pwc-archive/papers-with-abstracts huggingface dataset:
A new approach to automatic disc localization in clinical lumbar MRI
Authors: Subarna Ghosh
Date: 2012-05-02 00:00:00
Proceeding: Localization of the ¨ lumbar discs using machine learning and exact probabilistic inference 2012 5

Lower back pain (LBP) is widely prevalent in people all over the world and negatively affects the quality of life due to chronic pain
and change in posture. Automatic localization of intervertebral discs from lumbar MRI is the first step towards computer-aided diagnosis of lower back ailments. Till date, most of the research has been useful in determining a point within each lumbar disc, hence we go one step further and propose a localization method which outputs a tight bounding box for each disc. We use HOG (Histogram of Oriented Gradients) features along with SVM (Support Vector Machine)as classifier and successfully combine these machine learning techniques with heuristics to achieve 99% disc localization accuracy on 53 clinical cases (318 lumbar discs). We also devise our own metrics to evaluate the accuracy and tightness of our disc bounding box and compare our results with previous research.
Investigating Object Compositionality in Generative Adversarial Networks
Authors: Sjoerd van Steenkiste, Karol Kurach, Jürgen Schmidhuber, Sylvain Gelly
Date: 2018-10-17 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image Generation, Inductive Bias, Instance Segmentation, Object, Representation Learning, Semantic Segmentation, Unsupervised Instance Segmentation

Deep generative models seek to recover the process with which the observed data was generated. They may be used to synthesize new samples or to subsequently extract representations. Successful approaches in the domain of images are driven by several core inductive biases. However, a bias to account for the compositional way in which humans structure a visual scene in terms of objects has frequently been overlooked. In this work, we investigate object compositionality as an inductive bias for Generative Adversarial Networks (GANs). We present a minimal modification of a standard generator to incorporate this inductive bias and find that it reliably learns to generate images as compositions of objects. Using this general design as a backbone, we then propose two useful extensions to incorporate dependencies among objects and background. We extensively evaluate our approach on several multi-object image datasets and highlight the merits of incorporating structure for representation learning purposes. In particular, we find that our structured GANs are better at generating multi-object images that are more faithful to the reference distribution. More so, we demonstrate how, by leveraging the structure of the learned generative process, one can `invert' the learned generative model to perform unsupervised instance segmentation. On the challenging CLEVR dataset, it is shown how our approach is able to improve over other recent purely unsupervised object-centric approaches to image generation.
A Short Survey of Topological Data Analysis in Time Series and Systems Analysis
Authors: Shafie Gholizadeh, Wlodek Zadrozny
Date: 2018-09-27 00:00:00
Tasks: Time Series, Time Series Analysis, Topological Data Analysis

Topological Data Analysis (TDA) is the collection of mathematical tools that
capture the structure of shapes in data. Despite computational topology and
computational geometry, the utilization of TDA in time series and signal
processing is relatively new. In some recent contributions, TDA has been
utilized as an alternative to the conventional signal processing methods.
Specifically, TDA is been considered to deal with noisy signals and time
series. In these applications, TDA is used to find the shapes in data as the
main properties, while the other properties are assumed much less informative.
In this paper, we will review recent developments and contributions where
topological data analysis especially persistent homology has been applied to
time series analysis, dynamical systems and signal processing. We will cover
problem statements such as stability determination, risk analysis, systems
behaviour, and predicting critical transitions in financial markets.
Associatively Segmenting Instances and Semantics in Point Clouds
Authors: Xinlong Wang, Shu Liu, Xiaoyong Shen, Chunhua Shen, Jiaya Jia
Date: 2019-02-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Instance Segmentation, 3D Semantic Segmentation, Instance Segmentation, Segmentation, Semantic Segmentation

