pwc-archive/papers-with-abstracts huggingface dataset:
Lidar based Detection and Classification of Pedestrians and Vehicles Using Machine Learning Methods
Authors: Farzad Shafiei Dizaji
Date: 2019-06-12 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Object, object-detection, Object Detection, Real-Time Object Detection, Self-Driving Cars

The goal of this paper is to classify objects mapped by LiDAR sensor into different classes such as vehicles, pedestrians and bikers. Utilizing a LiDAR-based object detector and Neural Networks-based classifier, a novel real-time object detection is presented essentially with respect to aid self-driving vehicles in recognizing and classifying other objects encountered in the course of driving and proceed accordingly. We discuss our work using machine learning methods to tackle a common high-level problem found in machine learning applications for self-driving cars: the classification of pointcloud data obtained from a 3D LiDAR sensor.
Data Extraction from Charts via Single Deep Neural Network
Authors: Xiaoyi Liu, Diego Klabjan, Patrick NBless
Date: 2019-06-06 00:00:00
Tasks: object-detection, Object Detection

Automatic data extraction from charts is challenging for two reasons: there exist many relations among objects in a chart, which is not a common consideration in general computer vision problems; and different types of charts may not be processed by the same model. To address these problems, we propose a framework of a single deep neural network, which consists of object detection, text recognition and object matching modules. The framework handles both bar and pie charts, and it may also be extended to other types of charts by slight revisions and by augmenting the training data. Our model performs successfully on 79.4% of test simulated bar charts and 88.0% of test simulated pie charts, while for charts outside of the training domain it degrades for 57.5% and 62.3%, respectively.
Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval Manuscripts
Authors: Michele Alberti, Lars Vögtlin, Vinaychandran Pondenkandath, Mathias Seuret, Rolf Ingold, Marcus Liwicki
Date: 2019-06-11 00:00:00
Tasks: Denoising, Segmentation, Semantic Segmentation, Text-Line Extraction

This paper introduces a new way for text-line extraction by integrating deep-learning based pre-classification and state-of-the-art segmentation methods. Text-line extraction in complex handwritten documents poses a significant challenge, even to the most modern computer vision algorithms. Historical manuscripts are a particularly hard class of documents as they present several forms of noise, such as degradation, bleed-through, interlinear glosses, and elaborated scripts. In this work, we propose a novel method which uses semantic segmentation at pixel level as intermediate task, followed by a text-line extraction step. We measured the performance of our method on a recent dataset of challenging medieval manuscripts and surpassed state-of-the-art results by reducing the error by 80.7%. Furthermore, we demonstrate the effectiveness of our approach on various other datasets written in different scripts. Hence, our contribution is two-fold. First, we demonstrate that semantic pixel segmentation can be used as strong denoising pre-processing step before performing text line extraction. Second, we introduce a novel, simple and robust algorithm that leverages the high-quality semantic segmentation to achieve a text-line extraction performance of 99.42% line IU on a challenging dataset.
A synthetic dataset for deep learning
Authors: Xinjie Lan
Date: 2019-06-01 00:00:00
Tasks: Deep Learning

In this paper, we propose a novel method for generating a synthetic dataset obeying Gaussian distribution. Compared to the commonly used benchmark datasets with unknown distribution, the synthetic dataset has an explicit distribution, i.e., Gaussian distribution. Meanwhile, it has the same characteristics as the benchmark dataset MNIST. As a result, we can easily apply Deep Neural Networks (DNNs) on the synthetic dataset. This synthetic dataset provides a novel experimental tool to verify the proposed theories of deep learning.
Team JL Solution to Google Landmark Recognition 2019
Authors: Yinzheng Gu, Chuanpeng Li
Date: 2019-06-10 00:00:00
Tasks: Landmark Recognition, Re-Ranking, Retrieval

In this paper, we describe our solution to the Google Landmark Recognition 2019 Challenge held on Kaggle. Due to the large number of classes, noisy data, imbalanced class sizes, and the presence of a significant amount of distractors in the test set, our method is based mainly on retrieval techniques with both global and local CNN approaches. Our full pipeline, after ensembling the models and applying several steps of re-ranking strategies, scores 0.37606 GAP on the private leaderboard which won the 1st place in the competition.
Comparing Energy Efficiency of CPU, GPU and FPGA Implementations for Vision Kernels
Authors: Murad Qasaimeh, Kristof Denolf, Jack Lo, Kees Vissers, Joseph Zambreno, Phillip H. Jones
Date: 2019-05-31 00:00:00
Tasks: CPU, GPU, Navigate

