2021

Permanent URI for this collectionhttps://hdl.handle.net/20.500.14570/2695

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    3D Head Model Estimation from a Single Photo
    (2021) Zatserkovnyi, Rostyslav
    Today, 3D human head models are widely used in fields such as computer vision, entertainment, healthcare, and biometrics. Since a high-quality scan of a human head is expensive and time-consuming to obtain, machine learning algorithms are used to estimate the shape and texture of a 3D model from a single "in-the-wild" photograph, often taken at extreme angles or with non-uniform illumination. However, as a full head texture cannot be trivially inferred from a single photograph due to self-occlusion, many only focus on modeling an incomplete and partially textured model of the human head. This work proposes a machine learning pipeline that reconstructs a fully textured 3D head model from a single photograph. We collect a novel dataset of 99.3 thousand high-resolution human head textures created from synthetic celebrity photographs. To the best of our knowledge, this is the first UV texture dataset of a similar scale and fidelity. Using this dataset, we train a free-form inpainting GAN that learns to recreate full head textures from partially obscured projections of the input photograph.
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    Real-time inverse kinematics and inverse dynamics from motion capture
    (2021) Zabava, Kateryna
    This work applies machine learning to solving inverse dynamics and inverse kinematics tasks from the motion capture data. This approach may simplify the calculation process and help do scientific simulations as part of a physics engine that describes the neural control of human motion and decodes movement intent in individuals with neural damage. The existing algorithm has to be modified for every experiment and takes a significant amount of time to execute. It is also sensitive to noise and missing data, and it is not a real-time calculation. We propose a solution of inverse kinematics tasks with neural networks. Here we report accuracy results both on clean data and noisy data. We also apply a similar approach for the inverse dynamics task. The approach shows high accuracy on clean data, but this accuracy decreases if applied to the noisy data.
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    Automated Fact-checking for Wikipedia
    (2021) Trokhymovych, Mykola
    The incoming flow of information is continuously increasing along with the disinformation piece that can harm society. Filtering unreliable content helps keep Wikipedia as free as possible of disinformation, making it one of the most significant reliable information sources. Consequently, Wikipedia’s knowledge base is widely used for facts verification academic research. The main goal of our work is to transform recent academic achievements into a practical open-source Wikipedia-based fact-checking application that is both accurate and efficient. We review the primary NLI related datasets and study their relevant limitations. As a result, we propose the data filtering method that improves the model’s performance and generalization. We show that transfer learning for NLI models are not working well, and complete model training is needed to achieve the best result on a specific dataset. We come up with an unsupervised fine-tuning of the Masked Language model on field-specific texts for model domain adaptation. Finally, we present the new fact-checking system WikiCheck API that automatically performs a facts validation process based on the Wikipedia knowledge base. It is comparable to SOTA solutions in terms of accuracy and can be used on low memory CPU instances.
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    Improving Sequence Tagging for Grammatical Error Correction
    (2021) Tarnavskyi, Maksym
    In this work, we investigated the recent sequence tagging approach for the Grammatical Error Correction task. We compared the impact of different transformerbased encoders of base and large configurations and showed the influence of tags’ vocabulary size. Also, we discovered ensembling methods on data and model levels. We proposed two methods for selecting better quality data and filtering noisy data. We generated new training GEC data based on knowledge distillation from an ensemble of models and discovered strategies for its usage. Our best ensemble without pre-training on the synthetic data achieves a new SOTA result of an F0.5 76.05 on BEA-2019 (test), in contrast, when the newest obtained results were achieved with pre-training on synthetic data. Our best single model with pre-training on synthetic data achieves F0.5 of 73.21 on BEA-2019 (test). Our investigation improved the previous results by 0.8/2.45 points for the single/ensemble sequence tagging models. The code, generated datasets, and trained models are publicly available.
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    Modeling and Prediction of Alzheimer’s Disease Progression
    (2021) Smailova, Sevil
    Alzheimer’s Disease is an irreversible disease that causes a decline in cognitive abilities and leads to dementia. Many efforts are applied to understand the behavior of the disease progression and foresee its future state. The metrics that assess the level of cognition are named as cognitive scores. The dynamics of cognitive scores help understand the future disease progression. However, there is a lack of understanding on what is the best benchmark for the predicted value of the cognitive score. Moreover, there could be cases when the future value of the cognitive score is not statistically different comparing to the current value. In this work we discover those patients that by design cannot have the dynamics in their progression of cognitive scores. We justify that the dynamics of progression for Cognitively Normal patients do not change over five years. We reveal that there is no statistically significant change in progression after the 1-year follow-ups. We unified the evaluation framework of different imputation, feature selection methods and machine learning models on different time to prediction settings as well as on different patient populations.
