pwc-archive/papers-with-abstracts huggingface dataset:
Fast and Robust Video-Based Exercise Classification via Body Pose Tracking and Scalable Multivariate Time Series Classifiers
Authors: Ashish Singh, Antonio Bevilacqua, Thach Le Nguyen, Feiyan Hu, Kevin McGuinness, Martin OReilly, Darragh Whelan, Brian Caulfield, Georgiana Ifrim
Date: 2022-10-02 00:00:00
Tasks: Pose Estimation, Pose Tracking, Time Series, Time Series Analysis

Technological advancements have spurred the usage of machine learning based applications in sports science. Physiotherapists, sports coaches and athletes actively look to incorporate the latest technologies in order to further improve performance and avoid injuries. While wearable sensors are very popular, their use is hindered by constraints on battery power and sensor calibration, especially for use cases which require multiple sensors to be placed on the body. Hence, there is renewed interest in video-based data capture and analysis for sports science. In this paper, we present the application of classifying S\&C exercises using video. We focus on the popular Military Press exercise, where the execution is captured with a video-camera using a mobile device, such as a mobile phone, and the goal is to classify the execution into different types. Since video recordings need a lot of storage and computation, this use case requires data reduction, while preserving the classification accuracy and enabling fast prediction. To this end, we propose an approach named BodyMTS to turn video into time series by employing body pose tracking, followed by training and prediction using multivariate time series classifiers. We analyze the accuracy and robustness of BodyMTS and show that it is robust to different types of noise caused by either video quality or pose estimation factors. We compare BodyMTS to state-of-the-art deep learning methods which classify human activity directly from videos and show that BodyMTS achieves similar accuracy, but with reduced running time and model engineering effort. Finally, we discuss some of the practical aspects of employing BodyMTS in this application in terms of accuracy and robustness under reduced data quality and size. We show that BodyMTS achieves an average accuracy of 87\%, which is significantly higher than the accuracy of human domain experts.
Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval
Authors: Kei Nishimaki, Kumpei Ikuta, Yuto Onga, Hitoshi Iyatomi, Kenichi Oishi
Date: 2022-10-02 00:00:00
Tasks: Content-Based Image Retrieval, Dimensionality Reduction, Image Retrieval, Retrieval

Content-based image retrieval (CBIR) systems are an emerging technology that supports reading and interpreting medical images. Since 3D brain MR images are high dimensional, dimensionality reduction is necessary for CBIR using machine learning techniques. In addition, for a reliable CBIR system, each dimension in the resulting low-dimensional representation must be associated with a neurologically interpretable region. We propose a localized variational autoencoder (Loc-VAE) that provides neuroanatomically interpretable low-dimensional representation from 3D brain MR images for clinical CBIR. Loc-VAE is based on $\beta$-VAE with the additional constraint that each dimension of the low-dimensional representation corresponds to a local region of the brain. The proposed Loc-VAE is capable of acquiring representation that preserves disease features and is highly localized, even under high-dimensional compression ratios (4096:1). The low-dimensional representation obtained by Loc-VAE improved the locality measure of each dimension by 4.61 points compared to naive $\beta$-VAE, while maintaining comparable brain reconstruction capability and information about the diagnosis of Alzheimer's disease.
DARE: A large-scale handwritten date recognition system
Authors: Christian M. Dahl, Torben S. D. Johansen, Emil N. Sørensen, Christian E. Westermann, Simon F. Wittrock
Date: 2022-10-02 00:00:00
Tasks: Handwritten Text Recognition, Transfer Learning