Developing high performance embedded vision applications requires balancing run-time performance with energy constraints. Given the mix of hardware accelerators that exist for embedded computer vision (e.g. multi-core CPUs, GPUs, and FPGAs), and their associated vendor optimized vision libraries, it becomes a challenge for developers to navigate this fragmented solution space. To aid with determining which embedded platform is most suitable for their application, we conduct a comprehensive benchmark of the run-time performance and energy efficiency of a wide range of vision kernels. We discuss rationales for why a given underlying hardware architecture innately performs well or poorly based on the characteristics of a range of vision kernel categories. Specifically, our study is performed for three commonly used HW accelerators for embedded vision applications: ARM57 CPU, Jetson TX2 GPU and ZCU102 FPGA, using their vendor optimized vision libraries: OpenCV, VisionWorks and xfOpenCV. Our results show that the GPU achieves an energy/frame reduction ratio of 1.1-3.2x compared to the others for simple kernels. While for more complicated kernels and complete vision pipelines, the FPGA outperforms the others with energy/frame reduction ratios of 1.2-22.3x. It is also observed that the FPGA performs increasingly better as a vision application's pipeline complexity grows.
Coloring With Limited Data: Few-Shot Colorization via Memory-Augmented Networks
Authors: Seungjoo Yoo, Hyojin Bahng, Sunghyo Chung, Junsoo Lee, Jaehyuk Chang, Jaegul Choo
Date: 2019-06-09 00:00:00
Tasks: Colorization, Few-Shot Learning, Triplet

Despite recent advancements in deep learning-based automatic colorization, they are still limited when it comes to few-shot learning. Existing models require a significant amount of training data. To tackle this issue, we present a novel memory-augmented colorization model MemoPainter that can produce high-quality colorization with limited data. In particular, our model is able to capture rare instances and successfully colorize them. We also propose a novel threshold triplet loss that enables unsupervised training of memory networks without the need of class labels. Experiments show that our model has superior quality in both few-shot and one-shot colorization tasks.
Characterizing Bias in Classifiers using Generative Models
Authors: Daniel McDuff, Shuang Ma, Yale Song, Ashish Kapoor
Date: 2019-05-30 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Bayesian Optimization, image-classification, Image Classification

Models that are learned from real-world data are often biased because the data used to train them is biased. This can propagate systemic human biases that exist and ultimately lead to inequitable treatment of people, especially minorities. To characterize bias in learned classifiers, existing approaches rely on human oracles labeling real-world examples to identify the "blind spots" of the classifiers; these are ultimately limited due to the human labor required and the finite nature of existing image examples. We propose a simulation-based approach for interrogating classifiers using generative adversarial models in a systematic manner. We incorporate a progressive conditional generative model for synthesizing photo-realistic facial images and Bayesian Optimization for an efficient interrogation of independent facial image classification systems. We show how this approach can be used to efficiently characterize racial and gender biases in commercial systems.
HalalNet: A Deep Neural Network that Classifies the Halalness Slaughtered Chicken from their Images
Authors: A. Elfakharany, R. Yusof, N. Ismail, R. Arfa, M. Yunus
Date: 2019-06-10 00:00:00
Tasks: One-Shot Learning, Transfer Learning

Halal requirement in food is important for millions of Muslims worldwide especially for meat and chicken products, insuring that slaughter houses adhere to this requirement is a challenging task to do manually. In this paper a method is proposed that uses a camera that takes images of slaughtered chicken on the conveyor in a slaughter house, the images are then analyzed by a deep neural network to classify if the image is of a halal slaughtered chicken or not. However, traditional deep learning models require large amounts of data to train on, which in this case these amounts of data were challenging to collect especially the images of non-halal slaughtered chicken, hence this paper shows how the use of one shot learning [1] and transfer learning [2] can reach high accuracy on the few amounts of data that were available. The architecture used is based on the Siamese neural networks architecture which ranks the similarity between two inputs [3] while using the Xception network [4] as the twin networks. We call it HalalNet. This work was done as part of SYCUT (syriah compliant slaughtering system) which is a monitoring system that monitors the halalness of the slaughtered chicken in a slaughter house. The data used to train and validate HalalNet was collected from the Azain slaughtering site (Semenyih, Selangor, Malaysia) containing images of both halal and non-halal slaughtered chicken.
A database for face presentation attack using wax figure faces
Authors: Shan Jia, Chuanbo Hu, Guodong Guo, Zhengquan Xu
Date: 2019-06-06 00:00:00
Tasks: Diversity, Face Presentation Attack Detection, Face Recognition, Small Data Image Classification