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    Central pattern generator model using spiking neural networks
    (2021) Pryyma, Yuriy
    are essential for survival. The neural locomotor pathways contain the central pattern generator (CPG), a network of neurons embedded into the spinal cord and generating dynamic output for walking and running. Even though there are multiple formulations of the CPG, from coupled oscillators to complex networks of Hodgkin-Huxley neurons, the optimal choice of model implementation depends on its application. The choice of a formulation is often described as the trade-off between complexity and the level of details in the model’s function. However, the advantages between different formulations have not been established. Recently, the spiking neural networks (SNN) have gained popularity as a biological analog for neural dynamics that uses methodology developed for artificial neural networks. This formulation uses spiking frequency instead of rate signals to accomplish dynamic computations with the integrate-and-fire neurons. In this study1, we aimed to create the framework for comparing a versatile CPG rate model and its implementation with the model build with SNN. We used a neuromorphic software package (Nengo) to develop and validate a bilateral CPG model’s structural and functional details based on the halfcenter oscillators. The spiking model shows similar precision for calculating the empirical phase-duration characteristics of gait in cats as the rate model, and it also reproduces the linear relationship between the CPG input and the empirical limb speed of forward progression. While the phase characteristic was used to optimize neural dynamics, the input relationship with the limb speed is the product of the model structure. Furthermore, the spiking model has increased tolerance to temporal noise, and it can withstand some structural damage. The spiking and rate models require further comparative analysis. Overall, the development of adaptable spiking models could help integrate the biomimetic components within the control systems for assistive robotics and electrical stimulation devices to rehabilitate locomotion after central and peripheral injuries.
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    Incorporating Metadata for Semantic Segmentation by employing Channel Attention Mechanism
    (2021) Plutenko, Iaroslav
    The meta-information accompanying data from image acquisition devices has limited use in microscopy image processing techniques involving Deep Learning. This project aims to incorporate the supplementary metadata for semantic segmentation by employing a channel selection mechanism in convolutional networks outlining its potential benefits and practical applications where metadata can be used for switching tasks within a master model. The results of conducted experiments show that meta-information is helpful, and the phenomenon is more expressed with incompatible segmentation tasks, where a multi-head model or separate models are required otherwise. Overall, we have achieved a slight increase in scores for similar tasks as well and demonstrated the applicability of the CNN model for separate tasks, forcing it to work as an ensemble, leveraging the beneficial effect of multi-task learning.
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    Robust Visual Odometry for Realistic PointGoal Navigation
    (2021) Partsey, Ruslan
    The ability to navigate in complex environments is a fundamental skill of a home robot. Despite extensive study, indoor navigation in unseen environments under noisy actuation and sensing and without access to precise localization continues to be an open frontier for research in Embodied AI. In this work, we focus on designing a visual odometry module for robust egomotion estimation and it’s integration with navigation policy for efficient navigation under noisy actuation and sensing. Specifically, we study how the observations transformations and incorporating meta-information available to the navigation agent impacts visual odometry model generalization performance. We present a set of regularization techniques that can be implemented as train- and test-time augmentations to increase the robustness to noise. Navigation agent, equipped with our visual odometry module, reaches the goal in 86% of episodes and scores 0.66 SPL in Habitat Challenge 2021 benchmark.
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    Point cloud human pose estimation using capsule networks
    (2021) Onbysh, Oleksandr
    Human pose estimation based on points cloud is an emerging field that develops with 3D scanning devices’ popularity. Build-in LiDAR technology in mobile phones and a growing VR market creates a demand for lightweight and accurate models for 3D point cloud. Widely advanced deep learning tools are mainly used for structured data, and they face new challenges in unstructured 3D space. Recent research on capsule networks proves that this type of model outperforms classical CNN architectures in tasks that require viewpoint invariance from the model. Thus capsule networks challenge multiple issues of classic CNNs like preserving the orientation and spatial relationship of extracted features, which could significantly improve the 3D points cloud classification task’s performance. The project’s objective is to experimentally assess the applicability of capsule neural network architecture to the task of point cloud human pose estimation and measure performance on non-synthetic data. Additionally, measure noise sustainability of capsule networks for 3D data compared to regular models. Compare models’ performance with restricted amount of training data.
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