pwc-archive/papers-with-abstracts huggingface dataset:
UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-Reps
Authors: Peter Meltzer, Hooman Shayani, Amir Khasahmadi, Pradeep Kumar Jayaraman, Aditya Sanghi, Joseph Lambourne
Date: 2021-04-28 00:00:00
Proceeding: ICCV 2021 10
Tasks: Computational Efficiency, Few-Shot Learning, Unsupervised Few-Shot Learning

Boundary Representations (B-Reps) are the industry standard in 3D Computer Aided Design/Manufacturing (CAD/CAM) and industrial design due to their fidelity in representing stylistic details. However, they have been ignored in the 3D style research. Existing 3D style metrics typically operate on meshes or pointclouds, and fail to account for end-user subjectivity by adopting fixed definitions of style, either through crowd-sourcing for style labels or hand-crafted features. We propose UVStyle-Net, a style similarity measure for B-Reps that leverages the style signals in the second order statistics of the activations in a pre-trained (unsupervised) 3D encoder, and learns their relative importance to a subjective end-user through few-shot learning. Our approach differs from all existing data-driven 3D style methods since it may be used in completely unsupervised settings, which is desirable given the lack of publicly available labelled B-Rep datasets. More importantly, the few-shot learning accounts for the inherent subjectivity associated with style. We show quantitatively that our proposed method with B-Reps is able to capture stronger style signals than alternative methods on meshes and pointclouds despite its significantly greater computational efficiency. We also show it is able to generate meaningful style gradients with respect to the input shape, and that few-shot learning with as few as two positive examples selected by an end-user is sufficient to significantly improve the style measure. Finally, we demonstrate its efficacy on a large unlabeled public dataset of CAD models. Source code and data will be released in the future.
NeRD: Neural 3D Reflection Symmetry Detector
Authors: Yichao Zhou, Shichen Liu, Yi Ma
Date: 2021-04-19 00:00:00
Proceeding: CVPR 2021 1
Tasks: Pose Estimation, regression

Recent advances have shown that symmetry, a structural prior that most objects exhibit, can support a variety of single-view 3D understanding tasks. However, detecting 3D symmetry from an image remains a challenging task. Previous works either assume that the symmetry is given or detect the symmetry with a heuristic-based method. In this paper, we present NeRD, a Neural 3D Reflection Symmetry Detector, which combines the strength of learning-based recognition and geometry-based reconstruction to accurately recover the normal direction of objects' mirror planes. Specifically, we first enumerate the symmetry planes with a coarse-to-fine strategy and then find the best ones by building 3D cost volumes to examine the intra-image pixel correspondence from the symmetry. Our experiments show that the symmetry planes detected with our method are significantly more accurate than the planes from direct CNN regression on both synthetic and real-world datasets. We also demonstrate that the detected symmetry can be used to improve the performance of downstream tasks such as pose estimation and depth map regression. The code of this paper has been made public at https://github.com/zhou13/nerd.
Automating Visual Blockage Classification of Culverts with Deep Learning
Authors: Umair Iqbal, Johan Barthelemy, Wanqing Li, Pascal Perez
Date: 2021-04-21 00:00:00
Tasks: Classification, Deep Learning, General Classification

Blockage of culverts by transported debris materials is reported as main contributor in originating urban flash floods. Conventional modelling approaches had no success in addressing the problem largely because of unavailability of peak floods hydraulic data and highly non-linear behaviour of debris at culvert. This article explores a new dimension to investigate the issue by proposing the use of Intelligent Video Analytic (IVA) algorithms for extracting blockage related information. Potential of using existing Convolutional Neural Network (CNN) algorithms (i.e., DarkNet53, DenseNet121, InceptionResNetV2, InceptionV3, MobileNet, ResNet50, VGG16, EfficientNetB3, NASNet) is investigated over a custom collected blockage dataset (i.e., Images of Culvert Openings and Blockage (ICOB)) to predict the blockage in a given image. Models were evaluated based on their performance on test dataset (i.e., accuracy, loss, precision, recall, F1-score, Jaccard-Index), Floating Point Operations Per Second (FLOPs) and response times to process a single test instance. From the results, NASNet was reported most efficient in classifying the blockage with the accuracy of 85\%; however, EfficientNetB3 was recommended for the hardware implementation because of its improved response time with accuracy comparable to NASNet (i.e., 83\%). False Negative (FN) instances, False Positive (FP) instances and CNN layers activation suggested that background noise and oversimplified labelling criteria were two contributing factors in degraded performance of existing CNN algorithms.
Regression on Deep Visual Features using Artificial Neural Networks (ANNs) to Predict Hydraulic Blockage at Culverts
Authors: Umair Iqbal, Johan Barthelemy, Wanqing Li, Pascal Perez
Date: 2021-04-25 00:00:00
Tasks: regression

