pwc-archive/papers-with-abstracts huggingface dataset:
Multiview Hessian Regularization for Image Annotation
Authors: Weifeng Liu, DaCheng Tao
Date: 2019-04-23 00:00:00
Tasks: General Classification

The rapid development of computer hardware and Internet technology makes
large scale data dependent models computationally tractable, and opens a bright
avenue for annotating images through innovative machine learning algorithms.
Semi-supervised learning (SSL) has consequently received intensive attention in
recent years and has been successfully deployed in image annotation. One
representative work in SSL is Laplacian regularization (LR), which smoothes the
conditional distribution for classification along the manifold encoded in the
graph Laplacian, however, it has been observed that LR biases the
classification function towards a constant function which possibly results in
poor generalization. In addition, LR is developed to handle uniformly
distributed data (or single view data), although instances or objects, such as
images and videos, are usually represented by multiview features, such as
color, shape and texture. In this paper, we present multiview Hessian
regularization (mHR) to address the above two problems in LR-based image
annotation. In particular, mHR optimally combines multiple Hessian
regularizations, each of which is obtained from a particular view of instances,
and steers the classification function which varies linearly along the data
manifold. We apply mHR to kernel least squares and support vector machines as
two examples for image annotation. Extensive experiments on the PASCAL VOC'07
dataset validate the effectiveness of mHR by comparing it with baseline
algorithms, including LR and HR.
DAG-GNN: DAG Structure Learning with Graph Neural Networks
Authors: Yue Yu, Jie Chen, Tian Gao, Mo Yu
Date: 2019-04-22 00:00:00
Tasks: Graph Neural Network

Learning a faithful directed acyclic graph (DAG) from samples of a joint
distribution is a challenging combinatorial problem, owing to the intractable
search space superexponential in the number of graph nodes. A recent
breakthrough formulates the problem as a continuous optimization with a
structural constraint that ensures acyclicity (Zheng et al., 2018). The authors
apply the approach to the linear structural equation model (SEM) and the
least-squares loss function that are statistically well justified but
nevertheless limited. Motivated by the widespread success of deep learning that
is capable of capturing complex nonlinear mappings, in this work we propose a
deep generative model and apply a variant of the structural constraint to learn
the DAG. At the heart of the generative model is a variational autoencoder
parameterized by a novel graph neural network architecture, which we coin
DAG-GNN. In addition to the richer capacity, an advantage of the proposed model
is that it naturally handles discrete variables as well as vector-valued ones.
We demonstrate that on synthetic data sets, the proposed method learns more
accurate graphs for nonlinearly generated samples; and on benchmark data sets
with discrete variables, the learned graphs are reasonably close to the global
optima. The code is available at \url{https://github.com/fishmoon1234/DAG-GNN}.
DirectShape: Direct Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation
Authors: Rui Wang, Nan Yang, Joerg Stueckler, Daniel Cremers
Date: 2019-04-22 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, Object Detection, Pose Estimation, Scene Understanding

Scene understanding from images is a challenging problem encountered in autonomous driving. On the object level, while 2D methods have gradually evolved from computing simple bounding boxes to delivering finer grained results like instance segmentations, the 3D family is still dominated by estimating 3D bounding boxes. In this paper, we propose a novel approach to jointly infer the 3D rigid-body poses and shapes of vehicles from a stereo image pair using shape priors. Unlike previous works that geometrically align shapes to point clouds from dense stereo reconstruction, our approach works directly on images by combining a photometric and a silhouette alignment term in the energy function. An adaptive sparse point selection scheme is proposed to efficiently measure the consistency with both terms. In experiments, we show superior performance of our method on 3D pose and shape estimation over the previous geometric approach and demonstrate that our method can also be applied as a refinement step and significantly boost the performances of several state-of-the-art deep learning based 3D object detectors. All related materials and demonstration videos are available at the project page https://vision.in.tum.de/research/vslam/direct-shape.
Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning, Extended version
Authors: Erwan Lecarpentier, Emmanuel Rachelson
Date: 2019-04-22 00:00:00
Tasks: Model-based Reinforcement Learning, Reinforcement Learning