Compared to 2D face presentation attacks (e.g. printed photos and video replays), 3D type attacks are more challenging to face recognition systems (FRS) by presenting 3D characteristics or materials similar to real faces. Existing 3D face spoofing databases, however, mostly based on 3D masks, are restricted to small data size or poor authenticity due to the production difficulty and high cost. In this work, we introduce the first wax figure face database, WFFD, as one type of super-realistic 3D presentation attacks to spoof the FRS. This database consists of 2200 images with both real and wax figure faces (totally 4400 faces) with a high diversity from online collections. Experiments on this database first investigate the vulnerability of three popular FRS to this kind of new attack. Further, we evaluate the performance of several face presentation attack detection methods to show the attack abilities of this super-realistic face spoofing database.
The Worrisome Impact of an Inter-rater Bias on Neural Network Training
Authors: Or Shwartzman, Harel Gazit, Ilan Shelef, Tammy Riklin-Raviv
Date: 2019-06-12 00:00:00
Tasks: Segmentation, Semantic Segmentation

The problem of inter-rater variability is often discussed in the context of manual labeling of medical images. The emergence of data-driven approaches such as Deep Neural Networks (DNNs) brought this issue of raters' disagreement to the front-stage. In this paper, we highlight the issue of inter-rater bias as opposed to random inter-observer variability and demonstrate its influence on DNN training, leading to different segmentation results for the same input images. In fact, lower overlap scores are obtained between the outputs of a DNN trained on annotations of one rater and tested on another. Moreover, we demonstrate that inter-rater bias in the training examples is amplified and becomes more consistent, considering the segmentation predictions of the DNNs' test data. We support our findings by showing that a classifier-DNN trained to distinguish between raters based on their manual annotations performs better when the automatic segmentation predictions rather than the actual raters' annotations were tested. For this study, we used two different datasets: the ISBI 2015 Multiple Sclerosis (MS) challenge dataset, including MRI scans each with annotations provided by two raters with different levels of expertise; and Intracerebral Hemorrhage (ICH) CT scans with manual and semi-manual segmentations. The results obtained allow us to underline a worrisome clinical implication of a DNN bias induced by an inter-rater bias during training. Specifically, we present a consistent underestimate of MS-lesion loads when calculated from segmentation predictions of a DNN trained on input provided by the less experienced rater. In the same manner, the differences in ICH volumes calculated based on outputs of identical DNNs, each trained on annotations from a different source are more consistent and larger than the differences in volumes between the manual and semi-manual annotations used for training.
Instant automatic diagnosis of diabetic retinopathy
Authors: Gwenolé Quellec, Mathieu Lamard, Bruno Lay, Alexandre Le Guilcher, Ali Erginay, Béatrice Cochener, Pascale Massin
Date: 2019-06-12 00:00:00

The purpose of this study is to evaluate the performance of the OphtAI system for the automatic detection of referable diabetic retinopathy (DR) and the automatic assessment of DR severity using color fundus photography. OphtAI relies on ensembles of convolutional neural networks trained to recognize eye laterality, detect referable DR and assess DR severity. The system can either process single images or full examination records. To document the automatic diagnoses, accurate heatmaps are generated. The system was developed and validated using a dataset of 763,848 images from 164,660 screening procedures from the OPHDIAT screening program. For comparison purposes, it was also evaluated in the public Messidor-2 dataset. Referable DR can be detected with an area under the ROC curve of AUC = 0.989 in the Messidor-2 dataset, using the University of Iowa's reference standard (95% CI: 0.984-0.994). This is better than the only AI system authorized by the FDA, evaluated in the exact same conditions (AUC = 0.980). OphtAI can also detect vision-threatening DR with an AUC of 0.997 (95% CI: 0.996-0.998) and proliferative DR with an AUC of 0.997 (95% CI: 0.995-0.999). The system runs in 0.3 seconds using a graphics processing unit and less than 2 seconds without. OphtAI is safer, faster and more comprehensive than the only AI system authorized by the FDA so far. Instant DR diagnosis is now possible, which is expected to streamline DR screening and to give easy access to DR screening to more diabetic patients.
DVDnet: A Fast Network for Deep Video Denoising
Authors: Matias Tassano, Julie Delon, Thomas Veit
Date: 2019-06-04 00:00:00
Tasks: Denoising, Video Denoising

In this paper, we propose a state-of-the-art video denoising algorithm based on a convolutional neural network architecture. Previous neural network based approaches to video denoising have been unsuccessful as their performance cannot compete with the performance of patch-based methods. However, our approach outperforms other patch-based competitors with significantly lower computing times. In contrast to other existing neural network denoisers, our algorithm exhibits several desirable properties such as a small memory footprint, and the ability to handle a wide range of noise levels with a single network model. The combination between its denoising performance and lower computational load makes this algorithm attractive for practical denoising applications. We compare our method with different state-of-art algorithms, both visually and with respect to objective quality metrics. The experiments show that our algorithm compares favorably to other state-of-art methods. Video examples, code and models are publicly available at \url{https://github.com/m-tassano/dvdnet}.
A New Compensatory Genetic Algorithm-Based Method for Effective Compressed Multi-function Convolutional Neural Network Model Selection with Multi-Objective Optimization
Authors: Luna M. Zhang
Date: 2019-06-08 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Model Selection