Handwritten text recognition for historical documents is an important task but it remains difficult due to a lack of sufficient training data in combination with a large variability of writing styles and degradation of historical documents. While recurrent neural network architectures are commonly used for handwritten text recognition, they are often computationally expensive to train and the benefit of recurrence drastically differs by task. For these reasons, it is important to consider non-recurrent architectures. In the context of handwritten date recognition, we propose an architecture based on the EfficientNetV2 class of models that is fast to train, robust to parameter choices, and accurately transcribes handwritten dates from a number of sources. For training, we introduce a database containing almost 10 million tokens, originating from more than 2.2 million handwritten dates which are segmented from different historical documents. As dates are some of the most common information on historical documents, and with historical archives containing millions of such documents, the efficient and automatic transcription of dates has the potential to lead to significant cost-savings over manual transcription. We show that training on handwritten text with high variability in writing styles result in robust models for general handwritten text recognition and that transfer learning from the DARE system increases transcription accuracy substantially, allowing one to obtain high accuracy even when using a relatively small training sample.
Fast OT for Latent Domain Adaptation
Authors: Siddharth Roheda, Ashkan Panahi, Hamid Krim
Date: 2022-10-02 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

In this paper, we address the problem of unsupervised Domain Adaptation. The need for such an adaptation arises when the distribution of the target data differs from that which is used to develop the model and the ground truth information of the target data is unknown. We propose an algorithm that uses optimal transport theory with a verifiably efficient and implementable solution to learn the best latent feature representation. This is achieved by minimizing the cost of transporting the samples from the target domain to the distribution of the source domain.
A Smart Recycling Bin Using Waste Image Classification At The Edge
Authors: Xueying Li, Ryan Grammenos
Date: 2022-10-02 00:00:00
Tasks: image-classification, Image Classification

Rapid economic growth gives rise to the urgent demand for a more efficient waste recycling system. This work thereby developed an innovative recycling bin that automatically separates urban waste to increase the recycling rate. We collected 1800 recycling waste images and combined them with an existing public dataset to train classification models for two embedded systems, Jetson Nano and K210, targeting different markets. The model reached an accuracy of 95.98% on Jetson Nano and 96.64% on K210. A bin program was designed to collect feedback from users. On Jetson Nano, the overall power consumption of the application was reduced by 30% from the previous work to 4.7 W, while the second system, K210, only needed 0.89 W of power to operate. In summary, our work demonstrated a fully functional prototype of an energy-saving, high-accuracy smart recycling bin, which can be commercialized in the future to improve urban waste recycling.
ManiCLIP: Multi-Attribute Face Manipulation from Text
Authors: Hao Wang, Guosheng Lin, Ana García del Molino, Anran Wang, Jiashi Feng, Zhiqi Shen
Date: 2022-10-02 00:00:00
Tasks: Attribute, Text-based Image Editing

In this paper we present a novel multi-attribute face manipulation method based on textual descriptions. Previous text-based image editing methods either require test-time optimization for each individual image or are restricted to single attribute editing. Extending these methods to multi-attribute face image editing scenarios will introduce undesired excessive attribute change, e.g., text-relevant attributes are overly manipulated and text-irrelevant attributes are also changed. In order to address these challenges and achieve natural editing over multiple face attributes, we propose a new decoupling training scheme where we use group sampling to get text segments from same attribute categories, instead of whole complex sentences. Further, to preserve other existing face attributes, we encourage the model to edit the latent code of each attribute separately via an entropy constraint. During the inference phase, our model is able to edit new face images without any test-time optimization, even from complex textual prompts. We show extensive experiments and analysis to demonstrate the efficacy of our method, which generates natural manipulated faces with minimal text-irrelevant attribute editing. Code and pre-trained model are available at https://github.com/hwang1996/ManiCLIP.
ROSIA: Rotation-Search-Based Star Identification Algorithm
Authors: Chee-Kheng Chng, Alvaro Parra Bustos, Benjamin McCarthy, Tat-Jun Chin
Date: 2022-10-02 00:00:00