Cross drainage hydraulic structures (i.e., culverts, bridges) in urban landscapes are prone to getting blocked by transported debris which often results in causing the flash floods. In context of Australia, Wollongong City Council (WCC) blockage conduit policy is the only formal guideline to consider blockage in design process. However, many argue that this policy is based on the post floods visual inspections and hence can not be considered accurate representation of hydraulic blockage. As a result of this on-going debate, visual blockage and hydraulic blockage are considered two distinct terms with no established quantifiable relation among both. This paper attempts to relate both terms by proposing the use of deep visual features for prediction of hydraulic blockage at a given culvert. An end-to-end machine learning pipeline is propounded which takes an image of culvert as input, extract visual features using deep learning models, pre-process the visual features and feed into regression model to predict the corresponding hydraulic blockage. Dataset (i.e., Hydrology-Lab Dataset (HD), Visual Hydrology-Lab Dataset (VHD)) used in this research was collected from in-lab experiments carried out using scaled physical models of culverts where multiple blockage scenarios were replicated at scale. Performance of regression models was assessed using standard evaluation metrics. Furthermore, performance of overall machine learning pipeline was assessed in terms of processing times for relative comparison of models and hardware requirement analysis. From the results ANN used with MobileNet extracted visual features achieved the best regression performance with $R^{2}$ score of 0.7855. Positive value of $R^{2}$ score indicated the presence of correlation between visual features and hydraulic blockage and suggested that both can be interrelated with each other.
Favelas 4D: Scalable methods for morphology analysis of informal settlements using terrestrial laser scanning data
Authors: Arianna Salazar Miranda, Guangyu Du, Claire Gorman, Fabio Duarte, Washington Fajardo, Carlo Ratti
Date: 2021-04-23 00:00:00
Tasks: Morphological Analysis

One billion people live in informal settlements worldwide. The complex and multilayered spaces that characterize this unplanned form of urbanization pose a challenge to traditional approaches to mapping and morphological analysis. This study proposes a methodology to study the morphological properties of informal settlements based on terrestrial LiDAR (Light Detection and Ranging) data collected in Rocinha, the largest favela in Rio de Janeiro, Brazil. Our analysis operates at two resolutions, including a \emph{global} analysis focused on comparing different streets of the favela to one another, and a \emph{local} analysis unpacking the variation of morphological metrics within streets. We show that our methodology reveals meaningful differences and commonalities both in terms of the global morphological characteristics across streets and their local distributions. Finally, we create morphological maps at high spatial resolution from LiDAR data, which can inform urban planning assessments of concerns related to crowding, structural safety, air quality, and accessibility in the favela. The methods for this study are automated and can be easily scaled to analyze entire informal settlements, leveraging the increasing availability of inexpensive LiDAR scanners on portable devices such as cellphones.
Towards Accurate Text-based Image Captioning with Content Diversity Exploration
Authors: Guanghui Xu, Shuaicheng Niu, Mingkui Tan, Yucheng Luo, Qing Du, Qi Wu
Date: 2021-04-23 00:00:00
Proceeding: CVPR 2021 1
Tasks: Caption Generation, Diversity, Image Captioning

