Anatomy-guided domain adaptation for 3D in-bed human pose estimation

3D human pose estimation is a key component of clinical monitoring systems. The clinical applicability of deep pose estimation models, however, is limited by their poor generalization under domain shifts along with their need for sufficient labeled training data. As a remedy, we present a novel domain adaptation method, adapting a model from a labeled source to a shifted unlabeled target domain. Our method comprises two complementary adaptation strategies based on prior knowledge about human anatomy. First, we guide the learning process in the target domain by constraining predictions to the space of anatomically plausible poses. To this end, we embed the prior knowledge into an anatomical loss function that penalizes asymmetric limb lengths, implausible bone lengths, and implausible joint angles. Second, we propose to filter pseudo labels for self-training according to their anatomical plausibility and incorporate the concept into the Mean Teacher paradigm. We unify both strategies in a point cloud-based framework applicable to unsupervised and source-free domain adaptation. Evaluation is performed for in-bed pose estimation under two adaptation scenarios, using the public SLP dataset and a newly created dataset. Our method consistently outperforms various state-of-the-art domain adaptation methods, surpasses the baseline model by 31%/66%, and reduces the domain gap by 65%/82%. Source code is available at https://github.com/multimodallearning/da-3dhpe-anatomy.Comment: submitted to Medical Image Analysi

Adversarial content manipulation for analyzing and improving model robustness

The recent rapid progress in machine learning systems has opened up many real-world applications --- from recommendation engines on web platforms to safety critical systems like autonomous vehicles. A model deployed in the real-world will often encounter inputs far from its training distribution. For example, a self-driving car might come across a black stop sign in the wild. To ensure safe operation, it is vital to quantify the robustness of machine learning models to such out-of-distribution data before releasing them into the real-world. However, the standard paradigm of benchmarking machine learning models with fixed size test sets drawn from the same distribution as the training data is insufficient to identify these corner cases efficiently. In principle, if we could generate all valid variations of an input and measure the model response, we could quantify and guarantee model robustness locally. Yet, doing this with real world data is not scalable. In this thesis, we propose an alternative, using generative models to create synthetic data variations at scale and test robustness of target models to these variations. We explore methods to generate semantic data variations in a controlled fashion across visual and text modalities. We build generative models capable of performing controlled manipulation of data like changing visual context, editing appearance of an object in images or changing writing style of text. Leveraging these generative models we propose tools to study robustness of computer vision systems to input variations and systematically identify failure modes. In the text domain, we deploy these generative models to improve diversity of image captioning systems and perform writing style manipulation to obfuscate private attributes of the user. Our studies quantifying model robustness explore two kinds of input manipulations, model-agnostic and model-targeted. The model-agnostic manipulations leverage human knowledge to choose the kinds of changes without considering the target model being tested. This includes automatically editing images to remove objects not directly relevant to the task and create variations in visual context. Alternatively, in the model-targeted approach the input variations performed are directly adversarially guided by the target model. For example, we adversarially manipulate the appearance of an object in the image to fool an object detector, guided by the gradients of the detector. Using these methods, we measure and improve the robustness of various computer vision systems -- specifically image classification, segmentation, object detection and visual question answering systems -- to semantic input variations.Der schnelle Fortschritt von Methoden des maschinellen Lernens hat viele neue Anwendungen ermöglicht – von Recommender-Systemen bis hin zu sicherheitskritischen Systemen wie autonomen Fahrzeugen. In der realen Welt werden diese Systeme oft mit Eingaben außerhalb der Verteilung der Trainingsdaten konfrontiert. Zum Beispiel könnte ein autonomes Fahrzeug einem schwarzen Stoppschild begegnen. Um sicheren Betrieb zu gewährleisten, ist es entscheidend, die Robustheit dieser Systeme zu quantifizieren, bevor sie in der Praxis eingesetzt werden. Aktuell werden diese Modelle auf festen Eingaben von derselben Verteilung wie die Trainingsdaten evaluiert. Allerdings ist diese Strategie unzureichend, um solche Ausnahmefälle zu identifizieren. Prinzipiell könnte die Robustheit “lokal” bestimmt werden, indem wir alle zulässigen Variationen einer Eingabe generieren und die Ausgabe des Systems überprüfen. Jedoch skaliert dieser Ansatz schlecht zu echten Daten. In dieser Arbeit benutzen wir generative Modelle, um synthetische Variationen von Eingaben zu erstellen und so die Robustheit eines Modells zu überprüfen. Wir erforschen Methoden, die es uns erlauben, kontrolliert semantische Änderungen an Bild- und Textdaten vorzunehmen. Wir lernen generative Modelle, die kontrollierte Manipulation von Daten ermöglichen, zum Beispiel den visuellen Kontext zu ändern, die Erscheinung eines Objekts zu bearbeiten oder den Schreibstil von Text zu ändern. Basierend auf diesen Modellen entwickeln wir neue Methoden, um die Robustheit von Bilderkennungssystemen bezüglich Variationen in den Eingaben zu untersuchen und Fehlverhalten zu identifizieren. Im Gebiet von Textdaten verwenden wir diese Modelle, um die Diversität von sogenannten Automatische Bildbeschriftung-Modellen zu verbessern und Schreibtstil-Manipulation zu erlauben, um private Attribute des Benutzers zu verschleiern. Um die Robustheit von Modellen zu quantifizieren, werden zwei Arten von Eingabemanipulationen untersucht: Modell-agnostische und Modell-spezifische Manipulationen. Modell-agnostische Manipulationen basieren auf menschlichem Wissen, um bestimmte Änderungen auszuwählen, ohne das entsprechende Modell miteinzubeziehen. Dies beinhaltet das Entfernen von für die Aufgabe irrelevanten Objekten aus Bildern oder Variationen des visuellen Kontextes. In dem alternativen Modell-spezifischen Ansatz werden Änderungen vorgenommen, die für das Modell möglichst ungünstig sind. Zum Beispiel ändern wir die Erscheinung eines Objekts um ein Modell der Objekterkennung täuschen. Dies ist durch den Gradienten des Modells möglich. Mithilfe dieser Werkzeuge können wir die Robustheit von Systemen zur Bildklassifizierung oder -segmentierung, Objekterkennung und Visuelle Fragenbeantwortung quantifizieren und verbessern