This paper presents a rotation-search-based approach for addressing the star identification (Star-ID) problem. The proposed algorithm, ROSIA, is a heuristics-free algorithm that seeks the optimal rotation that maximally aligns the input and catalog stars in their respective coordinates. ROSIA searches the rotation space systematically with the Branch-and-Bound (BnB) method. Crucially affecting the runtime feasibility of ROSIA is the upper bound function that prioritizes the search space. In this paper, we make a theoretical contribution by proposing a tight (provable) upper bound function that enables a 400x speed-up compared to an existing formulation. Coupling the bounding function with an efficient evaluation scheme that leverages stereographic projection and the R-tree data structure, ROSIA achieves feasible operational speed on embedded processors with state-of-the-art performances under different sources of noise. The source code of ROSIA is available at https://github.com/ckchng/ROSIA.
Unsupervised Visual Odometry and Action Integration for PointGoal Navigation in Indoor Environment
Authors: Yijun Cao, Xianshi Zhang, Fuya Luo, Chuan Lin, YongJie Li
Date: 2022-10-02 00:00:00
Tasks: Navigate, PointGoal Navigation, Visual Odometry

PointGoal navigation in indoor environment is a fundamental task for personal robots to navigate to a specified point. Recent studies solved this PointGoal navigation task with near-perfect success rate in photo-realistically simulated environments, under the assumptions with noiseless actuation and most importantly, perfect localization with GPS and compass sensors. However, accurate GPS signalis difficult to be obtained in real indoor environment. To improve the PointGoal navigation accuracy without GPS signal, we use visual odometry (VO) and propose a novel action integration module (AIM) trained in unsupervised manner. Sepecifically, unsupervised VO computes the relative pose of the agent from the re-projection error of two adjacent frames, and then replaces the accurate GPS signal with the path integration. The pseudo position estimated by VO is used to train action integration which assists agent to update their internal perception of location and helps improve the success rate of navigation. The training and inference process only use RGB, depth, collision as well as self-action information. The experiments show that the proposed system achieves satisfactory results and outperforms the partially supervised learning algorithms on the popular Gibson dataset.
PCONet: A Convolutional Neural Network Architecture to Detect Polycystic Ovary Syndrome (PCOS) from Ovarian Ultrasound Images
Authors: A. K. M. Salman Hosain, Md Humaion Kabir Mehedi, Irteza Enan Kabir
Date: 2022-10-02 00:00:00
Tasks: Transfer Learning

Polycystic Ovary Syndrome (PCOS) is an endrocrinological dysfunction prevalent among women of reproductive age. PCOS is a combination of syndromes caused by an excess of androgens - a group of sex hormones - in women. Syndromes including acne, alopecia, hirsutism, hyperandrogenaemia, oligo-ovulation, etc. are caused by PCOS. It is also a major cause of female infertility. An estimated 15% of reproductive-aged women are affected by PCOS globally. The necessity of detecting PCOS early due to the severity of its deleterious effects cannot be overstated. In this paper, we have developed PCONet - a Convolutional Neural Network (CNN) - to detect polycistic ovary from ovarian ultrasound images. We have also fine tuned InceptionV3 - a pretrained convolutional neural network of 45 layers - by utilizing the transfer learning method to classify polcystic ovarian ultrasound images. We have compared these two models on various quantitative performance evaluation parameters and demonstrated that PCONet is the superior one among these two with an accuracy of 98.12%, whereas the fine tuned InceptionV3 showcased an accuracy of 96.56% on test images.
Basic Binary Convolution Unit for Binarized Image Restoration Network
Authors: Bin Xia, Yulun Zhang, Yitong Wang, Yapeng Tian, Wenming Yang, Radu Timofte, Luc van Gool
Date: 2022-10-02 00:00:00
Tasks: Binarization, Image Restoration, Model Compression

