pwc-archive/papers-with-abstracts huggingface dataset:
MP-ResNet: Multi-path Residual Network for the Semantic segmentation of High-Resolution PolSAR Images
Authors: Lei Ding, Kai Zheng, Dong Lin, Yuxing Chen, Bing Liu, Jiansheng Li, Lorenzo Bruzzone
Date: 2020-11-10 00:00:00
Tasks: Decoder, Segmentation, Semantic Segmentation, valid

There are limited studies on the semantic segmentation of high-resolution Polarimetric Synthetic Aperture Radar (PolSAR) images due to the scarcity of training data and the inference of speckle noises. The Gaofen contest has provided open access of a high-quality PolSAR semantic segmentation dataset. Taking this chance, we propose a Multi-path ResNet (MP-ResNet) architecture for the semantic segmentation of high-resolution PolSAR images. Compared to conventional U-shape encoder-decoder convolutional neural network (CNN) architectures, the MP-ResNet learns semantic context with its parallel multi-scale branches, which greatly enlarges its valid receptive fields and improves the embedding of local discriminative features. In addition, MP-ResNet adopts a multi-level feature fusion design in its decoder to make the best use of the features learned from its different branches. Ablation studies show that the MPResNet has significant advantages over its baseline method (FCN with ResNet34). It also surpasses several classic state-of-the-art methods in terms of overall accuracy (OA), mean F1 and fwIoU, whereas its computational costs are not much increased. This CNN architecture can be used as a baseline method for future studies on the semantic segmentation of PolSAR images. The code is available at: https://github.com/ggsDing/SARSeg.
On-Device Language Identification of Text in Images using Diacritic Characters
Authors: Shubham Vatsal, Nikhil Arora, Gopi Ramena, Sukumar Moharana, Dhruval Jain, Naresh Purre, Rachit S Munjal
Date: 2020-11-10 00:00:00
Tasks: Language Identification, object-detection, Object Detection, Optical Character Recognition, Optical Character Recognition (OCR)

Diacritic characters can be considered as a unique set of characters providing us with adequate and significant clue in identifying a given language with considerably high accuracy. Diacritics, though associated with phonetics often serve as a distinguishing feature for many languages especially the ones with a Latin script. In this proposed work, we aim to identify language of text in images using the presence of diacritic characters in order to improve Optical Character Recognition (OCR) performance in any given automated environment. We showcase our work across 13 Latin languages encompassing 85 diacritic characters. We use an architecture similar to Squeezedet for object detection of diacritic characters followed by a shallow network to finally identify the language. OCR systems when accompanied with identified language parameter tends to produce better results than sole deployment of OCR systems. The discussed work apart from guaranteeing an improvement in OCR results also takes on-device (mobile phone) constraints into consideration in terms of model size and inference time.
Multi-pooled Inception features for no-reference image quality assessment
Authors: Domonkos Varga
Date: 2020-11-10 00:00:00
Tasks: Image Quality Assessment, No-Reference Image Quality Assessment

Image quality assessment (IQA) is an important element of a broad spectrum of applications ranging from automatic video streaming to display technology. Furthermore, the measurement of image quality requires a balanced investigation of image content and features. Our proposed approach extracts visual features by attaching global average pooling (GAP) layers to multiple Inception modules of on an ImageNet database pretrained convolutional neural network (CNN). In contrast to previous methods, we do not take patches from the input image. Instead, the input image is treated as a whole and is run through a pretrained CNN body to extract resolution-independent, multi-level deep features. As a consequence, our method can be easily generalized to any input image size and pretrained CNNs. Thus, we present a detailed parameter study with respect to the CNN base architectures and the effectiveness of different deep features. We demonstrate that our best proposal - called MultiGAP-NRIQA - is able to provide state-of-the-art results on three benchmark IQA databases. Furthermore, these results were also confirmed in a cross database test using the LIVE In the Wild Image Quality Challenge database.
Pixel precise unsupervised detection of viral particle proliferation in cellular imaging data
Authors: Birgitta Dresp-Langley, John M. Wandeto
Date: 2020-11-10 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Quantization