This work tackles the problem of robust zero-shot planning in non-stationary stochastic environments. We study Markov Decision Processes (MDPs) evolving over time and consider Model-Based Reinforcement Learning algorithms in this setting. We make two hypotheses: 1) the environment evolves continuously with a bounded evolution rate; 2) a current model is known at each decision epoch but not its evolution. Our contribution can be presented in four points. 1) we define a specific class of MDPs that we call Non-Stationary MDPs (NSMDPs). We introduce the notion of regular evolution by making an hypothesis of Lipschitz-Continuity on the transition and reward functions w.r.t. time; 2) we consider a planning agent using the current model of the environment but unaware of its future evolution. This leads us to consider a worst-case method where the environment is seen as an adversarial agent; 3) following this approach, we propose the Risk-Averse Tree-Search (RATS) algorithm, a zero-shot Model-Based method similar to Minimax search; 4) we illustrate the benefits brought by RATS empirically and compare its performance with reference Model-Based algorithms.
Tertiary Eye Movement Classification by a Hybrid Algorithm
Authors: Samuel-Hunter Berndt, Douglas Kirkpatrick, Timothy Taviano, Oleg Komogortsev
Date: 2019-04-22 00:00:00
Tasks: Classification, General Classification

The proper classification of major eye movements, saccades, fixations, and
smooth pursuits, remains essential to utilizing eye-tracking data. There is
difficulty in separating out smooth pursuits from the other behavior types,
particularly from fixations. To this end, we propose a new offline algorithm,
I-VDT-HMM, for tertiary classification of eye movements. The algorithm combines
the simplicity of two foundational algorithms, I-VT and I-DT, as has been
implemented in I-VDT, with the statistical predictive power of the Viterbi
algorithm. We evaluate the fitness across a dataset of eight eye movement
records at eight sampling rates gathered from previous research, with a
comparison to the current state-of-the-art using the proposed quantitative and
qualitative behavioral scores. The proposed algorithm achieves promising
results in clean high sampling frequency data and with slight modifications
could show similar results with lower quality data. Though, the statistical
aspect of the algorithm comes at a cost of classification time.
Adaptive Transform Domain Image Super-resolution Via Orthogonally Regularized Deep Networks
Authors: Tiantong Guo, Hojjat S. Mousavi, Vishal Monga
Date: 2019-04-22 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Deep learning methods, in particular, trained Convolutional Neural Networks
(CNN) have recently been shown to produce compelling results for single image
Super-Resolution (SR). Invariably, a CNN is learned to map the Low Resolution
(LR) image to its corresponding High Resolution (HR) version in the spatial
domain. We propose a novel network structure for learning the SR mapping
function in an image transform domain, specifically the Discrete Cosine
Transform (DCT). As the first contribution, we show that DCT can be integrated
into the network structure as a Convolutional DCT (CDCT) layer. With the CDCT
layer, we construct the DCT Deep SR (DCT-DSR) network. We further extend the
DCT-DSR to allow the CDCT layer to become trainable (i.e., optimizable).
Because this layer represents an image transform, we enforce pairwise
orthogonality constraints and newly formulated complexity order constraints on
the individual basis functions/filters. This Orthogonally Regularized Deep SR
network (ORDSR) simplifies the SR task by taking advantage of image transform
domain while adapting the design of transform basis to the training image set.
Experimental results show ORDSR achieves state-of-the-art SR image quality with
fewer parameters than most of the deep CNN methods. A particular success of
ORDSR is in overcoming the artifacts introduced by bicubic interpolation. A key
burden of deep SR has been identified as the requirement of generous training
LR and HR image pairs; ORSDR exhibits a much more graceful degradation as
training size is reduced with significant benefits in the regime of limited
training. Analysis of memory and computation requirements confirms that ORDSR
can allow for a more efficient network with faster inference.
The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors
Authors: William H. Guss, Cayden Codel, Katja Hofmann, Brandon Houghton, Noboru Kuno, Stephanie Milani, Sharada Mohanty, Diego Perez Liebana, Ruslan Salakhutdinov, Nicholay Topin, Manuela Veloso, Phillip Wang
Date: 2019-04-22 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Efficient Exploration, Minecraft, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. As state-of-the-art reinforcement learning (RL) systems require an exponentially increasing number of samples, their development is restricted to a continually shrinking segment of the AI community. Likewise, many of these systems cannot be applied to real-world problems, where environment samples are expensive. Resolution of these limitations requires new, sample-efficient methods. To facilitate research in this direction, we introduce the MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors. The primary goal of the competition is to foster the development of algorithms which can efficiently leverage human demonstrations to drastically reduce the number of samples needed to solve complex, hierarchical, and sparse environments. To that end, we introduce: (1) the Minecraft ObtainDiamond task, a sequential decision making environment requiring long-term planning, hierarchical control, and efficient exploration methods; and (2) the MineRL-v0 dataset, a large-scale collection of over 60 million state-action pairs of human demonstrations that can be resimulated into embodied trajectories with arbitrary modifications to game state and visuals. Participants will compete to develop systems which solve the ObtainDiamond task with a limited number of samples from the environment simulator, Malmo. The competition is structured into two rounds in which competitors are provided several paired versions of the dataset and environment with different game textures. At the end of each round, competitors will submit containerized versions of their learning algorithms and they will then be trained/evaluated from scratch on a hold-out dataset-environment pair for a total of 4-days on a prespecified hardware platform.
Using Videos to Evaluate Image Model Robustness
Authors: Keren Gu, Brandon Yang, Jiquan Ngiam, Quoc Le, Jonathon Shlens
Date: 2019-04-22 00:00:00
Tasks: model