Lighter and faster image restoration (IR) models are crucial for the deployment on resource-limited devices. Binary neural network (BNN), one of the most promising model compression methods, can dramatically reduce the computations and parameters of full-precision convolutional neural networks (CNN). However, there are different properties between BNN and full-precision CNN, and we can hardly use the experience of designing CNN to develop BNN. In this study, we reconsider components in binary convolution, such as residual connection, BatchNorm, activation function, and structure, for IR tasks. We conduct systematic analyses to explain each component's role in binary convolution and discuss the pitfalls. Specifically, we find that residual connection can reduce the information loss caused by binarization; BatchNorm can solve the value range gap between residual connection and binary convolution; The position of the activation function dramatically affects the performance of BNN. Based on our findings and analyses, we design a simple yet efficient basic binary convolution unit (BBCU). Furthermore, we divide IR networks into four parts and specially design variants of BBCU for each part to explore the benefit of binarizing these parts. We conduct experiments on different IR tasks, and our BBCU significantly outperforms other BNNs and lightweight models, which shows that BBCU can serve as a basic unit for binarized IR networks. All codes and models will be released.
NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review
Authors: Kyle Gao, Yina Gao, Hongjie He, Dening Lu, Linlin Xu, Jonathan Li
Date: 2022-10-01 00:00:00
Tasks: 3D Reconstruction, Autonomous Navigation, NeRF, Novel View Synthesis, Survey

Neural Radiance Field (NeRF) has recently become a significant development in the field of Computer Vision, allowing for implicit, neural network-based scene representation and novel view synthesis. NeRF models have found diverse applications in robotics, urban mapping, autonomous navigation, virtual reality/augmented reality, and more. Due to the growing popularity of NeRF and its expanding research area, we present a comprehensive survey of NeRF papers from the past two years. Our survey is organized into architecture and application-based taxonomies and provides an introduction to the theory of NeRF and its training via differentiable volume rendering. We also present a benchmark comparison of the performance and speed of key NeRF models. By creating this survey, we hope to introduce new researchers to NeRF, provide a helpful reference for influential works in this field, as well as motivate future research directions with our discussion section.
Evaluation of Pre-Trained CNN Models for Geographic Fake Image Detection
Authors: Sid Ahmed Fezza, Mohammed Yasser Ouis, Bachir Kaddar, Wassim Hamidouche, Abdenour Hadid
Date: 2022-10-01 00:00:00
Tasks: Face Swapping, Fake Image Detection

Thanks to the remarkable advances in generative adversarial networks (GANs), it is becoming increasingly easy to generate/manipulate images. The existing works have mainly focused on deepfake in face images and videos. However, we are currently witnessing the emergence of fake satellite images, which can be misleading or even threatening to national security. Consequently, there is an urgent need to develop detection methods capable of distinguishing between real and fake satellite images. To advance the field, in this paper, we explore the suitability of several convolutional neural network (CNN) architectures for fake satellite image detection. Specifically, we benchmark four CNN models by conducting extensive experiments to evaluate their performance and robustness against various image distortions. This work allows the establishment of new baselines and may be useful for the development of CNN-based methods for fake satellite image detection.
Longitudinal Sentiment Analyses for Radicalization Research: Intertemporal Dynamics on Social Media Platforms and their Implications
Authors: Dennis Klinkhammer
Date: 2022-10-01 00:00:00
Tasks: Sentiment Analysis

This discussion paper demonstrates how longitudinal sentiment analyses can depict intertemporal dynamics on social media platforms, what challenges are inherent and how further research could benefit from a longitudinal perspective. Furthermore and since tools for sentiment analyses shall simplify and accelerate the analytical process regarding qualitative data at acceptable inter-rater reliability, their applicability in the context of radicalization research will be examined regarding the Tweets collected on January 6th 2021, the day of the storming of the U.S. Capitol in Washington. Therefore, a total of 49,350 Tweets will be analyzed evenly distributed within three different sequences: before, during and after the U.S. Capitol in Washington was stormed. These sequences highlight the intertemporal dynamics within comments on social media platforms as well as the possible benefits of a longitudinal perspective when using conditional means and conditional variances. Limitations regarding the identification of supporters of such events and associated hate speech as well as common application errors will be demonstrated as well. As a result, only under certain conditions a longitudinal sentiment analysis can increase the accuracy of evidence based predictions in the context of radicalization research.
Gait-based Age Group Classification with Adaptive Graph Neural Network
Authors: Timilehin B. Aderinola, Tee Connie, Thian Song Ong, Andrew Beng Jin Teoh, Michael Kah Ong Goh
Date: 2022-10-01 00:00:00
Tasks: Graph Neural Network, Pose Estimation

