pwc-archive/papers-with-abstracts huggingface dataset:
Serialized Output Training for End-to-End Overlapped Speech Recognition
Authors: Naoyuki Kanda, Yashesh Gaur, Xiaofei Wang, Zhong Meng, Takuya Yoshioka
Date: 2020-03-28 00:00:00
Tasks: Decoder, speech-recognition, Speech Recognition

This paper proposes serialized output training (SOT), a novel framework for multi-speaker overlapped speech recognition based on an attention-based encoder-decoder approach. Instead of having multiple output layers as with the permutation invariant training (PIT), SOT uses a model with only one output layer that generates the transcriptions of multiple speakers one after another. The attention and decoder modules take care of producing multiple transcriptions from overlapped speech. SOT has two advantages over PIT: (1) no limitation in the maximum number of speakers, and (2) an ability to model the dependencies among outputs for different speakers. We also propose a simple trick that allows SOT to be executed in $O(S)$, where $S$ is the number of the speakers in the training sample, by using the start times of the constituent source utterances. Experimental results on LibriSpeech corpus show that the SOT models can transcribe overlapped speech with variable numbers of speakers significantly better than PIT-based models. We also show that the SOT models can accurately count the number of speakers in the input audio.
Semantic Implicit Neural Scene Representations With Semi-Supervised Training
Authors: Amit Kohli, Vincent Sitzmann, Gordon Wetzstein
Date: 2020-03-28 00:00:00
Tasks: 3D geometry, 3D Semantic Segmentation, Representation Learning, Segmentation, Semantic Segmentation

The recent success of implicit neural scene representations has presented a viable new method for how we capture and store 3D scenes. Unlike conventional 3D representations, such as point clouds, which explicitly store scene properties in discrete, localized units, these implicit representations encode a scene in the weights of a neural network which can be queried at any coordinate to produce these same scene properties. Thus far, implicit representations have primarily been optimized to estimate only the appearance and/or 3D geometry information in a scene. We take the next step and demonstrate that an existing implicit representation (SRNs) is actually multi-modal; it can be further leveraged to perform per-point semantic segmentation while retaining its ability to represent appearance and geometry. To achieve this multi-modal behavior, we utilize a semi-supervised learning strategy atop the existing pre-trained scene representation. Our method is simple, general, and only requires a few tens of labeled 2D segmentation masks in order to achieve dense 3D semantic segmentation. We explore two novel applications for this semantically aware implicit neural scene representation: 3D novel view and semantic label synthesis given only a single input RGB image or 2D label mask, as well as 3D interpolation of appearance and semantics.
GRATE: Granular Recovery of Aggregated Tensor Data by Example
Authors: Ahmed S. Zamzam, Bo Yang, Nicholas D. Sidiropoulos
Date: 2020-03-27 00:00:00

In this paper, we address the challenge of recovering an accurate breakdown of aggregated tensor data using disaggregation examples. This problem is motivated by several applications. For example, given the breakdown of energy consumption at some homes, how can we disaggregate the total energy consumed during the same period at other homes? In order to address this challenge, we propose GRATE, a principled method that turns the ill-posed task at hand into a constrained tensor factorization problem. Then, this optimization problem is tackled using an alternating least-squares algorithm. GRATE has the ability to handle exact aggregated data as well as inexact aggregation where some unobserved quantities contribute to the aggregated data. Special emphasis is given to the energy disaggregation problem where the goal is to provide energy breakdown for consumers from their monthly aggregated consumption. Experiments on two real datasets show the efficacy of GRATE in recovering more accurate disaggregation than state-of-the-art energy disaggregation methods.
Towards Supervised and Unsupervised Neural Machine Translation Baselines for Nigerian Pidgin
Authors: Orevaoghene Ahia, Kelechi Ogueji
Date: 2020-03-27 00:00:00
Tasks: Machine Translation, NMT, Translation

Nigerian Pidgin is arguably the most widely spoken language in Nigeria. Variants of this language are also spoken across West and Central Africa, making it a very important language. This work aims to establish supervised and unsupervised neural machine translation (NMT) baselines between English and Nigerian Pidgin. We implement and compare NMT models with different tokenization methods, creating a solid foundation for future works.
Semiparametric Inference For Causal Effects In Graphical Models With Hidden Variables
Authors: Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser
Date: 2020-03-27 00:00:00

