pwc-archive/papers-with-abstracts huggingface dataset:
Pornographic Image Recognition via Weighted Multiple Instance Learning
Authors: Jin Xin, Wang Yuhui, Tan Xiaoyang
Date: 2019-02-11 00:00:00
Tasks: Multiple Instance Learning

In the era of Internet, recognizing pornographic images is of great
significance for protecting children's physical and mental health. However,
this task is very challenging as the key pornographic contents (e.g., breast
and private part) in an image often lie in local regions of small size. In this
paper, we model each image as a bag of regions, and follow a multiple instance
learning (MIL) approach to train a generic region-based recognition model.
Specifically, we take into account the region's degree of pornography, and make
three main contributions. First, we show that based on very few annotations of
the key pornographic contents in a training image, we can generate a bag of
properly sized regions, among which the potential positive regions usually
contain useful contexts that can aid recognition. Second, we present a simple
quantitative measure of a region's degree of pornography, which can be used to
weigh the importance of different regions in a positive image. Third, we
formulate the recognition task as a weighted MIL problem under the
convolutional neural network framework, with a bag probability function
introduced to combine the importance of different regions. Experiments on our
newly collected large scale dataset demonstrate the effectiveness of the
proposed method, achieving an accuracy with 97.52% true positive rate at 1%
false positive rate, tested on 100K pornographic images and 100K normal images.
Latent Space Reinforcement Learning for Steering Angle Prediction
Authors: Qadeer Khan, Torsten Schön, Patrick Wenzel
Date: 2019-02-11 00:00:00
Tasks: Deep Reinforcement Learning, Prediction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Model-free reinforcement learning has recently been shown to successfully
learn navigation policies from raw sensor data. In this work, we address the
problem of learning driving policies for an autonomous agent in a high-fidelity
simulator. Building upon recent research that applies deep reinforcement
learning to navigation problems, we present a modular deep reinforcement
learning approach to predict the steering angle of the car from raw images. The
first module extracts a low-dimensional latent semantic representation of the
image. The control module trained with reinforcement learning takes the latent
vector as input to predict the correct steering angle. The experimental results
have showed that our method is capable of learning to maneuver the car without
any human control signals.
Path Capsule Networks
Authors: Mohammed Amer, Tomás Maul
Date: 2019-02-11 00:00:00
Tasks: Pose Estimation

Capsule network (CapsNet) was introduced as an enhancement over convolutional neural networks, supplementing the latter's invariance properties with equivariance through pose estimation. CapsNet achieved a very decent performance with a shallow architecture and a significant reduction in parameters count. However, the width of the first layer in CapsNet is still contributing to a significant number of its parameters and the shallowness may be limiting the representational power of the capsules. To address these limitations, we introduce Path Capsule Network (PathCapsNet), a deep parallel multi-path version of CapsNet. We show that a judicious coordination of depth, max-pooling, regularization by DropCircuit and a new fan-in routing by agreement technique can achieve better or comparable results to CapsNet, while further reducing the parameter count significantly.
Efficient Primal-Dual Algorithms for Large-Scale Multiclass Classification
Authors: Dmitry Babichev, Dmitrii Ostrovskii, Francis Bach
Date: 2019-02-11 00:00:00
Tasks: Classification, General Classification

We develop efficient algorithms to train $\ell_1$-regularized linear
classifiers with large dimensionality $d$ of the feature space, number of
classes $k$, and sample size $n$. Our focus is on a special class of losses
that includes, in particular, the multiclass hinge and logistic losses. Our
approach combines several ideas: (i) passing to the equivalent saddle-point
problem with a quasi-bilinear objective; (ii) applying stochastic mirror
descent with a proper choice of geometry which guarantees a favorable accuracy
bound; (iii) devising non-uniform sampling schemes to approximate the matrix
products. In particular, for the multiclass hinge loss we propose a
\textit{sublinear} algorithm with iterations performed in $O(d+n+k)$ arithmetic
operations.
Taking a HINT: Leveraging Explanations to Make Vision and Language Models More Grounded
Authors: Ramprasaath R. Selvaraju, Stefan Lee, Yilin Shen, Hongxia Jin, Shalini Ghosh, Larry Heck, Dhruv Batra, Devi Parikh
Date: 2019-02-11 00:00:00
Proceeding: ICCV 2019 10
Tasks: Image Captioning, Question Answering, Visual Grounding, Visual Question Answering, Visual Question Answering (VQA)

