Surface Normals in the Wild
Authors: Weifeng Chen, Donglai Xiang, Jia Deng
Date: 2017-04-10 00:00:00
Proceeding: ICCV 2017 10
Tasks: Depth Estimation

We study the problem of single-image depth estimation for images in the wild.
We collect human annotated surface normals and use them to train a neural
network that directly predicts pixel-wise depth. We propose two novel loss
functions for training with surface normal annotations. Experiments on NYU
Depth and our own dataset demonstrate that our approach can significantly
improve the quality of depth estimation in the wild.
ViP-CNN: Visual Phrase Guided Convolutional Neural Network
Authors: Yikang Li, Wanli Ouyang, Xiaogang Wang, Xiao'ou Tang
Date: 2017-02-23 00:00:00
Proceeding: CVPR 2017 7
Tasks: Descriptive, Image Captioning, object-detection, Object Detection, Relationship Detection, Triplet, Visual Relationship Detection

As the intermediate level task connecting image captioning and object
detection, visual relationship detection started to catch researchers'
attention because of its descriptive power and clear structure. It detects the
objects and captures their pair-wise interactions with a
subject-predicate-object triplet, e.g. person-ride-horse. In this paper, each
visual relationship is considered as a phrase with three components. We
formulate the visual relationship detection as three inter-connected
recognition problems and propose a Visual Phrase guided Convolutional Neural
Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a
Phrase-guided Message Passing Structure (PMPS) to establish the connection
among relationship components and help the model consider the three problems
jointly. Corresponding non-maximum suppression method and model training
strategy are also proposed. Experimental results show that our ViP-CNN
outperforms the state-of-art method both in speed and accuracy. We further
pretrain ViP-CNN on our cleansed Visual Genome Relationship dataset, which is
found to perform better than the pretraining on the ImageNet for this task.
Sparse Autoencoder for Unsupervised Nucleus Detection and Representation in Histopathology Images
Authors: Le Hou, Vu Nguyen, Dimitris Samaras, Tahsin M. Kurc, Yi Gao, Tianhao Zhao, Joel H. Saltz
Date: 2017-04-03 00:00:00
Tasks: Prognosis

Histopathology images are crucial to the study of complex diseases such as
cancer. The histologic characteristics of nuclei play a key role in disease
diagnosis, prognosis and analysis. In this work, we propose a sparse
Convolutional Autoencoder (CAE) for fully unsupervised, simultaneous nucleus
detection and feature extraction in histopathology tissue images. Our CAE
detects and encodes nuclei in image patches in tissue images into sparse
feature maps that encode both the location and appearance of nuclei. Our CAE is
the first unsupervised detection network for computer vision applications. The
pretrained nucleus detection and feature extraction modules in our CAE can be
fine-tuned for supervised learning in an end-to-end fashion. We evaluate our
method on four datasets and reduce the errors of state-of-the-art methods up to
42%. We are able to achieve comparable performance with only 5% of the
fully-supervised annotation cost.
SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays
Authors: Wei Dai, Joseph Doyle, Xiaodan Liang, Hao Zhang, Nanqing Dong, Yuan Li, Eric P. Xing
Date: 2017-03-26 00:00:00
Tasks: Organ Segmentation, Segmentation

Chest X-ray (CXR) is one of the most commonly prescribed medical imaging
procedures, often with over 2-10x more scans than other imaging modalities such
as MRI, CT scan, and PET scans. These voluminous CXR scans place significant
workloads on radiologists and medical practitioners. Organ segmentation is a
crucial step to obtain effective computer-aided detection on CXR. In this work,
we propose Structure Correcting Adversarial Network (SCAN) to segment lung
fields and the heart in CXR images. SCAN incorporates a critic network to
impose on the convolutional segmentation network the structural regularities
emerging from human physiology. During training, the critic network learns to
discriminate between the ground truth organ annotations from the masks
synthesized by the segmentation network. Through this adversarial process the
critic network learns the higher order structures and guides the segmentation
model to achieve realistic segmentation outcomes. Extensive experiments show
that our method produces highly accurate and natural segmentation. Using only
very limited training data available, our model reaches human-level performance
without relying on any existing trained model or dataset. Our method also
generalizes well to CXR images from a different patient population and disease
profiles, surpassing the current state-of-the-art.
Pay Attention to Those Sets! Learning Quantification from Images
Authors: Ionut Sorodoc, Sandro Pezzelle, Aurélie Herbelot, Mariella Dimiccoli, Raffaella Bernardi
Date: 2017-04-10 00:00:00

Major advances have recently been made in merging language and vision
representations. But most tasks considered so far have confined themselves to
the processing of objects and lexicalised relations amongst objects (content
words). We know, however, that humans (even pre-school children) can abstract
over raw data to perform certain types of higher-level reasoning, expressed in
natural language by function words. A case in point is given by their ability
to learn quantifiers, i.e. expressions like 'few', 'some' and 'all'. From
formal semantics and cognitive linguistics, we know that quantifiers are
relations over sets which, as a simplification, we can see as proportions. For
instance, in 'most fish are red', most encodes the proportion of fish which are
red fish. In this paper, we study how well current language and vision
strategies model such relations. We show that state-of-the-art attention
mechanisms coupled with a traditional linguistic formalisation of quantifiers
gives best performance on the task. Additionally, we provide insights on the
role of 'gist' representations in quantification. A 'logical' strategy to
tackle the task would be to first obtain a numerosity estimation for the two
involved sets and then compare their cardinalities. We however argue that
precisely identifying the composition of the sets is not only beyond current
state-of-the-art models but perhaps even detrimental to a task that is most
efficiently performed by refining the approximate numerosity estimator of the
system.