Text-based image captioning (TextCap) which aims to read and reason images with texts is crucial for a machine to understand a detailed and complex scene environment, considering that texts are omnipresent in daily life. This task, however, is very challenging because an image often contains complex texts and visual information that is hard to be described comprehensively. Existing methods attempt to extend the traditional image captioning methods to solve this task, which focus on describing the overall scene of images by one global caption. This is infeasible because the complex text and visual information cannot be described well within one caption. To resolve this difficulty, we seek to generate multiple captions that accurately describe different parts of an image in detail. To achieve this purpose, there are three key challenges: 1) it is hard to decide which parts of the texts of images to copy or paraphrase; 2) it is non-trivial to capture the complex relationship between diverse texts in an image; 3) how to generate multiple captions with diverse content is still an open problem. To conquer these, we propose a novel Anchor-Captioner method. Specifically, we first find the important tokens which are supposed to be paid more attention to and consider them as anchors. Then, for each chosen anchor, we group its relevant texts to construct the corresponding anchor-centred graph (ACG). Last, based on different ACGs, we conduct multi-view caption generation to improve the content diversity of generated captions. Experimental results show that our method not only achieves SOTA performance but also generates diverse captions to describe images.
Mini-batch graphs for robust image classification
Authors: Arnab Kumar Mondal, Vineet Jain, Kaleem Siddiqi
Date: 2021-04-22 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Scene Classification

Current deep learning models for classification tasks in computer vision are trained using mini-batches. In the present article, we take advantage of the relationships between samples in a mini-batch, using graph neural networks to aggregate information from similar images. This helps mitigate the adverse effects of alterations to the input images on classification performance. Diverse experiments on image-based object and scene classification show that this approach not only improves a classifier's performance but also increases its robustness to image perturbations and adversarial attacks. Further, we also show that mini-batch graph neural networks can help to alleviate the problem of mode collapse in Generative Adversarial Networks.
PCE-PINNs: Physics-Informed Neural Networks for Uncertainty Propagation in Ocean Modeling
Authors: Björn Lütjens, Catherine H. Crawford, Mark Veillette, Dava Newman
Date: 2021-05-05 00:00:00
Tasks: Management

Climate models project an uncertainty range of possible warming scenarios from 1.5 to 5 degree Celsius global temperature increase until 2100, according to the CMIP6 model ensemble. Climate risk management and infrastructure adaptation requires the accurate quantification of the uncertainties at the local level. Ensembles of high-resolution climate models could accurately quantify the uncertainties, but most physics-based climate models are computationally too expensive to run as ensemble. Recent works in physics-informed neural networks (PINNs) have combined deep learning and the physical sciences to learn up to 15k faster copies of climate submodels. However, the application of PINNs in climate modeling has so far been mostly limited to deterministic models. We leverage a novel method that combines polynomial chaos expansion (PCE), a classic technique for uncertainty propagation, with PINNs. The PCE-PINNs learn a fast surrogate model that is demonstrated for uncertainty propagation of known parameter uncertainties. We showcase the effectiveness in ocean modeling by using the local advection-diffusion equation.
GCM: A Toolkit for Generating Synthetic Code-mixed Text
Authors: Mohd Sanad Zaki Rizvi, Anirudh Srinivasan, Tanuja Ganu, Monojit Choudhury, Sunayana Sitaram
Date: 2021-04-19 00:00:00
Proceeding: EACL 2021 2

Code-mixing is common in multilingual communities around the world, and processing it is challenging due to the lack of labeled and unlabeled data. We describe a tool that can automatically generate code-mixed data given parallel data in two languages. We implement two linguistic theories of code-mixing, the Equivalence Constraint theory and the Matrix Language theory to generate all possible code-mixed sentences in the language-pair, followed by sampling of the generated data to generate natural code-mixed sentences. The toolkit provides three modes: a batch mode, an interactive library mode and a web-interface to address the needs of researchers, linguists and language experts. The toolkit can be used to generate unlabeled text data for pre-trained models, as well as visualize linguistic theories of code-mixing. We plan to release the toolkit as open source and extend it by adding more implementations of linguistic theories, visualization techniques and better sampling techniques. We expect that the release of this toolkit will help facilitate more research in code-mixing in diverse language pairs.
Deep learning methods allow fully automated segmentation of metacarpal bones to quantify volumetric bone mineral density
Authors: Lukas Folle, Timo Meinderink, David Simon, Anna-Maria Liphardt, Gerhard Krönke, Georg Schett, Arnd Kleyer, Andreas Maier
Date: 2021-05-06 00:00:00
Tasks: 3D Instance Segmentation