A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Application

Continual learning, also known as incremental learning or life-long learning, stands at the forefront of deep learning and AI systems. It breaks through the obstacle of one-way training on close sets and enables continuous adaptive learning on open-set conditions. In the recent decade, continual learning has been explored and applied in multiple fields especially in computer vision covering classification, detection and segmentation tasks. Continual semantic segmentation (CSS), of which the dense prediction peculiarity makes it a challenging, intricate and burgeoning task. In this paper, we present a review of CSS, committing to building a comprehensive survey on problem formulations, primary challenges, universal datasets, neoteric theories and multifarious applications. Concretely, we begin by elucidating the problem definitions and primary challenges. Based on an in-depth investigation of relevant approaches, we sort out and categorize current CSS models into two main branches including \textit{data-replay} and \textit{data-free} sets. In each branch, the corresponding approaches are similarity-based clustered and thoroughly analyzed, following qualitative comparison and quantitative reproductions on relevant datasets. Besides, we also introduce four CSS specialities with diverse application scenarios and development tendencies. Furthermore, we develop a benchmark for CSS encompassing representative references, evaluation results and reproductions, which is available at~\url{https://github.com/YBIO/SurveyCSS}. We hope this survey can serve as a reference-worthy and stimulating contribution to the advancement of the life-long learning field, while also providing valuable perspectives for related fields.Comment: 20 pages, 12 figures. Undergoing Revie

Learning from aggregated data

Data aggregation is ubiquitous in modern life. Due to various reasons like privacy, scalability, robustness, etc., ground truth data is often subjected to aggregation before being released to the public, or utilised by researchers and analysts. Learning from aggregated data is a challenging problem that requires significant algorithmic innovation, since naive application of standard techniques to aggregated data is vulnerable to the ecological fallacy. In this work, we explore three different versions of this setting. First, we tackle the problem of using generalised linear models when features/covariates are fully observed but the targets are only available as histograms- a common scenario in the healthcare domain where many datasets contain both non-sensitive attributes like age, sex, zip-code, etc., as well as privacy sensitive attributes like healthcare records. We introduce an efficient algorithm that uses alternating data imputation and GLM estimation steps to learn predictive models in this setting. Next, we look at the problem of learning sparse linear models when both features and targets are in aggregated form, specified as empirical estimates of group-wise means computed over different sub-groups of the population. We show that if the true sub-populations are heterogeneous enough, the optimal sparse parameter can be recovered within an arbitrarily small tolerance even in the presence of noise, provided the empirical estimates are obtained from a sufficiently large number of observations. Third, we tackle the scenario of predictive modelling with data that is subjected to spatio-temporal aggregation. We show that by formulating the problem in the frequency domain, we can bypass the mathematical and representational challenges that arise due to non-uniform aggregation, misaligned sampling periods and aliasing. We introduce a novel algorithm that uses restricted Fourier transforms to estimate a linear model which, when applied to spatio-temporally aggregated data, has a generalisation error that is provably close to the optimal performance by the best possible linear model that can be learned from the non-aggregated data set. We then focus our attention on the complementary problem that involves designing aggregation strategies that can allow learning, as well as developing algorithmic techniques that can use only the aggregates to train a model that works on individual samples. We motivate our methods by using the example of Gaussian regression, and subsequently extend our techniques to subsume binary classifiers and generalised linear models. We deonstrate the effectiveness of our techniques with empirical evaluation on data from healthcare and telecommunication. Finally, we present a concrete example of our methods applied to a real life practical problem. Specifically, we consider an application in the domain of online advertising where the complexity of bidding strategies require accurate estimates of most probable cost-per-click or CPC incurred by advertisers, but the data used for training these CPC prediction models are only available as aggregated invoices supplied by an ad publisher on a daily or hourly basis. We introduce a novel learning framework that can use aggregates computed at varying levels of granularity for building individual-level predictive models. We generalise our modelling and algorithmic framework to handle data from diverse domains, and extend our techniques to cover arbitrary aggregation paradigms like sliding windows and overlapping/non-uniform aggregation. We show empirical evidence for the efficacy of our techniques with experiments on both synthetic data and real data from the online advertising domain as well as healthcare to demonstrate the wider applicability of our framework.Electrical and Computer Engineerin