Cellular and molecular imaging techniques and models have been developed to characterize single stages of viral proliferation after focal infection of cells in vitro. The fast and automatic classification of cell imaging data may prove helpful prior to any further comparison of representative experimental data to mathematical models of viral propagation in host cells. Here, we use computer generated images drawn from a reproduction of an imaging model from a previously published study of experimentally obtained cell imaging data representing progressive viral particle proliferation in host cell monolayers. Inspired by experimental time-based imaging data, here in this study viral particle increase in time is simulated by a one-by-one increase, across images, in black or gray single pixels representing dead or partially infected cells, and hypothetical remission by a one-by-one increase in white pixels coding for living cells in the original image model. The image simulations are submitted to unsupervised learning by a Self-Organizing Map (SOM) and the Quantization Error in the SOM output (SOM-QE) is used for automatic classification of the image simulations as a function of the represented extent of viral particle proliferation or cell recovery. Unsupervised classification by SOM-QE of 160 model images, each with more than three million pixels, is shown to provide a statistically reliable, pixel precise, and fast classification model that outperforms human computer-assisted image classification by RGB image mean computation. The automatic classification procedure proposed here provides a powerful approach to understand finely tuned mechanisms in the infection and proliferation of virus in cell lines in vitro or other cells.
The Virtual Goniometer: A new method for measuring angles on 3D models of fragmentary bone and lithics
Authors: Katrina Yezzi-Woodley, Jeff Calder, Peter J. Olver, Annie Melton, Paige Cody, Thomas Huffstutler, Alexander Terwilliger, Martha Tappen, Reed Coil, Gilbert Tostevin
Date: 2020-11-10 00:00:00

The contact goniometer is a commonly used tool in lithic and zooarchaeological analysis, despite suffering from a number of shortcomings due to the physical interaction between the measuring implement, the object being measured, and the individual taking the measurements. However, lacking a simple and efficient alternative, researchers in a variety of fields continue to use the contact goniometer to this day. In this paper, we present a new goniometric method that we call the virtual goniometer, which takes angle measurements virtually on a 3D model of an object. The virtual goniometer allows for rapid data collection, and for the measurement of many angles that cannot be physically accessed by a manual goniometer. We compare the intra-observer variability of the manual and virtual goniometers, and find that the virtual goniometer is far more consistent and reliable. Furthermore, the virtual goniometer allows for precise replication of angle measurements, even among multiple users, which is important for reproducibility of goniometric-based research. The virtual goniometer is available as a plug-in in the open source mesh processing packages Meshlab and Blender, making it easily accessible to researchers exploring the potential for goniometry to improve archaeological methods and address anthropological questions.
AIM 2020 Challenge on Rendering Realistic Bokeh
Authors: Andrey Ignatov, Radu Timofte, Ming Qian, Congyu Qiao, Jiamin Lin, Zhenyu Guo, Chenghua Li, Cong Leng, Jian Cheng, Juewen Peng, Xianrui Luo, Ke Xian, Zijin Wu, Zhiguo Cao, Densen Puthussery, Jiji C V, Hrishikesh P S, Melvin Kuriakose, Saikat Dutta, Sourya Dipta Das, Nisarg A. Shah, Kuldeep Purohit, Praveen Kandula, Maitreya Suin, A. N. Rajagopalan, Saagara M B, Minnu A L, Sanjana A R, Praseeda S, Ge Wu, Xueqin Chen, Tengyao Wang, Max Zheng, Hulk Wong, Jay Zou
Date: 2020-11-10 00:00:00
Tasks: Bokeh Effect Rendering