Arthritis patients develop hand bone loss, which leads to destruction and functional impairment of the affected joints. High resolution peripheral quantitative computed tomography (HR-pQCT) allows the quantification of volumetric bone mineral density (vBMD) and bone microstructure in vivo with an isotropic voxel size of 82 micrometres. However, image-processing to obtain bone characteristics is a time-consuming process as it requires semi-automatic segmentation of the bone. In this work, a fully automatic vBMD measurement pipeline for the metacarpal (MC) bone using deep learning methods is introduced. Based on a dataset of HR-pQCT volumes with MC measurements for 541 patients with arthritis, a segmentation network is trained. The best network achieves an intersection over union as high as 0.94 and a Dice similarity coefficient of 0.97 while taking only 33 s to process a whole patient yielding a speedup between 2.5 and 4.0 for the whole workflow. Strong correlation between the vBMD measurements of the expert and of the automatic pipeline are achieved for the average bone density with 0.999 (Pearson) and 0.996 (Spearman’s rank) with p<0.001 for all correlations. A qualitative assessment of the network predictions and the manual annotations yields a 65.9% probability that the expert favors the network predictions. Further, the steps to integrate the pipeline into the clinical workflow are shown. In order to make these workflow improvements available to others, we openly share the code of this work.
Bregman divergence as general framework to estimate unnormalized statistical models
Authors: Michael Gutmann, Jun-Ichiro Hirayama
Date: 2012-02-14 00:00:00

We show that the Bregman divergence provides a rich framework to estimate unnormalized statistical models for continuous or discrete random variables, that is, models which do not integrate or sum to one, respectively. We prove that recent estimation methods such as noise-contrastive estimation, ratio matching, and score matching belong to the proposed framework, and explain their interconnection based on supervised learning. Further, we discuss the role of boosting in unsupervised learning.
Self-supervised learning for tool wear monitoring with a disentangled-variational-autoencoder
Authors: Tim von Hahn, Chris K. Mechefske
Date: 2021-03-31 00:00:00
Tasks: Anomaly Detection, Feature Engineering, Self-Supervised Learning

The use of end-to-end deep learning in Machinery Health Monitoring allows ma-chine learning models to be created without the need for feature engineering. The research presented here expands on this use in the context of tool wear monitoring. A disentangled-variational-autoencoder, with a temporal convolutional neural network, is used to model and trend tool wear in a self-supervised manner, and anomaly detection is used to make predictions from both the input and latent spaces. The method achieves a precision-recall area-under-curve (PR-AUC) score of 0.45 across all cutting parameters on a milling data set, and a top score of 0.80for shallow depth cuts. The method achieves a top PR-AUC score of 0.41 on areal-world industrial CNC data set, but the method does not generalize as well across the broad range of manufactured parts. The benefits of the approach, along with the drawbacks, are discussed in detail. The code for the experiment is available at: https://github.com/tvhahn/ml-tool-wear
Aerospace Sliding Mode Control Toolbox: Relative Degree Approach with Resource Prospector Lander and Launch Vehicle Case Studies
Authors: S. Kode, Y. Shtessel, A. Levant, J. Rakoczy, M. Hannan, J. Orr
Date: 2021-05-08 00:00:00

Conventional Sliding mode control and observation techniques are widely used in aerospace applications, including aircrafts, UAVs, launch vehicles, missile interceptors, and hypersonic missiles. This work is dedicated to creating a MATLAB-based sliding mode controller design and simulation software toolbox that aims to support aerospace vehicle applications. An architecture of the aerospace sliding mode control toolbox (SMC Aero) using the relative degree approach is proposed. The SMC Aero libraries include 1st order sliding mode control (1-SMC), second order sliding mode control (2-SMC), higher order sliding mode (HOSM) control (either fixed gain or adaptive), as well as higher order sliding mode differentiators. The efficacy of the SMC Aero toolbox is confirmed in two case studies: controlling and simulating resource prospector lander (RPL) soft landing on the Moon and launch vehicle (LV) attitude control in ascent mode.
Estimation of 3D Human Pose Using Prior Knowledge
Authors: Shu Chen, Lei Zhang, Beiji Zou
Date: 2021-05-09 00:00:00
Tasks: Pose Estimation