A 3D point cloud describes the real scene precisely and intuitively.To date
how to segment diversified elements in such an informative 3D scene is rarely
discussed. In this paper, we first introduce a simple and flexible framework to
segment instances and semantics in point clouds simultaneously. Then, we
propose two approaches which make the two tasks take advantage of each other,
leading to a win-win situation. Specifically, we make instance segmentation
benefit from semantic segmentation through learning semantic-aware point-level
instance embedding. Meanwhile, semantic features of the points belonging to the
same instance are fused together to make more accurate per-point semantic
predictions. Our method largely outperforms the state-of-the-art method in 3D
instance segmentation along with a significant improvement in 3D semantic
segmentation. Code has been made available at:
https://github.com/WXinlong/ASIS.
MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video
Authors: Qunliang Xing, Zhenyu Guan, Mai Xu, Ren Yang, Tie Liu, Zulin Wang
Date: 2019-02-26 00:00:00
Tasks: Video Enhancement, Video Restoration

The past few years have witnessed great success in applying deep learning to enhance the quality of compressed image/video. The existing approaches mainly focus on enhancing the quality of a single frame, not considering the similarity between consecutive frames. Since heavy fluctuation exists across compressed video frames as investigated in this paper, frame similarity can be utilized for quality enhancement of low-quality frames given their neighboring high-quality frames. This task is Multi-Frame Quality Enhancement (MFQE). Accordingly, this paper proposes an MFQE approach for compressed video, as the first attempt in this direction. In our approach, we firstly develop a Bidirectional Long Short-Term Memory (BiLSTM) based detector to locate Peak Quality Frames (PQFs) in compressed video. Then, a novel Multi-Frame Convolutional Neural Network (MF-CNN) is designed to enhance the quality of compressed video, in which the non-PQF and its nearest two PQFs are the input. In MF-CNN, motion between the non-PQF and PQFs is compensated by a motion compensation subnet. Subsequently, a quality enhancement subnet fuses the non-PQF and compensated PQFs, and then reduces the compression artifacts of the non-PQF. Also, PQF quality is enhanced in the same way. Finally, experiments validate the effectiveness and generalization ability of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video. The code is available at https://github.com/RyanXingQL/MFQEv2.0.git.
Disentangled Representation Learning for 3D Face Shape
Authors: Zi-Hang Jiang, Qianyi Wu, Keyu Chen, Juyong Zhang
Date: 2019-02-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: Attribute, Representation Learning

In this paper, we present a novel strategy to design disentangled 3D face
shape representation. Specifically, a given 3D face shape is decomposed into
identity part and expression part, which are both encoded and decoded in a
nonlinear way. To solve this problem, we propose an attribute decomposition
framework for 3D face mesh. To better represent face shapes which are usually
nonlinear deformed between each other, the face shapes are represented by a
vertex based deformation representation rather than Euclidean coordinates. The
experimental results demonstrate that our method has better performance than
existing methods on decomposing the identity and expression parts. Moreover,
more natural expression transfer results can be achieved with our method than
existing methods.
RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation
Authors: Bastian Wandt, Bodo Rosenhahn
Date: 2019-02-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Human Pose Estimation, Memorization, Monocular 3D Human Pose Estimation, Pose Estimation, Weakly-supervised 3D Human Pose Estimation

This paper addresses the problem of 3D human pose estimation from single
images. While for a long time human skeletons were parameterized and fitted to
the observation by satisfying a reprojection error, nowadays researchers
directly use neural networks to infer the 3D pose from the observations.
However, most of these approaches ignore the fact that a reprojection
constraint has to be satisfied and are sensitive to overfitting. We tackle the
overfitting problem by ignoring 2D to 3D correspondences. This efficiently
avoids a simple memorization of the training data and allows for a weakly
supervised training. One part of the proposed reprojection network (RepNet)
learns a mapping from a distribution of 2D poses to a distribution of 3D poses
using an adversarial training approach. Another part of the network estimates
the camera. This allows for the definition of a network layer that performs the
reprojection of the estimated 3D pose back to 2D which results in a
reprojection loss function. Our experiments show that RepNet generalizes well
to unknown data and outperforms state-of-the-art methods when applied to unseen
data. Moreover, our implementation runs in real-time on a standard desktop PC.
Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding
Authors: Zehao Yu, Jia Zheng, Dongze Lian, Zihan Zhou, Shenghua Gao
Date: 2019-02-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Plane Detection, 3D Reconstruction, Clustering, Instance Segmentation, Plane Instance Segmentation, Semantic Segmentation