This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world bokeh simulation problem, where the goal was to learn a realistic shallow focus technique using a large-scale EBB! bokeh dataset consisting of 5K shallow / wide depth-of-field image pairs captured using the Canon 7D DSLR camera. The participants had to render bokeh effect based on only one single frame without any additional data from other cameras or sensors. The target metric used in this challenge combined the runtime and the perceptual quality of the solutions measured in the user study. To ensure the efficiency of the submitted models, we measured their runtime on standard desktop CPUs as well as were running the models on smartphone GPUs. The proposed solutions significantly improved the baseline results, defining the state-of-the-art for practical bokeh effect rendering problem.
Tattoo tomography: Freehand 3D photoacoustic image reconstruction with an optical pattern
Authors: Niklas Holzwarth, Melanie Schellenberg, Janek Gröhl, Kris Dreher, Jan-Hinrich Nölke, Alexander Seitel, Minu D. Tizabi, Beat P. Müller-Stich, Lena Maier-Hein
Date: 2020-11-10 00:00:00
Tasks: 3D Reconstruction, Image Reconstruction

Purpose: Photoacoustic tomography (PAT) is a novel imaging technique that can spatially resolve both morphological and functional tissue properties, such as the vessel topology and tissue oxygenation. While this capacity makes PAT a promising modality for the diagnosis, treatment and follow-up of various diseases, a current drawback is the limited field-of-view (FoV) provided by the conventionally applied 2D probes. Methods: In this paper, we present a novel approach to 3D reconstruction of PAT data (Tattoo tomography) that does not require an external tracking system and can smoothly be integrated into clinical workflows. It is based on an optical pattern placed on the region of interest prior to image acquisition. This pattern is designed in a way that a tomographic image of it enables the recovery of the probe pose relative to the coordinate system of the pattern. This allows the transformation of a sequence of acquired PA images into one common global coordinate system and thus the consistent 3D reconstruction of PAT imaging data. Results: An initial feasibility study conducted with experimental phantom data and in vivo forearm data indicates that the Tattoo approach is well-suited for 3D reconstruction of PAT data with high accuracy and precision. Conclusion: In contrast to previous approaches to 3D ultrasound (US) or PAT reconstruction, the Tattoo approach neither requires complex external hardware nor training data acquired for a specific application. It could thus become a valuable tool for clinical freehand PAT.
Noise2Stack: Improving Image Restoration by Learning from Volumetric Data
Authors: Mikhail Papkov, Kenny Roberts, Lee Ann Madissoon, Omer Bayraktar, Dmytro Fishman, Kaupo Palo, Leopold Parts
Date: 2020-11-10 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration

Biomedical images are noisy. The imaging equipment itself has physical limitations, and the consequent experimental trade-offs between signal-to-noise ratio, acquisition speed, and imaging depth exacerbate the problem. Denoising is, therefore, an essential part of any image processing pipeline, and convolutional neural networks are currently the method of choice for this task. One popular approach, Noise2Noise, does not require clean ground truth, and instead, uses a second noisy copy as a training target. Self-supervised methods, like Noise2Self and Noise2Void, relax data requirements by learning the signal without an explicit target but are limited by the lack of information in a single image. Here, we introduce Noise2Stack, an extension of the Noise2Noise method to image stacks that takes advantage of a shared signal between spatially neighboring planes. Our experiments on magnetic resonance brain scans and newly acquired multiplane microscopy data show that learning only from image neighbors in a stack is sufficient to outperform Noise2Noise and Noise2Void and close the gap to supervised denoising methods. Our findings point towards low-cost, high-reward improvement in the denoising pipeline of multiplane biomedical images. As a part of this work, we release a microscopy dataset to establish a benchmark for the multiplane image denoising.
A Soft Computing Approach for Selecting and Combining Spectral Bands
Authors: Juan F. H. Albarracín, Rafael S. Oliveira, Marina Hirota, Jefersson A. dos Santos, Ricardo da S. Torres
Date: 2020-11-10 00:00:00
Tasks: Classification, General Classification, Time Series, Time Series Analysis