Identification theory for causal effects in causal models associated with hidden variable directed acyclic graphs (DAGs) is well studied. However, the corresponding algorithms are underused due to the complexity of estimating the identifying functionals they output. In this work, we bridge the gap between identification and estimation of population-level causal effects involving a single treatment and a single outcome. We derive influence function based estimators that exhibit double robustness for the identified effects in a large class of hidden variable DAGs where the treatment satisfies a simple graphical criterion; this class includes models yielding the adjustment and front-door functionals as special cases. We also provide necessary and sufficient conditions under which the statistical model of a hidden variable DAG is nonparametrically saturated and implies no equality constraints on the observed data distribution. Further, we derive an important class of hidden variable DAGs that imply observed data distributions observationally equivalent (up to equality constraints) to fully observed DAGs. In these classes of DAGs, we derive estimators that achieve the semiparametric efficiency bounds for the target of interest where the treatment satisfies our graphical criterion. Finally, we provide a sound and complete identification algorithm that directly yields a weight based estimation strategy for any identifiable effect in hidden variable causal models.
Deep CG2Real: Synthetic-to-Real Translation via Image Disentanglement
Authors: Sai Bi, Kalyan Sunkavalli, Federico Perazzi, Eli Shechtman, Vladimir Kim, Ravi Ramamoorthi
Date: 2020-03-27 00:00:00
Proceeding: ICCV 2019 10
Tasks: Disentanglement, Domain Adaptation, Synthetic-to-Real Translation, Translation

We present a method to improve the visual realism of low-quality, synthetic images, e.g. OpenGL renderings. Training an unpaired synthetic-to-real translation network in image space is severely under-constrained and produces visible artifacts. Instead, we propose a semi-supervised approach that operates on the disentangled shading and albedo layers of the image. Our two-stage pipeline first learns to predict accurate shading in a supervised fashion using physically-based renderings as targets, and further increases the realism of the textures and shading with an improved CycleGAN network. Extensive evaluations on the SUNCG indoor scene dataset demonstrate that our approach yields more realistic images compared to other state-of-the-art approaches. Furthermore, networks trained on our generated "real" images predict more accurate depth and normals than domain adaptation approaches, suggesting that improving the visual realism of the images can be more effective than imposing task-specific losses.
Designing Color Filters that Make Cameras MoreColorimetric
Authors: Graham D. Finlayson, Yuteng Zhu
Date: 2020-03-27 00:00:00

When we place a colored filter in front of a camera the effective camera response functions are equal to the given camera spectral sensitivities multiplied by the filter spectral transmittance. In this paper, we solve for the filter which returns the modified sensitivities as close to being a linear transformation from the color matching functions of human visual system as possible. When this linearity condition - sometimes called the Luther condition - is approximately met, the `camera+filter' system can be used for accurate color measurement. Then, we reformulate our filter design optimisation for making the sensor responses as close to the CIEXYZ tristimulus values as possible given the knowledge of real measured surfaces and illuminants spectra data. This data-driven method in turn is extended to incorporate constraints on the filter (smoothness and bounded transmission). Also, because how the optimisation is initialised is shown to impact on the performance of the solved-for filters, a multi-initialisation optimisation is developed. Experiments demonstrate that, by taking pictures through our optimised color filters we can make cameras significantly more colorimetric.
Random Machines Regression Approach: an ensemble support vector regression model with free kernel choice
Authors: Anderson Ara, Mateus Maia, Samuel Macêdo, Francisco Louzada
Date: 2020-03-27 00:00:00
Tasks: BIG-bench Machine Learning, regression

Machine learning techniques always aim to reduce the generalized prediction error. In order to reduce it, ensemble methods present a good approach combining several models that results in a greater forecasting capacity. The Random Machines already have been demonstrated as strong technique, i.e: high predictive power, to classification tasks, in this article we propose an procedure to use the bagged-weighted support vector model to regression problems. Simulation studies were realized over artificial datasets, and over real data benchmarks. The results exhibited a good performance of Regression Random Machines through lower generalization error without needing to choose the best kernel function during tuning process.
Deep 3D Capture: Geometry and Reflectance from Sparse Multi-View Images
Authors: Sai Bi, Zexiang Xu, Kalyan Sunkavalli, David Kriegman, Ravi Ramamoorthi
Date: 2020-03-27 00:00:00
Proceeding: CVPR 2020 6

We introduce a novel learning-based method to reconstruct the high-quality geometry and complex, spatially-varying BRDF of an arbitrary object from a sparse set of only six images captured by wide-baseline cameras under collocated point lighting. We first estimate per-view depth maps using a deep multi-view stereo network; these depth maps are used to coarsely align the different views. We propose a novel multi-view reflectance estimation network architecture that is trained to pool features from these coarsely aligned images and predict per-view spatially-varying diffuse albedo, surface normals, specular roughness and specular albedo. We do this by jointly optimizing the latent space of our multi-view reflectance network to minimize the photometric error between images rendered with our predictions and the input images. While previous state-of-the-art methods fail on such sparse acquisition setups, we demonstrate, via extensive experiments on synthetic and real data, that our method produces high-quality reconstructions that can be used to render photorealistic images.
Combining Visible and Infrared Spectrum Imagery using Machine Learning for Small Unmanned Aerial System Detection
Authors: Vinicius G. Goecks, Grayson Woods, John Valasek
Date: 2020-03-27 00:00:00
Tasks: BIG-bench Machine Learning, object-detection, Object Detection