Single-image piece-wise planar 3D reconstruction aims to simultaneously
segment plane instances and recover 3D plane parameters from an image. Most
recent approaches leverage convolutional neural networks (CNNs) and achieve
promising results. However, these methods are limited to detecting a fixed
number of planes with certain learned order. To tackle this problem, we propose
a novel two-stage method based on associative embedding, inspired by its recent
success in instance segmentation. In the first stage, we train a CNN to map
each pixel to an embedding space where pixels from the same plane instance have
similar embeddings. Then, the plane instances are obtained by grouping the
embedding vectors in planar regions via an efficient mean shift clustering
algorithm. In the second stage, we estimate the parameter for each plane
instance by considering both pixel-level and instance-level consistencies. With
the proposed method, we are able to detect an arbitrary number of planes.
Extensive experiments on public datasets validate the effectiveness and
efficiency of our method. Furthermore, our method runs at 30 fps at the testing
time, thus could facilitate many real-time applications such as visual SLAM and
human-robot interaction. Code is available at
https://github.com/svip-lab/PlanarReconstruction.
Learning a Deep ConvNet for Multi-label Classification with Partial Labels
Authors: Thibaut Durand, Nazanin Mehrasa, Greg Mori
Date: 2019-02-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: Classification, General Classification, image-classification, Image Classification, Missing Labels, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-label Image Recognition with Partial Labels

Deep ConvNets have shown great performance for single-label image
classification (e.g. ImageNet), but it is necessary to move beyond the
single-label classification task because pictures of everyday life are
inherently multi-label. Multi-label classification is a more difficult task
than single-label classification because both the input images and output label
spaces are more complex. Furthermore, collecting clean multi-label annotations
is more difficult to scale-up than single-label annotations. To reduce the
annotation cost, we propose to train a model with partial labels i.e. only some
labels are known per image. We first empirically compare different labeling
strategies to show the potential for using partial labels on multi-label
datasets. Then to learn with partial labels, we introduce a new classification
loss that exploits the proportion of known labels per example. Our approach
allows the use of the same training settings as when learning with all the
annotations. We further explore several curriculum learning based strategies to
predict missing labels. Experiments are performed on three large-scale
multi-label datasets: MS COCO, NUS-WIDE and Open Images.
Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
Authors: Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian Reid, Silvio Savarese
Date: 2019-02-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object, object-detection, Object Detection, regression

Intersection over Union (IoU) is the most popular evaluation metric used in
the object detection benchmarks. However, there is a gap between optimizing the
commonly used distance losses for regressing the parameters of a bounding box
and maximizing this metric value. The optimal objective for a metric is the
metric itself. In the case of axis-aligned 2D bounding boxes, it can be shown
that $IoU$ can be directly used as a regression loss. However, $IoU$ has a
plateau making it infeasible to optimize in the case of non-overlapping
bounding boxes. In this paper, we address the weaknesses of $IoU$ by
introducing a generalized version as both a new loss and a new metric. By
incorporating this generalized $IoU$ ($GIoU$) as a loss into the state-of-the
art object detection frameworks, we show a consistent improvement on their
performance using both the standard, $IoU$ based, and new, $GIoU$ based,
performance measures on popular object detection benchmarks such as PASCAL VOC
and MS COCO.
Stereo R-CNN based 3D Object Detection for Autonomous Driving
Authors: Peiliang Li, Xiaozhi Chen, Shaojie Shen
Date: 2019-02-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Object Detection, 3D Object Detection From Stereo Images, Autonomous Driving, Object, object-detection, Object Detection, Region Proposal