We introduce a soft computing approach for automatically selecting and combining indices from remote sensing multispectral images that can be used for classification tasks. The proposed approach is based on a Genetic-Programming (GP) framework, a technique successfully used in a wide variety of optimization problems. Through GP, it is possible to learn indices that maximize the separability of samples from two different classes. Once the indices specialized for all the pairs of classes are obtained, they are used in pixelwise classification tasks. We used the GP-based solution to evaluate complex classification problems, such as those that are related to the discrimination of vegetation types within and between tropical biomes. Using time series defined in terms of the learned spectral indices, we show that the GP framework leads to superior results than other indices that are used to discriminate and classify tropical biomes.
Classification of optics-free images with deep neural networks
Authors: Soren Nelson, Rajesh Menon
Date: 2020-11-10 00:00:00
Tasks: Binary Classification, Classification, General Classification

The thinnest possible camera is achieved by removing all optics, leaving only the image sensor. We train deep neural networks to perform multi-class detection and binary classification (with accuracy of 92%) on optics-free images without the need for anthropocentric image reconstructions. Inferencing from optics-free images has the potential for enhanced privacy and power efficiency.
Principles of Stochastic Computing: Fundamental Concepts and Applications
Authors: S. Rahimi Kari
Date: 2020-11-08 00:00:00

The semiconductor and IC industry is facing the issue of high energy consumption. In modern days computers and processing systems are designed based on the Turing machine and Von Neumann's architecture. This architecture mainly focused on designing systems based on deterministic behaviors. To tackle energy consumption and reliability in systems, Stochastic Computing was introduced. In this research, we aim to review and study the principles behind stochastic computing and its implementation techniques. By utilizing stochastic computing, we can achieve higher energy efficiency and smaller area sizes in terms of designing arithmetic units. Also, we aim to popularize the affiliation of Stochastic systems in designing futuristic BLSI and Neuromorphic systems.
OpenKinoAI: An Open Source Framework for Intelligent Cinematography and Editing of Live Performances
Authors: Rémi Ronfard, Rémi Colin de Verdière
Date: 2020-10-30 00:00:00
Tasks: Video Editing

OpenKinoAI is an open source framework for post-production of ultra high definition video which makes it possible to emulate professional multiclip editing techniques for the case of single camera recordings. OpenKinoAI includes tools for uploading raw video footage of live performances on a remote web server, detecting, tracking and recognizing the performers in the original material, reframing the raw video into a large choice of cinematographic rushes, editing the rushes into movies, and annotating rushes and movies for documentation purposes. OpenKinoAI is made available to promote research in multiclip video editing of ultra high definition video, and to allow performing artists and companies to use this research for archiving, documenting and sharing their work online in an innovative fashion.
EPSR: Edge Profile Super resolution
Authors: Jiun Lee, Jaekwang Kim, Inyong Yun
Date: 2020-11-09 00:00:00
Tasks: Image Super-Resolution, SSIM, Super-Resolution

In this paper, we propose Edge Profile Super Resolution(EPSR) method to preserve structure information and to restore texture. We make EPSR by stacking modified Fractal Residual Network(mFRN) structures hierarchically and repeatedly. mFRN is made up of lots of Residual Edge Profile Blocks(REPBs) consisting of three different modules such as Residual Efficient Channel Attention Block(RECAB) module, Edge Profile(EP) module, and Context Network(CN) module. RECAB produces more informative features with high frequency components. From the feature, EP module produce structure informed features by generating edge profile itself. Finally, CN module captures details by exploiting high frequency information such as texture and structure with proper sharpness. As repeating the procedure in mFRN structure, our EPSR could extract high-fidelity features and thus it prevents texture loss and preserves structure with appropriate sharpness. Experimental results present that our EPSR achieves competitive performance against state-of-the-art methods in PSNR and SSIM evaluation metrics as well as visual results.
Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning
Authors: Hammam Alshazly, Christoph Linse, Erhardt Barth, Thomas Martinetz
Date: 2020-11-09 00:00:00
Tasks: Specificity, Transfer Learning