In recent years, there have been many popular Convolutional Neural Networks (CNNs), such as Google's Inception-V4, that have performed very well for various image classification problems. These commonly used CNN models usually use the same activation function, such as RELU, for all neurons in the convolutional layers; they are "Single-function CNNs." However, SCNNs may not always be optimal. Thus, a "Multi-function CNN" (MCNN), which uses different activation functions for different neurons, has been shown to outperform a SCNN. Also, CNNs typically have very large architectures that use a lot of memory and need a lot of data in order to be trained well. As a result, they tend to have very high training and prediction times too. An important research problem is how to automatically and efficiently find the best CNN with both high classification performance and compact architecture with high training and prediction speeds, small power usage, and small memory size for any image classification problem. It is very useful to intelligently find an effective, fast, energy-efficient, and memory-efficient "Compressed Multi-function CNN" (CMCNN) from a large number of candidate MCNNs. A new compensatory algorithm using a new genetic algorithm (GA) is created to find the best CMCNN with an ideal compensation between performance and architecture size. The optimal CMCNN has the best performance and the smallest architecture size. Simulations using the CIFAR10 dataset showed that the new compensatory algorithm could find CMCNNs that could outperform non-compressed MCNNs in terms of classification performance (F1-score), speed, power usage, and memory usage. Other effective, fast, power-efficient, and memory-efficient CMCNNs based on popular CNN architectures will be developed for image classification problems in important real-world applications, such as brain informatics and biomedical imaging.
The Online Resources Shared on Twitter About the #MeToo Movement: The Pareto Principle
Authors: Iman Tahamtan, Javad Seif
Date: 2019-06-10 00:00:00

In this paper we examine the most influential resources shared on Twitter about the #MeToo movement. We also examine whether a small proportion of domain names and URLs (e.g. 20%) appear in a large number of tweets (e.g. 80%) that contain #MeToo (known as the 80/20 rule or Pareto principle). R and Python were used to analyze the data. Results demonstrated that the most frequently shared domains were twitter.com (47.20%), nytimes.com (4.42%) and youtube.com (3.69%). The most frequently shared content was a recent poll which indicated "men are afraid to mentor women after the #MeToo movement". In accordance with the Pareto principle, 8% of domain names accounted for 80% of the shared content on Twitter that contained #MeToo. This study provides a base for researchers who are interested in understanding what online resources people rely on when sharing information about online social movements (e.g. #MeToo).
CDTB: A Color and Depth Visual Object Tracking Dataset and Benchmark
Authors: Alan Lukežič, Ugur Kart, Jani Käpylä, Ahmed Durmush, Joni-Kristian Kämäräinen, Jiří Matas, Matej Kristan
Date: 2019-07-01 00:00:00
Tasks: Benchmarking, Object Tracking, Visual Object Tracking

A long-term visual object tracking performance evaluation methodology and a benchmark are proposed. Performance measures are designed by following a long-term tracking definition to maximize the analysis probing strength. The new measures outperform existing ones in interpretation potential and in better distinguishing between different tracking behaviors. We show that these measures generalize the short-term performance measures, thus linking the two tracking problems. Furthermore, the new measures are highly robust to temporal annotation sparsity and allow annotation of sequences hundreds of times longer than in the current datasets without increasing manual annotation labor. A new challenging dataset of carefully selected sequences with many target disappearances is proposed. A new tracking taxonomy is proposed to position trackers on the short-term/long-term spectrum. The benchmark contains an extensive evaluation of the largest number of long-term tackers and comparison to state-of-the-art short-term trackers. We analyze the influence of tracking architecture implementations to long-term performance and explore various re-detection strategies as well as influence of visual model update strategies to long-term tracking drift. The methodology is integrated in the VOT toolkit to automate experimental analysis and benchmarking and to facilitate future development of long-term trackers.
Unsupervised predictive coding models may explain visual brain representation
Authors: Marcio Fonseca
Date: 2019-06-30 00:00:00
Tasks: image-classification, Image Classification

Deep predictive coding networks are neuroscience-inspired unsupervised learning models that learn to predict future sensory states. We build upon the PredNet implementation by Lotter, Kreiman, and Cox (2016) to investigate if predictive coding representations are useful to predict brain activity in the visual cortex. We use representational similarity analysis (RSA) to compare PredNet representations to functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data from the Algonauts Project. In contrast to previous findings in the literature (Khaligh-Razavi &Kriegeskorte, 2014), we report empirical data suggesting that unsupervised models trained to predict frames of videos may outperform supervised image classification baselines. Our best submission achieves an average noise normalized score of 16.67% and 27.67% on the fMRI and MEG tracks of the Algonauts Challenge.