Many vision and language models suffer from poor visual grounding - often falling back on easy-to-learn language priors rather than basing their decisions on visual concepts in the image. In this work, we propose a generic approach called Human Importance-aware Network Tuning (HINT) that effectively leverages human demonstrations to improve visual grounding. HINT encourages deep networks to be sensitive to the same input regions as humans. Our approach optimizes the alignment between human attention maps and gradient-based network importances - ensuring that models learn not just to look at but rather rely on visual concepts that humans found relevant for a task when making predictions. We apply HINT to Visual Question Answering and Image Captioning tasks, outperforming top approaches on splits that penalize over-reliance on language priors (VQA-CP and robust captioning) using human attention demonstrations for just 6% of the training data.
Peeking into the Future: Predicting Future Person Activities and Locations in Videos
Authors: Junwei Liang, Lu Jiang, Juan Carlos Niebles, Alexander Hauptmann, Li Fei-Fei
Date: 2019-02-11 00:00:00
Proceeding: CVPR 2019 6
Tasks: Activity Prediction, Future prediction, Human motion prediction, Motion Forecasting, motion prediction, Multi-Task Learning, Prediction, Trajectory Forecasting, Trajectory Prediction

Deciphering human behaviors to predict their future paths/trajectories and what they would do from videos is important in many applications. Motivated by this idea, this paper studies predicting a pedestrian's future path jointly with future activities. We propose an end-to-end, multi-task learning system utilizing rich visual features about human behavioral information and interaction with their surroundings. To facilitate the training, the network is learned with an auxiliary task of predicting future location in which the activity will happen. Experimental results demonstrate our state-of-the-art performance over two public benchmarks on future trajectory prediction. Moreover, our method is able to produce meaningful future activity prediction in addition to the path. The result provides the first empirical evidence that joint modeling of paths and activities benefits future path prediction.
Visual SLAM: Why Bundle Adjust?
Authors: Álvaro Parra, Tat-Jun Chin, Anders Eriksson, Ian Reid
Date: 2019-02-11 00:00:00

Bundle adjustment plays a vital role in feature-based monocular SLAM. In many modern SLAM pipelines, bundle adjustment is performed to estimate the 6DOF camera trajectory and 3D map (3D point cloud) from the input feature tracks. However, two fundamental weaknesses plague SLAM systems based on bundle adjustment. First, the need to carefully initialise bundle adjustment means that all variables, in particular the map, must be estimated as accurately as possible and maintained over time, which makes the overall algorithm cumbersome. Second, since estimating the 3D structure (which requires sufficient baseline) is inherent in bundle adjustment, the SLAM algorithm will encounter difficulties during periods of slow motion or pure rotational motion. We propose a different SLAM optimisation core: instead of bundle adjustment, we conduct rotation averaging to incrementally optimise only camera orientations. Given the orientations, we estimate the camera positions and 3D points via a quasi-convex formulation that can be solved efficiently and globally optimally. Our approach not only obviates the need to estimate and maintain the positions and 3D map at keyframe rate (which enables simpler SLAM systems), it is also more capable of handling slow motions or pure rotational motions.
Harnessing Low-Fidelity Data to Accelerate Bayesian Optimization via Posterior Regularization
Authors: Bin Liu
Date: 2019-02-11 00:00:00
Tasks: Bayesian Optimization, global-optimization