This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopt advanced deep network architectures and propose a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conduct extensive sets of experiments on two CT image datasets, namely the SARS-CoV-2 CT-scan and the COVID19-CT. The obtained results show superior performances for our models compared with previous studies, where our best models achieve average accuracy, precision, sensitivity, specificity and F1 score of 99.4%, 99.6%, 99.8%, 99.6% and 99.4% on the SARS-CoV-2 dataset; and 92.9%, 91.3%, 93.7%, 92.2% and 92.5% on the COVID19-CT dataset, respectively. Furthermore, we apply two visualization techniques to provide visual explanations for the models' predictions. The visualizations show well-separated clusters for CT images of COVID-19 from other lung diseases, and accurate localizations of the COVID-19 associated regions.
RMITB at TREC COVID 2020
Authors: Rodger Benham, Alistair Moffat, J. Shane Culpepper
Date: 2020-11-09 00:00:00

Search engine users rarely express an information need using the same query, and small differences in queries can lead to very different result sets. These user query variations have been exploited in past TREC CORE tracks to contribute diverse, highly-effective runs in offline evaluation campaigns with the goal of producing reusable test collections. In this paper, we document the query fusion runs submitted to the first and second round of TREC COVID, using ten queries per topic created by the first author. In our analysis, we focus primarily on the effects of having our second priority run omitted from the judgment pool. This run is of particular interest, as it surfaced a number of relevant documents that were not judged until later rounds of the task. If the additional judgments were included in the first round, the performance of this run increased by 35 rank positions when using RBP p=0.5, highlighting the importance of judgment depth and coverage in assessment tasks.
The improved model of user similarity coefficients computation For recommendation systems
Authors: Yelyzaveta Meleshko, Oleksandr Drieiev, Anas Mahmoud Al-Oraiqat
Date: 2020-11-10 00:00:00
Tasks: Recommendation Systems

The subject matter of the article is a model of calculating the user similarity coefficients of the recommendation systems. The goal is the development of the improved model of user similarity coefficients calculation for recommendation systems to optimize the time of forming recommendation lists. The tasks to be solved are: to investigate the probability of changing user preferences of a recommendation system by comparing their similarity coefficients in time, to investigate which distribution function describes the changes of similarity coefficients of users in time. The methods used are: graph theory, probability theory, radioactivity theory, algorithm theory. Conclusions. In the course of the researches, the model of user similarity coefficients calculating for the recommendation systems has been improved. The model differs from the known ones in that it takes into account the recalculation period of similarity coefficients for the individual user and average recalculation period of similarity coefficients for all users of the system or a specific group of users. The software has been developed, in which a series of experiments was conducted to test the effectiveness of the developed method. The conducted experiments showed that the developed method in general increases the quality of the recommendation system without significant fluctuations of Precision and Recall of the system. Precision and Recall can decrease slightly or increase, depending on the characteristics of the incoming data set. The use of the proposed solutions will increase the application period of the previously calculated similarity coefficients of users for the prediction of preferences without their recalculation and, accordingly, it will shorten the time of formation and issuance of recommendation lists up to 2 times.
Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graph
Authors: Riku Togashi, Mayu Otani, Shin'ichi Satoh
Date: 2020-11-10 00:00:00

Solving cold-start problems is indispensable to provide meaningful recommendation results for new users and items. Under sparsely observed data, unobserved user-item pairs are also a vital source for distilling latent users' information needs. Most present works leverage unobserved samples for extracting negative signals. However, such an optimisation strategy can lead to biased results toward already popular items by frequently handling new items as negative instances. In this study, we tackle the cold-start problems for new users/items by appropriately leveraging unobserved samples. We propose a knowledge graph (KG)-aware recommender based on graph neural networks, which augments labelled samples through pseudo-labelling. Our approach aggressively employs unobserved samples as positive instances and brings new items into the spotlight. To avoid exhaustive label assignments to all possible pairs of users and items, we exploit a KG for selecting probably positive items for each user. We also utilise an improved negative sampling strategy and thereby suppress the exacerbation of popularity biases. Through experiments, we demonstrate that our approach achieves improvements over the state-of-the-art KG-aware recommenders in a variety of scenarios; in particular, our methodology successfully improves recommendation performance for cold-start users/items.