Estimating three-dimensional human poses from the positions of two-dimensional joints has shown promising results.However, using two-dimensional joint coordinates as input loses more information than image-based approaches and results in ambiguity.In order to overcome this problem, we combine bone length and camera parameters with two-dimensional joint coordinates for input.This combination is more discriminative than the two-dimensional joint coordinates in that it can improve the accuracy of the model's prediction depth and alleviate the ambiguity that comes from projecting three-dimensional coordinates into two-dimensional space. Furthermore, we introduce direction constraints which can better measure the difference between the ground truth and the output of the proposed model. The experimental results on the H36M show that the method performed better than other state-of-the-art three-dimensional human pose estimation approaches.
Generative Adversarial Registration for Improved Conditional Deformable Templates
Authors: Neel Dey, Mengwei Ren, Adrian V. Dalca, Guido Gerig
Date: 2021-05-07 00:00:00
Proceeding: ICCV 2021 10
Tasks: Image Registration, Medical Image Registration, Specificity

Deformable templates are essential to large-scale medical image registration, segmentation, and population analysis. Current conventional and deep network-based methods for template construction use only regularized registration objectives and often yield templates with blurry and/or anatomically implausible appearance, confounding downstream biomedical interpretation. We reformulate deformable registration and conditional template estimation as an adversarial game wherein we encourage realism in the moved templates with a generative adversarial registration framework conditioned on flexible image covariates. The resulting templates exhibit significant gain in specificity to attributes such as age and disease, better fit underlying group-wise spatiotemporal trends, and achieve improved sharpness and centrality. These improvements enable more accurate population modeling with diverse covariates for standardized downstream analyses and easier anatomical delineation for structures of interest.
Visual Grounding with Transformers
Authors: Ye Du, Zehua Fu, Qingjie Liu, Yunhong Wang
Date: 2021-05-10 00:00:00
Tasks: Decoder

In this paper, we propose a transformer based approach for visual grounding. Unlike previous proposal-and-rank frameworks that rely heavily on pretrained object detectors or proposal-free frameworks that upgrade an off-the-shelf one-stage detector by fusing textual embeddings, our approach is built on top of a transformer encoder-decoder and is independent of any pretrained detectors or word embedding models. Termed VGTR -- Visual Grounding with TRansformers, our approach is designed to learn semantic-discriminative visual features under the guidance of the textual description without harming their location ability. This information flow enables our VGTR to have a strong capability in capturing context-level semantics of both vision and language modalities, rendering us to aggregate accurate visual clues implied by the description to locate the interested object instance. Experiments show that our method outperforms state-of-the-art proposal-free approaches by a considerable margin on five benchmarks while maintaining fast inference speed.
Toward asynchronous EEG-based BCI: Detecting imagined words segments in continuous EEG signals
Authors: Tonatiuh Hernández-Del-Toro, Carlos A. Reyes-García, Luis Villaseñor-Pineda
Date: 2021-04-13 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG)

An asynchronous Brain--Computer Interface (BCI) based on imagined speech is a tool that allows to control an external device or to emit a message at the moment the user desires to by decoding EEG signals of imagined speech. In order to correctly implement these types of BCI, we must be able to detect from a continuous signal, when the subject starts to imagine words. In this work, five methods of feature extraction based on wavelet decomposition, empirical mode decomposition, frequency energies, fractal dimension and chaos theory features are presented to solve the task of detecting imagined words segments from continuous EEG signals as a preliminary study for a latter implementation of an asynchronous BCI based on imagined speech. These methods are tested in three datasets using four different classifiers and the higher F1 scores obtained are 0.73, 0.79, and 0.68 for each dataset, respectively. This results are promising to build a system that automatizes the segmentation of imagined words segments for latter classification.