Bayesian optimization (BO) is a powerful paradigm for derivative-free global optimization of a black-box objective function (BOF) that is expensive to evaluate. However, the overhead of BO can still be prohibitive for problems with highly expensive function evaluations. In this paper, we investigate how to reduce the required number of function evaluations for BO without compromise in solution quality. We explore the idea of posterior regularization to harness low fidelity (LF) data within the Gaussian process upper confidence bound (GP-UCB) framework. The LF data can arise from previous evaluations of an LF approximation of the BOF or of a related optimization task. An extra GP model called LF-GP is trained to fit the LF data. We develop an operator termed dynamic weighted product of experts (DW-POE) fusion. The regularization is induced by this operator on the posterior of the BOF. The impact of the LF GP model on the resulting regularized posterior is adaptively adjusted via Bayesian formalism. Extensive experimental results on benchmark BOF optimization tasks demonstrate the superior performance of the proposed algorithm over state-of-the-art.
Fast Evaluation of Low-Thrust Transfers via Deep Neural Networks
Authors: Yue-he Zhu, Ya-zhong Luo
Date: 2019-02-11 00:00:00

The design of low-thrust-based multitarget interplanetary missions requires a
method to quickly and accurately evaluate the low-thrust transfer between any
two visiting targets. Complete evaluation of the low-thrust transfer includes
not only the estimation of the optimal fuel consumption but also the judgment
of transfer feasibility. In this paper, a deep neural network (DNN)-based
method is proposed for quickly evaluating low-thrust transfer. An efficient
database generation method is developed for obtaining both the infeasible and
optimal transfers. A classification DNN and a regression DNN are trained based
on the infeasible and optimal transfers to judge the transfer feasibility and
estimate the optimal fuel consumption, respectively. The simulation results
show that the well-trained DNNs are capable of quickly determining the transfer
feasibility with a correct rate of greater than 98% and approximating the
optimal transfer fuel consumption with a relative estimation error of less than
0.4%. The tests on two asteroid chains further show the superiority of the
DNN-based method for application to the design of low-thrust-based multitarget
interplanetary missions
A Short Note on Concentration Inequalities for Random Vectors with SubGaussian Norm
Authors: Chi Jin, Praneeth Netrapalli, Rong Ge, Sham M. Kakade, Michael. I. Jordan
Date: 2019-02-11 00:00:00

In this note, we derive concentration inequalities for random vectors with
subGaussian norm (a generalization of both subGaussian random vectors and norm
bounded random vectors), which are tight up to logarithmic factors.
Discrimination in the Age of Algorithms
Authors: Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan, Cass R. Sunstein
Date: 2019-02-11 00:00:00
Tasks: Decision Making, Specificity

The law forbids discrimination. But the ambiguity of human decision-making
often makes it extraordinarily hard for the legal system to know whether anyone
has actually discriminated. To understand how algorithms affect discrimination,
we must therefore also understand how they affect the problem of detecting
discrimination. By one measure, algorithms are fundamentally opaque, not just
cognitively but even mathematically. Yet for the task of proving
discrimination, processes involving algorithms can provide crucial forms of
transparency that are otherwise unavailable. These benefits do not happen
automatically. But with appropriate requirements in place, the use of
algorithms will make it possible to more easily examine and interrogate the
entire decision process, thereby making it far easier to know whether
discrimination has occurred. By forcing a new level of specificity, the use of
algorithms also highlights, and makes transparent, central tradeoffs among
competing values. Algorithms are not only a threat to be regulated; with the
right safeguards in place, they have the potential to be a positive force for
equity.
UcoSLAM: Simultaneous Localization and Mapping by Fusion of KeyPoints and Squared Planar Markers
Authors: Rafael Munoz-Salinas, Rafael Medina-Carnicer
Date: 2019-02-11 00:00:00
Tasks: Simultaneous Localization and Mapping