Advances in machine learning and deep neural networks for object detection, coupled with lower cost and power requirements of cameras, led to promising vision-based solutions for sUAS detection. However, solely relying on the visible spectrum has previously led to reliability issues in low contrast scenarios such as sUAS flying below the treeline and against bright sources of light. Alternatively, due to the relatively high heat signatures emitted from sUAS during flight, a long-wave infrared (LWIR) sensor is able to produce images that clearly contrast the sUAS from its background. However, compared to widely available visible spectrum sensors, LWIR sensors have lower resolution and may produce more false positives when exposed to birds or other heat sources. This research work proposes combining the advantages of the LWIR and visible spectrum sensors using machine learning for vision-based detection of sUAS. Utilizing the heightened background contrast from the LWIR sensor combined and synchronized with the relatively increased resolution of the visible spectrum sensor, a deep learning model was trained to detect the sUAS through previously difficult environments. More specifically, the approach demonstrated effective detection of multiple sUAS flying above and below the treeline, in the presence of heat sources, and glare from the sun. Our approach achieved a detection rate of 71.2 +- 8.3%, improving by 69% when compared to LWIR and by 30.4% when visible spectrum alone, and achieved false alarm rate of 2.7 +- 2.6%, decreasing by 74.1% and by 47.1% when compared to LWIR and visible spectrum alone, respectively, on average, for single and multiple drone scenarios, controlled for the same confidence metric of the machine learning object detector of at least 50%. Videos of the solution's performance can be seen at https://sites.google.com/view/tamudrone-spie2020/.
The impossibility of low rank representations for triangle-rich complex networks
Authors: C. Seshadhri, Aneesh Sharma, Andrew Stolman, Ashish Goel
Date: 2020-03-27 00:00:00
Tasks: Clustering, Recommendation Systems

The study of complex networks is a significant development in modern science, and has enriched the social sciences, biology, physics, and computer science. Models and algorithms for such networks are pervasive in our society, and impact human behavior via social networks, search engines, and recommender systems to name a few. A widely used algorithmic technique for modeling such complex networks is to construct a low-dimensional Euclidean embedding of the vertices of the network, where proximity of vertices is interpreted as the likelihood of an edge. Contrary to the common view, we argue that such graph embeddings do not}capture salient properties of complex networks. The two properties we focus on are low degree and large clustering coefficients, which have been widely established to be empirically true for real-world networks. We mathematically prove that any embedding (that uses dot products to measure similarity) that can successfully create these two properties must have rank nearly linear in the number of vertices. Among other implications, this establishes that popular embedding techniques such as Singular Value Decomposition and node2vec fail to capture significant structural aspects of real-world complex networks. Furthermore, we empirically study a number of different embedding techniques based on dot product, and show that they all fail to capture the triangle structure.
Detection and Description of Change in Visual Streams
Authors: Davis Gilton, Ruotian Luo, Rebecca Willett, Greg Shakhnarovich
Date: 2020-03-27 00:00:00
Tasks: Change Detection, Representation Learning

This paper presents a framework for the analysis of changes in visual streams: ordered sequences of images, possibly separated by significant time gaps. We propose a new approach to incorporating unlabeled data into training to generate natural language descriptions of change. We also develop a framework for estimating the time of change in visual stream. We use learned representations for change evidence and consistency of perceived change, and combine these in a regularized graph cut based change detector. Experimental evaluation on visual stream datasets, which we release as part of our contribution, shows that representation learning driven by natural language descriptions significantly improves change detection accuracy, compared to methods that do not rely on language.
MCFlow: Monte Carlo Flow Models for Data Imputation
Authors: Trevor W. Richardson, Wencheng Wu, Lei Lin, Beilei Xu, Edgar A. Bernal
Date: 2020-03-27 00:00:00
Proceeding: CVPR 2020 6
Tasks: Imputation

We consider the topic of data imputation, a foundational task in machine learning that addresses issues with missing data. To that end, we propose MCFlow, a deep framework for imputation that leverages normalizing flow generative models and Monte Carlo sampling. We address the causality dilemma that arises when training models with incomplete data by introducing an iterative learning scheme which alternately updates the density estimate and the values of the missing entries in the training data. We provide extensive empirical validation of the effectiveness of the proposed method on standard multivariate and image datasets, and benchmark its performance against state-of-the-art alternatives. We demonstrate that MCFlow is superior to competing methods in terms of the quality of the imputed data, as well as with regards to its ability to preserve the semantic structure of the data.
On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery
Authors: Qian Wang, Neelanjan Bhowmik, Toby P. Breckon
Date: 2020-03-27 00:00:00
Tasks: 3D Object Detection, Computed Tomography (CT), General Classification, Object, object-detection, Object Detection