Human visual systems are robust to a wide range of image transformations that are challenging for artificial networks. We present the first study of image model robustness to the minute transformations found across video frames, which we term "natural robustness". Compared to previous studies on adversarial examples and synthetic distortions, natural robustness captures a more diverse set of common image transformations that occur in the natural environment. Our study across a dozen model architectures shows that more accurate models are more robust to natural transformations, and that robustness to synthetic color distortions is a good proxy for natural robustness. In examining brittleness in videos, we find that majority of the brittleness found in videos lies outside the typical definition of adversarial examples (99.9\%). Finally, we investigate training techniques to reduce brittleness and find that no single technique systematically improves natural robustness across twelve tested architectures.
Bold Hearts Team Description for RoboCup 2019 (Humanoid Kid Size League)
Authors: Marcus M. Scheunemann, Sander G. van Dijk, Rebecca Miko, Daniel Barry, George M. Evans, Alessandra Rossi, Daniel Polani
Date: 2019-04-22 00:00:00
Tasks: Semantic Segmentation

We participated in the RoboCup 2018 competition in Montreal with our newly
developed BoldBot based on the Darwin-OP and mostly self-printed custom parts.
This paper is about the lessons learnt from that competition and further
developments for the RoboCup 2019 competition. Firstly, we briefly introduce
the team along with an overview of past achievements. We then present a simple,
standalone 2D simulator we use for simplifying the entry for new members with
making basic RoboCup concepts quickly accessible. We describe our approach for
semantic-segmentation for our vision used in the 2018 competition, which
replaced the lookup-table (LUT) implementation we had before. We also discuss
the extra structural support we plan to add to the printed parts of the BoldBot
and our transition to ROS 2 as our new middleware. Lastly, we will present a
collection of open-source contributions of our team.
Distributed Differentially Private Computation of Functions with Correlated Noise
Authors: Hafiz Imtiaz, Jafar Mohammadi, Anand D. Sarwate
Date: 2019-04-22 00:00:00
Tasks: BIG-bench Machine Learning, Privacy Preserving

Many applications of machine learning, such as human health research, involve processing private or sensitive information. Privacy concerns may impose significant hurdles to collaboration in scenarios where there are multiple sites holding data and the goal is to estimate properties jointly across all datasets. Differentially private decentralized algorithms can provide strong privacy guarantees. However, the accuracy of the joint estimates may be poor when the datasets at each site are small. This paper proposes a new framework, Correlation Assisted Private Estimation (CAPE), for designing privacy-preserving decentralized algorithms with better accuracy guarantees in an honest-but-curious model. CAPE can be used in conjunction with the functional mechanism for statistical and machine learning optimization problems. A tighter characterization of the functional mechanism is provided that allows CAPE to achieve the same performance as a centralized algorithm in the decentralized setting using all datasets. Empirical results on regression and neural network problems for both synthetic and real datasets show that differentially private methods can be competitive with non-private algorithms in many scenarios of interest.
Learning Feature-to-Feature Translator by Alternating Back-Propagation for Generative Zero-Shot Learning
Authors: Yizhe Zhu, Jianwen Xie, Bingchen Liu, Ahmed Elgammal
Date: 2019-04-22 00:00:00
Proceeding: ICCV 2019 10
Tasks: Zero-Shot Learning

We investigate learning feature-to-feature translator networks by alternating back-propagation as a general-purpose solution to zero-shot learning (ZSL) problems. It is a generative model-based ZSL framework. In contrast to models based on generative adversarial networks (GAN) or variational autoencoders (VAE) that require auxiliary networks to assist the training, our model consists of a single conditional generator that maps class-level semantic features and Gaussian white noise vector accounting for instance-level latent factors to visual features, and is trained by maximum likelihood estimation. The training process is a simple yet effective alternating back-propagation process that iterates the following two steps: (i) the inferential back-propagation to infer the latent factors of each observed example, and (ii) the learning back-propagation to update the model parameters. We show that, with slight modifications, our model is capable of learning from incomplete visual features for ZSL. We conduct extensive comparisons with existing generative ZSL methods on five benchmarks, demonstrating the superiority of our method in not only ZSL performance but also convergence speed and computational cost. Specifically, our model outperforms the existing state-of-the-art methods by a remarkable margin up to 3.1% and 4.0% in ZSL and generalized ZSL settings, respectively.
UDFNet: Unsupervised Disparity Fusion with Adversarial Networks
Authors: Can Pu, Robert B. Fisher
Date: 2019-04-22 00:00:00