We propose a 3D object detection method for autonomous driving by fully
exploiting the sparse and dense, semantic and geometry information in stereo
imagery. Our method, called Stereo R-CNN, extends Faster R-CNN for stereo
inputs to simultaneously detect and associate object in left and right images.
We add extra branches after stereo Region Proposal Network (RPN) to predict
sparse keypoints, viewpoints, and object dimensions, which are combined with 2D
left-right boxes to calculate a coarse 3D object bounding box. We then recover
the accurate 3D bounding box by a region-based photometric alignment using left
and right RoIs. Our method does not require depth input and 3D position
supervision, however, outperforms all existing fully supervised image-based
methods. Experiments on the challenging KITTI dataset show that our method
outperforms the state-of-the-art stereo-based method by around 30% AP on both
3D detection and 3D localization tasks. Code has been released at
https://github.com/HKUST-Aerial-Robotics/Stereo-RCNN.
Image-Question-Answer Synergistic Network for Visual Dialog
Authors: Dalu Guo, Chang Xu, DaCheng Tao
Date: 2019-02-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: Visual Dialog

The image, question (combined with the history for de-referencing), and the
corresponding answer are three vital components of visual dialog. Classical
visual dialog systems integrate the image, question, and history to search for
or generate the best matched answer, and so, this approach significantly
ignores the role of the answer. In this paper, we devise a novel
image-question-answer synergistic network to value the role of the answer for
precise visual dialog. We extend the traditional one-stage solution to a
two-stage solution. In the first stage, candidate answers are coarsely scored
according to their relevance to the image and question pair. Afterward, in the
second stage, answers with high probability of being correct are re-ranked by
synergizing with image and question. On the Visual Dialog v1.0 dataset, the
proposed synergistic network boosts the discriminative visual dialog model to
achieve a new state-of-the-art of 57.88\% normalized discounted cumulative
gain. A generative visual dialog model equipped with the proposed technique
also shows promising improvements.
Transfer Learning for Performance Modeling of Configurable Systems: A Causal Analysis
Authors: Mohammad Ali Javidian, Pooyan Jamshidi, Marco Valtorta
Date: 2019-02-26 00:00:00
Tasks: Transfer Learning

Modern systems (e.g., deep neural networks, big data analytics, and
compilers) are highly configurable, which means they expose different
performance behavior under different configurations. The fundamental challenge
is that one cannot simply measure all configurations due to the sheer size of
the configuration space. Transfer learning has been used to reduce the
measurement efforts by transferring knowledge about performance behavior of
systems across environments. Previously, research has shown that statistical
models are indeed transferable across environments. In this work, we
investigate identifiability and transportability of causal effects and
statistical relations in highly-configurable systems. Our causal analysis
agrees with previous exploratory analysis \cite{Jamshidi17} and confirms that
the causal effects of configuration options can be carried over across
environments with high confidence. We expect that the ability to carry over
causal relations will enable effective performance analysis of
highly-configurable systems.
Multi-Task Learning with Contextualized Word Representations for Extented Named Entity Recognition
Authors: Thai-Hoang Pham, Khai Mai, Nguyen Minh Trung, Nguyen Tuan Duc, Danushka Bolegala, Ryohei Sasano, Satoshi Sekine
Date: 2019-02-26 00:00:00
Tasks: Multi-Task Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Fine-Grained Named Entity Recognition (FG-NER) is critical for many NLP
applications. While classical named entity recognition (NER) has attracted a
substantial amount of research, FG-NER is still an open research domain. The
current state-of-the-art (SOTA) model for FG-NER relies heavily on manual
efforts for building a dictionary and designing hand-crafted features. The
end-to-end framework which achieved the SOTA result for NER did not get the
competitive result compared to SOTA model for FG-NER. In this paper, we
investigate how effective multi-task learning approaches are in an end-to-end
framework for FG-NER in different aspects. Our experiments show that using
multi-task learning approaches with contextualized word representation can help
an end-to-end neural network model achieve SOTA results without using any
additional manual effort for creating data and designing features.
Utterance-level Aggregation For Speaker Recognition In The Wild
Authors: Weidi Xie, Arsha Nagrani, Joon Son Chung, Andrew Zisserman
Date: 2019-02-26 00:00:00
Tasks: Speaker Recognition, Text-Independent Speaker Verification