X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation security screening whilst prior work on prohibited item detection focuses primarily on 2D X-ray imagery. In this paper, we aim to evaluate the possibility of extending the automatic prohibited item detection from 2D X-ray imagery to volumetric 3D CT baggage security screening imagery. To these ends, we take advantage of 3D Convolutional Neural Neworks (CNN) and popular object detection frameworks such as RetinaNet and Faster R-CNN in our work. As the first attempt to use 3D CNN for volumetric 3D CT baggage security screening, we first evaluate different CNN architectures on the classification of isolated prohibited item volumes and compare against traditional methods which use hand-crafted features. Subsequently, we evaluate object detection performance of different architectures on volumetric 3D CT baggage images. The results of our experiments on Bottle and Handgun datasets demonstrate that 3D CNN models can achieve comparable performance (98% true positive rate and 1.5% false positive rate) to traditional methods but require significantly less time for inference (0.014s per volume). Furthermore, the extended 3D object detection models achieve promising performance in detecting prohibited items within volumetric 3D CT baggage imagery with 76% mAP for bottles and 88% mAP for handguns, which shows both the challenge and promise of such threat detection within 3D CT X-ray security imagery.
SceneCAD: Predicting Object Alignments and Layouts in RGB-D Scans
Authors: Armen Avetisyan, Tatiana Khanova, Christopher Choy, Denver Dash, Angela Dai, Matthias Nießner
Date: 2020-03-27 00:00:00
Proceeding: ECCV 2020 8
Tasks: Graph Neural Network, Object

We present a novel approach to reconstructing lightweight, CAD-based representations of scanned 3D environments from commodity RGB-D sensors. Our key idea is to jointly optimize for both CAD model alignments as well as layout estimations of the scanned scene, explicitly modeling inter-relationships between objects-to-objects and objects-to-layout. Since object arrangement and scene layout are intrinsically coupled, we show that treating the problem jointly significantly helps to produce globally-consistent representations of a scene. Object CAD models are aligned to the scene by establishing dense correspondences between geometry, and we introduce a hierarchical layout prediction approach to estimate layout planes from corners and edges of the scene.To this end, we propose a message-passing graph neural network to model the inter-relationships between objects and layout, guiding generation of a globally object alignment in a scene. By considering the global scene layout, we achieve significantly improved CAD alignments compared to state-of-the-art methods, improving from 41.83% to 58.41% alignment accuracy on SUNCG and from 50.05% to 61.24% on ScanNet, respectively. The resulting CAD-based representations makes our method well-suited for applications in content creation such as augmented- or virtual reality.
Acceleration of Convolutional Neural Network Using FFT-Based Split Convolutions
Authors: Kamran Chitsaz, Mohsen Hajabdollahi, Nader Karimi, Shadrokh Samavi, Shahram Shirani
Date: 2020-03-27 00:00:00
Tasks: Quantization

Convolutional neural networks (CNNs) have a large number of variables and hence suffer from a complexity problem for their implementation. Different methods and techniques have developed to alleviate the problem of CNN's complexity, such as quantization, pruning, etc. Among the different simplification methods, computation in the Fourier domain is regarded as a new paradigm for the acceleration of CNNs. Recent studies on Fast Fourier Transform (FFT) based CNN aiming at simplifying the computations required for FFT. However, there is a lot of space for working on the reduction of the computational complexity of FFT. In this paper, a new method for CNN processing in the FFT domain is proposed, which is based on input splitting. There are problems in the computation of FFT using small kernels in situations such as CNN. Splitting can be considered as an effective solution for such issues aroused by small kernels. Using splitting redundancy, such as overlap-and-add, is reduced and, efficiency is increased. Hardware implementation of the proposed FFT method, as well as different analyses of the complexity, are performed to demonstrate the proper performance of the proposed method.
Image compression optimized for 3D reconstruction by utilizing deep neural networks
Authors: Alex Golts, Yoav Y. Schechner
Date: 2020-03-27 00:00:00
Tasks: 3D Reconstruction, Image Compression

Computer vision tasks are often expected to be executed on compressed images. Classical image compression standards like JPEG 2000 are widely used. However, they do not account for the specific end-task at hand. Motivated by works on recurrent neural network (RNN)-based image compression and three-dimensional (3D) reconstruction, we propose unified network architectures to solve both tasks jointly. These joint models provide image compression tailored for the specific task of 3D reconstruction. Images compressed by our proposed models, yield 3D reconstruction performance superior as compared to using JPEG 2000 compression. Our models significantly extend the range of compression rates for which 3D reconstruction is possible. We also show that this can be done highly efficiently at almost no additional cost to obtain compression on top of the computation already required for performing the 3D reconstruction task.