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Apr 02, 2020
Computer Vision

Learning to See Through Obstructions

We present a learning-based approach for removing unwanted obstructions, such as window reflections, fence occlusions or raindrops, from a short sequ…
Apr 02, 2020
Image and Video Processing
Computer Vision

Single-Image HDR Reconstruction by Learning to Reverse the Camera P…

Recovering a high dynamic range (HDR) image from a single low dynamic range (LDR) input image is challenging due to missing details in under-/over-ex…
Apr 02, 2020
Computer Vision

Tracking Objects as Points

Tracking has traditionally been the art of following interest points through space and time. This changed with the rise of powerful deep networks. No…
Apr 02, 2020
Computer Vision

Learning Unsupervised Hierarchical Part Decomposition of 3D Objects f…

Humans perceive the 3D world as a set of distinct objects that are characterized by various low-level (geometry, reflectance) and high-level (connect…
Apr 02, 2020
Computation and Language

Causal Inference of Script Knowledge

When does a sequence of events define an everyday scenario and how can this knowledge be induced from text? Prior works in inducing such scripts have…
Apr 02, 2020
Computer Vision

DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes

We propose DOPS, a fast single-stage 3D object detection method for LIDAR data. Previous methods often make domain-specific design decisions, for exa…
Apr 02, 2020
Artificial Intelligence
Computation and Language
Machine Learning

Improving the Utility of Knowledge Graph Embeddings with Calibration

This paper addresses machine learning models that embed knowledge graph entities and relationships toward the goal of predicting unseen triples, whic…
Apr 02, 2020
Robotics
Machine Learning

Multimodal Material Classification for Robots using Spectroscopy and …

Material recognition can help inform robots about how to properly interact with and manipulate real-world objects. In this paper, we present a multim…
Apr 02, 2020
Computer Vision

We propose a novel DASR framework for unsupervised real-world image SR. Given only unpaired data, DASR firstly trains a down-sampling network to generate synthetic LR images in the real-world LR distribution. Then, the generated synthetic pairs and …

Unsupervised Real-world Image Super Resolution via Domain-distance Aw…

These days, unsupervised super-resolution (SR) has been soaring due to its practical and promising potential in real scenarios. The philosophy of off…
Apr 02, 2020
Computer Vision

With our physics-based simulation pipeline, we generated a dataset, PressurePose, consisting of 200K synthetic pressure images with an unprecedented variety of body shapes and poses. Then, we trained a deep learning model, PressureNet, entirely on s…

Bodies at Rest: 3D Human Pose and Shape Estimation from a Pressure Im…

People spend a substantial part of their lives at rest in bed. 3D human pose and shape estimation for this activity would have numerous beneficial ap…
Apr 02, 2020
Machine Learning

In this paper we used Single World Intervention Graphs (SWIGs) Richardson & Robins (2013), the potential outcomes calculus Malinsky et al. (2019), and the nested Markov factorization of mixed graphs Richardson et al. (2017) to yield a set of increas…

Identification Methods With Arbitrary Interventional Distributions as…

Causal inference quantifies cause-effect relationships by estimating counterfactual parameters from data. This entails using \emph{identification the…
Apr 02, 2020

We are grateful to Eduard Gorbar for valuable comments and discussion. Y. S. acknowledges support from the National Academy of Sciences of Ukraine in frames of priority project “Fundamental properties of matter in the relativistic collisions of nucl…

Model-independent constraints in inflationary magnetogenesis

We derive a simple model-independent upper bound on the strength of magnetic fields obtained in inflationary and post-inflationary magnetogenesis tak…
Apr 02, 2020
Computer Vision

We have presented Progressive Population Based Augmentation, a novel automated augmentation algorithm for point clouds. PPBA optimizes the augmentation schedule via narrowing down the search space and adopting the best parameters from past iteration…

Improving 3D Object Detection through Progressive Population Based …

Data augmentation has been widely adopted for object detection in 3D point clouds. All previous efforts have focused on manually designing specific d…
Apr 01, 2020
Computer Vision
Cryptography and Security

Evading Deepfake-Image Detectors with White- and Black-Box Attacks

It is now possible to synthesize highly realistic images of people who don't exist. Such content has, for example, been implicated in the creation of…
Apr 01, 2020
Computer Vision

Articulation-aware Canonical Surface Mapping

We tackle the tasks of: 1) predicting a Canonical Surface Mapping (CSM) that indicates the mapping from 2D pixels to corresponding points on a canoni…
Apr 01, 2020
Image and Video Processing
Computer Vision
Numerical Analysis

Robust Image Reconstruction with Misaligned Structural Information

Multi-modality (or multi-channel) imaging is becoming increasingly important and more widely available, e.g. hyperspectral imaging in remote sensing,…
Apr 01, 2020
Computer Vision
Machine Learning

Symmetry and Group in Attribute-Object Compositions

Attributes and objects can compose diverse compositions. To model the compositional nature of these general concepts, it is a good choice to learn th…
Apr 01, 2020
Databases
Computation and Language

Deep Entity Matching with Pre-Trained Language Models

We present Ditto, a novel entity matching system based on pre-trained Transformer-based language models. We fine-tune and cast EM as a sequence-pair …
Apr 01, 2020
Machine Learning
Signal Processing

From Fourier to Koopman: Spectral Methods for Long-term Time Series …

We propose spectral methods for long-term forecasting of temporal signals stemming from linear and nonlinear quasi-periodic dynamical systems. For li…
Apr 01, 2020
Machine Learning
Optimization and Control

Tightened Convex Relaxations for Neural Network Robustness Certificat…

In this paper, we consider the problem of certifying the robustness of neural networks to perturbed and adversarial input data. Such certification is…
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