This paper proposes a novel approach for Simultaneous Localization and
Mapping by fusing natural and artificial landmarks. Most of the SLAM approaches
use natural landmarks (such as keypoints). However, they are unstable over
time, repetitive in many cases or insufficient for a robust tracking (e.g. in
indoor buildings). On the other hand, other approaches have employed artificial
landmarks (such as squared fiducial markers) placed in the environment to help
tracking and relocalization. We propose a method that integrates both
approaches in order to achieve long-term robust tracking in many scenarios.
  Our method has been compared to the start-of-the-art methods ORB-SLAM2 and
LDSO in the public dataset Kitti, Euroc-MAV, TUM and SPM, obtaining better
precision, robustness and speed. Our tests also show that the combination of
markers and keypoints achieves better accuracy than each one of them
independently.
Error Analysis on Graph Laplacian Regularized Estimator
Authors: Kaige Yang, Xiaowen Dong, Laura Toni
Date: 2019-02-11 00:00:00
Tasks: Representation Learning

We provide a theoretical analysis of the representation learning problem
aimed at learning the latent variables (design matrix) $\Theta$ of observations
$Y$ with the knowledge of the coefficient matrix $X$. The design matrix is
learned under the assumption that the latent variables $\Theta$ are smooth with
respect to a (known) topological structure $\mathcal{G}$. To learn such latent
variables, we study a graph Laplacian regularized estimator, which is the
penalized least squares estimator with penalty term proportional to a Laplacian
quadratic form. This type of estimators has recently received considerable
attention due to its capability in incorporating underlying topological graph
structure of variables into the learning process. While the estimation problem
can be solved efficiently by state-of-the-art optimization techniques, its
statistical consistency properties have been largely overlooked. In this work,
we develop a non-asymptotic bound of estimation error under the classical
statistical setting, where sample size is larger than the ambient dimension of
the latent variables. This bound illustrates theoretically the impact of the
alignment between the data and the graph structure as well as the graph
spectrum on the estimation accuracy. It also provides theoretical evidence of
the advantage, in terms of convergence rate, of the graph Laplacian regularized
estimator over classical ones (that ignore the graph structure) in case of a
smoothness prior. Finally, we provide empirical results of the estimation error
to corroborate the theoretical analysis.
Manifold Optimization Assisted Gaussian Variational Approximation
Authors: Bingxin Zhou, Junbin Gao, Minh-Ngoc Tran, Richard Gerlach
Date: 2019-02-11 00:00:00
Tasks: Bayesian Inference

Gaussian variational approximation is a popular methodology to approximate posterior distributions in Bayesian inference especially in high dimensional and large data settings. To control the computational cost while being able to capture the correlations among the variables, the low rank plus diagonal structure was introduced in the previous literature for the Gaussian covariance matrix. For a specific Bayesian learning task, the uniqueness of the solution is usually ensured by imposing stringent constraints on the parameterized covariance matrix, which could break down during the optimization process. In this paper, we consider two special covariance structures by applying the Stiefel manifold and Grassmann manifold constraints, to address the optimization difficulty in such factorization architectures. To speed up the updating process with minimum hyperparameter-tuning efforts, we design two new schemes of Riemannian stochastic gradient descent methods and compare them with other existing methods of optimizing on manifolds. In addition to fixing the identification issue, results from both simulation and empirical experiments prove the ability of the proposed methods of obtaining competitive accuracy and comparable converge speed in both high-dimensional and large-scale learning tasks.
Truncated Gaussian-Mixture Variational AutoEncoder
Authors: Qingyu Zhao, Nicolas Honnorat, Ehsan Adeli, Kilian M. Pohl
Date: 2019-02-11 00:00:00
Tasks: Clustering, Functional Connectivity, Outlier Detection