Deep learning techniques have recently been utilized for model-free age-associated gait feature extraction. However, acquiring model-free gait demands accurate pre-processing such as background subtraction, which is non-trivial in unconstrained environments. On the other hand, model-based gait can be obtained without background subtraction and is less affected by covariates. For model-based gait-based age group classification problems, present works rely solely on handcrafted features, where feature extraction is tedious and requires domain expertise. This paper proposes a deep learning approach to extract age-associated features from model-based gait for age group classification. Specifically, we first develop an unconstrained gait dataset called Multimedia University Gait Age and Gender dataset (MMU GAG). Next, the body joint coordinates are determined via pose estimation algorithms and represented as compact gait graphs via a novel part aggregation scheme. Then, a Part-AdaptIve Residual Graph Convolutional Neural Network (PairGCN) is designed for age-associated feature learning. Experiments suggest that PairGCN features are far more informative than handcrafted features, yielding up to 99% accuracy for classifying subjects as a child, adult, or senior in the MMU GAG dataset.
Det-SLAM: A semantic visual SLAM for highly dynamic scenes using Detectron2
Authors: Ali Eslamian, Mohammad R. Ahmadzadeh
Date: 2022-10-01 00:00:00
Tasks: Semantic Segmentation, Semantic SLAM, Simultaneous Localization and Mapping

According to experts, Simultaneous Localization and Mapping (SLAM) is an intrinsic part of autonomous robotic systems. Several SLAM systems with impressive performance have been invented and used during the last several decades. However, there are still unresolved issues, such as how to deal with moving objects in dynamic situations. Classic SLAM systems depend on the assumption of a static environment, which becomes unworkable in highly dynamic situations. Several methods have been presented to tackle this issue in recent years, but each has its limitations. This research combines the visual SLAM systems ORB-SLAM3 and Detectron2 to present the Det-SLAM system, which employs depth information and semantic segmentation to identify and eradicate dynamic spots to accomplish semantic SLAM for dynamic situations. Evaluation of public TUM datasets indicates that Det-SLAM is more resilient than previous dynamic SLAM systems and can lower the estimated error of camera posture in dynamic indoor scenarios.
Offline Handwritten Amharic Character Recognition Using Few-shot Learning
Authors: Mesay Samuel, Lars Schmidt-Thieme, DP Sharma, Abiot Sinamo, Abey Bruck
Date: 2022-10-01 00:00:00
Tasks: Few-Shot Learning, image-classification, Image Classification

Few-shot learning is an important, but challenging problem of machine learning aimed at learning from only fewer labeled training examples. It has become an active area of research due to deep learning requiring huge amounts of labeled dataset, which is not feasible in the real world. Learning from a few examples is also an important attempt towards learning like humans. Few-shot learning has proven a very good promise in different areas of machine learning applications, particularly in image classification. As it is a recent technique, most researchers focus on understanding and solving the issues related to its concept by focusing only on common image datasets like Mini-ImageNet and Omniglot. Few-shot learning also opens an opportunity to address low resource languages like Amharic. In this study, offline handwritten Amharic character recognition using few-shot learning is addressed. Particularly, prototypical networks, the popular and simpler type of few-shot learning, is implemented as a baseline. Using the opportunities explored in the nature of Amharic alphabet having row-wise and column-wise similarities, a novel way of augmenting the training episodes is proposed. The experimental results show that the proposed method outperformed the baseline method. This study has implemented few-shot learning for Amharic characters for the first time. More importantly, the findings of the study open new ways of examining the influence of training episodes in few-shot learning, which is one of the important issues that needs exploration. The datasets used for this study are collected from native Amharic language writers using an Android App developed as a part of this study.