Existing disparity fusion methods based on deep learning achieve
state-of-the-art performance, but they require ground truth disparity data to
train. As far as I know, this is the first time an unsupervised disparity
fusion not using ground truth disparity data has been proposed. In this paper,
a mathematical model for disparity fusion is proposed to guide an adversarial
network to train effectively without ground truth disparity data. The initial
disparity maps are inputted from the left view along with auxiliary information
(gradient, left & right intensity image) into the refiner and the refiner is
trained to output the refined disparity map registered on the left view. The
refined left disparity map and left intensity image are used to reconstruct a
fake right intensity image. Finally, the fake and real right intensity images
(from the right stereo vision camera) are fed into the discriminator. In the
model, the refiner is trained to output a refined disparity value close to the
weighted sum of the disparity inputs for global initialisation. Then, three
refinement principles are adopted to refine the results further. (1) The
reconstructed intensity error between the fake and real right intensity image
is minimised. (2) The similarities between the fake and real right image in
different receptive fields are maximised. (3) The refined disparity map is
smoothed based on the corresponding intensity image. The adversarial networks'
architectures are effective for the fusion task. The fusion time using the
proposed network is small. The network can achieve 90 fps using Nvidia Geforce
GTX 1080Ti on the Kitti2015 dataset when the input resolution is 1242 * 375
(Width * Height) without downsampling and cropping. The accuracy of this work
is equal to (or better than) the state-of-the-art supervised methods.
LBS Autoencoder: Self-supervised Fitting of Articulated Meshes to Point Clouds
Authors: Chun-Liang Li, Tomas Simon, Jason Saragih, Barnabás Póczos, Yaser Sheikh
Date: 2019-04-22 00:00:00
Proceeding: CVPR 2019 6

We present LBS-AE; a self-supervised autoencoding algorithm for fitting
articulated mesh models to point clouds. As input, we take a sequence of point
clouds to be registered as well as an artist-rigged mesh, i.e. a template mesh
equipped with a linear-blend skinning (LBS) deformation space parameterized by
a skeleton hierarchy. As output, we learn an LBS-based autoencoder that
produces registered meshes from the input point clouds. To bridge the gap
between the artist-defined geometry and the captured point clouds, our
autoencoder models pose-dependent deviations from the template geometry. During
training, instead of using explicit correspondences, such as key points or pose
supervision, our method leverages LBS deformations to bootstrap the learning
process. To avoid poor local minima from erroneous point-to-point
correspondences, we utilize a structured Chamfer distance based on
part-segmentations, which are learned concurrently using self-supervision. We
demonstrate qualitative results on real captured hands, and report quantitative
evaluations on the FAUST benchmark for body registration. Our method achieves
performance that is superior to other unsupervised approaches and comparable to
methods using supervised examples.
Leveraging Orientation for Weakly Supervised Object Detection with Application to Firearm Localization
Authors: Javed Iqbal, Muhammad Akhtar Munir, Arif Mahmood, Afsheen Rafaqat Ali, Mohsen Ali
Date: 2019-04-22 00:00:00
Tasks: Object, object-detection, Object Detection, Weakly Supervised Object Detection

Automatic detection of firearms is important for enhancing the security and safety of people, however, it is a challenging task owing to the wide variations in shape, size, and appearance of firearms. Also, most of the generic object detectors process axis-aligned rectangular areas though, a thin and long rifle may actually cover only a small percentage of that area and the rest may contain irrelevant details suppressing the required object signatures. To handle these challenges, we propose a weakly supervised Orientation Aware Object Detection (OAOD) algorithm which learns to detect oriented object bounding boxes (OBB) while using AxisAligned Bounding Boxes (AABB) for training. The proposed OAOD is different from the existing oriented object detectors which strictly require OBB during training which may not always be present. The goal of training on AABB and detection of OBB is achieved by employing a multistage scheme, with Stage-1 predicting the AABB and Stage-2 predicting OBB. In-between the two stages, the oriented proposal generation module along with the object aligned RoI pooling is designed to extract features based on the predicted orientation and to make these features orientation invariant. A diverse and challenging dataset consisting of eleven thousand images is also proposed for firearm detection which is manually annotated for firearm classification and localization. The proposed ITU Firearm dataset (ITUF) contains a wide range of guns and rifles. The OAOD algorithm is evaluated on the ITUF dataset and compared with current state-of-the-art object detectors, including fully supervised oriented object detectors. OAOD has outperformed both types of object detectors with a significant margin. The experimental results (mAP: 88.3 on AABB & mAP: 77.5 on OBB) demonstrate the effectiveness of the proposed algorithm for firearm detection.