Variation Autoencoder (VAE) has become a powerful tool in modeling the non-linear generative process of data from a low-dimensional latent space. Recently, several studies have proposed to use VAE for unsupervised clustering by using mixture models to capture the multi-modal structure of latent representations. This strategy, however, is ineffective when there are outlier data samples whose latent representations are meaningless, yet contaminating the estimation of key major clusters in the latent space. This exact problem arises in the context of resting-state fMRI (rs-fMRI) analysis, where clustering major functional connectivity patterns is often hindered by heavy noise of rs-fMRI and many minor clusters (rare connectivity patterns) of no interest to analysis. In this paper we propose a novel generative process, in which we use a Gaussian-mixture to model a few major clusters in the data, and use a non-informative uniform distribution to capture the remaining data. We embed this truncated Gaussian-Mixture model in a Variational AutoEncoder framework to obtain a general joint clustering and outlier detection approach, called tGM-VAE. We demonstrated the applicability of tGM-VAE on the MNIST dataset and further validated it in the context of rs-fMRI connectivity analysis.
Towards Segmenting Anything That Moves
Authors: Achal Dave, Pavel Tokmakov, Deva Ramanan
Date: 2019-02-11 00:00:00
Tasks: Action Detection, Instance Segmentation, Motion Estimation, Motion Segmentation, Moving Object Detection, object-detection, Object Detection, Optical Flow Estimation, Semantic Segmentation, Video Semantic Segmentation

Detecting and segmenting individual objects, regardless of their category, is crucial for many applications such as action detection or robotic interaction. While this problem has been well-studied under the classic formulation of spatio-temporal grouping, state-of-the-art approaches do not make use of learning-based methods. To bridge this gap, we propose a simple learning-based approach for spatio-temporal grouping. Our approach leverages motion cues from optical flow as a bottom-up signal for separating objects from each other. Motion cues are then combined with appearance cues that provide a generic objectness prior for capturing the full extent of objects. We show that our approach outperforms all prior work on the benchmark FBMS dataset. One potential worry with learning-based methods is that they might overfit to the particular type of objects that they have been trained on. To address this concern, we propose two new benchmarks for generic, moving object detection, and show that our model matches top-down methods on common categories, while significantly out-performing both top-down and bottom-up methods on never-before-seen categories.
Generalization through Simulation: Integrating Simulated and Real Data into Deep Reinforcement Learning for Vision-Based Autonomous Flight
Authors: Katie Kang, Suneel Belkhale, Gregory Kahn, Pieter Abbeel, Sergey Levine
Date: 2019-02-11 00:00:00
Tasks: Collision Avoidance, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning provides a promising approach for vision-based
control of real-world robots. However, the generalization of such models
depends critically on the quantity and variety of data available for training.
This data can be difficult to obtain for some types of robotic systems, such as
fragile, small-scale quadrotors. Simulated rendering and physics can provide
for much larger datasets, but such data is inherently of lower quality: many of
the phenomena that make the real-world autonomous flight problem challenging,
such as complex physics and air currents, are modeled poorly or not at all, and
the systematic differences between simulation and the real world are typically
impossible to eliminate. In this work, we investigate how data from both
simulation and the real world can be combined in a hybrid deep reinforcement
learning algorithm. Our method uses real-world data to learn about the dynamics
of the system, and simulated data to learn a generalizable perception system
that can enable the robot to avoid collisions using only a monocular camera. We
demonstrate our approach on a real-world nano aerial vehicle collision
avoidance task, showing that with only an hour of real-world data, the
quadrotor can avoid collisions in new environments with various lighting
conditions and geometry. Code, instructions for building the aerial vehicles,
and videos of the experiments can be found at github.com/gkahn13/GtS
Acceleration via Symplectic Discretization of High-Resolution Differential Equations
Authors: Bin Shi, Simon S. Du, Weijie J. Su, Michael. I. Jordan
Date: 2019-02-11 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Vocal Bursts Intensity Prediction

We study first-order optimization methods obtained by discretizing ordinary differential equations (ODEs) corresponding to Nesterov's accelerated gradient methods (NAGs) and Polyak's heavy-ball method. We consider three discretization schemes: an explicit Euler scheme, an implicit Euler scheme, and a symplectic scheme. We show that the optimization algorithm generated by applying the symplectic scheme to a high-resolution ODE proposed by Shi et al. [2018] achieves an accelerated rate for minimizing smooth strongly convex functions. On the other hand, the resulting algorithm either fails to achieve acceleration or is impractical when the scheme is implicit, the ODE is low-resolution, or the scheme is explicit.