The objective of this paper is speaker recognition "in the wild"-where utterances may be of variable length and also contain irrelevant signals. Crucial elements in the design of deep networks for this task are the type of trunk (frame level) network, and the method of temporal aggregation. We propose a powerful speaker recognition deep network, using a "thin-ResNet" trunk architecture, and a dictionary-based NetVLAD or GhostVLAD layer to aggregate features across time, that can be trained end-to-end. We show that our network achieves state of the art performance by a significant margin on the VoxCeleb1 test set for speaker recognition, whilst requiring fewer parameters than previous methods. We also investigate the effect of utterance length on performance, and conclude that for "in the wild" data, a longer length is beneficial.
A multimodal movie review corpus for fine-grained opinion mining
Authors: Alexandre Garcia, Slim Essid, Florence d'Alché-Buc, Chloé Clavel
Date: 2019-02-26 00:00:00
Tasks: Opinion Mining, Prediction

In this paper, we introduce a set of opinion annotations for the POM movie review dataset, composed of 1000 videos. The annotation campaign is motivated by the development of a hierarchical opinion prediction framework allowing one to predict the different components of the opinions (e.g. polarity and aspect) and to identify the corresponding textual spans. The resulting annotations have been gathered at two granularity levels: a coarse one (opinionated span) and a finer one (span of opinion components). We introduce specific categories in order to make the annotation of opinions easier for movie reviews. For example, some categories allow the discovery of user recommendation and preference in movie reviews. We provide a quantitative analysis of the annotations and report the inter-annotator agreement under the different levels of granularity. We provide thus the first set of ground-truth annotations which can be used for the task of fine-grained multimodal opinion prediction. We provide an analysis of the data gathered through an inter-annotator study and show that a linear structured predictor learns meaningful features even for the prediction of scarce labels. Both the annotations and the baseline system are made publicly available. https://github.com/eusip/POM/
SceneFlowFields++: Multi-frame Matching, Visibility Prediction, and Robust Interpolation for Scene Flow Estimation
Authors: René Schuster, Oliver Wasenmüller, Christian Unger, Georg Kuschk, Didier Stricker
Date: 2019-02-26 00:00:00
Tasks: Scene Flow Estimation

State-of-the-art scene flow algorithms pursue the conflicting targets of accuracy, run time, and robustness. With the successful concept of pixel-wise matching and sparse-to-dense interpolation, we push the limits of scene flow estimation. Avoiding strong assumptions on the domain or the problem yields a more robust algorithm. This algorithm is fast because we avoid explicit regularization during matching, which allows an efficient computation. Using image information from multiple time steps and explicit visibility prediction based on previous results, we achieve competitive performances on different data sets. Our contributions and results are evaluated in comparative experiments. Overall, we present an accurate scene flow algorithm that is faster and more generic than any individual benchmark leader.
Perturbed-History Exploration in Stochastic Multi-Armed Bandits
Authors: Branislav Kveton, Csaba Szepesvari, Mohammad Ghavamzadeh, Craig Boutilier
Date: 2019-02-26 00:00:00
Tasks: Multi-Armed Bandits

We propose an online algorithm for cumulative regret minimization in a stochastic multi-armed bandit. The algorithm adds $O(t)$ i.i.d. pseudo-rewards to its history in round $t$ and then pulls the arm with the highest average reward in its perturbed history. Therefore, we call it perturbed-history exploration (PHE). The pseudo-rewards are carefully designed to offset potentially underestimated mean rewards of arms with a high probability. We derive near-optimal gap-dependent and gap-free bounds on the $n$-round regret of PHE. The key step in our analysis is a novel argument that shows that randomized Bernoulli rewards lead to optimism. Finally, we empirically evaluate PHE and show that it is competitive with state-of-the-art baselines.