Federated Domain Generalization: A Survey

Machine learning typically relies on the assumption that training and testing distributions are identical and that data is centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly and data is often distributed across different devices, organizations, or edge nodes. Consequently, it is imperative to develop models that can effectively generalize to unseen distributions where data is distributed across different domains. In response to this challenge, there has been a surge of interest in federated domain generalization (FDG) in recent years. FDG combines the strengths of federated learning (FL) and domain generalization (DG) techniques to enable multiple source domains to collaboratively learn a model capable of directly generalizing to unseen domains while preserving data privacy. However, generalizing the federated model under domain shifts is a technically challenging problem that has received scant attention in the research area so far. This paper presents the first survey of recent advances in this area. Initially, we discuss the development process from traditional machine learning to domain adaptation and domain generalization, leading to FDG as well as provide the corresponding formal definition. Then, we categorize recent methodologies into four classes: federated domain alignment, data manipulation, learning strategies, and aggregation optimization, and present suitable algorithms in detail for each category. Next, we introduce commonly used datasets, applications, evaluations, and benchmarks. Finally, we conclude this survey by providing some potential research topics for the future

SoK: Chasing Accuracy and Privacy, and Catching Both in Differentially Private Histogram Publication

Histograms and synthetic data are of key importance in data analysis. However, researchers have shown that even aggregated data such as histograms, containing no obvious sensitive attributes, can result in privacy leakage. To enable data analysis, a strong notion of privacy is required to avoid risking unintended privacy violations.Such a strong notion of privacy is differential privacy, a statistical notion of privacy that makes privacy leakage quantifiable. The caveat regarding differential privacy is that while it has strong guarantees for privacy, privacy comes at a cost of accuracy. Despite this trade-off being a central and important issue in the adoption of differential privacy, there exists a gap in the literature regarding providing an understanding of the trade-off and how to address it appropriately. Through a systematic literature review (SLR), we investigate the state-of-the-art within accuracy improving differentially private algorithms for histogram and synthetic data publishing. Our contribution is two-fold: 1) we identify trends and connections in the contributions to the field of differential privacy for histograms and synthetic data and 2) we provide an understanding of the privacy/accuracy trade-off challenge by crystallizing different dimensions to accuracy improvement. Accordingly, we position and visualize the ideas in relation to each other and external work, and deconstruct each algorithm to examine the building blocks separately with the aim of pinpointing which dimension of accuracy improvement each technique/approach is targeting. Hence, this systematization of knowledge (SoK) provides an understanding of in which dimensions and how accuracy improvement can be pursued without sacrificing privacy

3D 손 포즈 인식을 위한 인조 데이터의 이용

학위논문(박사) -- 서울대학교대학원 : 융합과학기술대학원 융합과학부(지능형융합시스템전공), 2021.8. 양한열.3D hand pose estimation (HPE) based on RGB images has been studied for a long time. Relevant methods have focused mainly on optimization of neural framework for graphically connected finger joints. Training RGB-based HPE models has not been easy to train because of the scarcity on RGB hand pose datasets; unlike human body pose datasets, the finger joints that span hand postures are structured delicately and exquisitely. Such structure makes accurately annotating each joint with unique 3D world coordinates difficult, which is why many conventional methods rely on synthetic data samples to cover large variations of hand postures. Synthetic dataset consists of very precise annotations of ground truths, and further allows control over the variety of data samples, yielding a learning model to be trained with a large pose space. Most of the studies, however, have performed frame-by-frame estimation based on independent static images. Synthetic visual data can provide practically infinite diversity and rich labels, while avoiding ethical issues with privacy and bias. However, for many tasks, current models trained on synthetic data generalize poorly to real data. The task of 3D human hand pose estimation is a particularly interesting example of this synthetic-to-real problem, because learning-based approaches perform reasonably well given real training data, yet labeled 3D poses are extremely difficult to obtain in the wild, limiting scalability. In this dissertation, we attempt to not only consider the appearance of a hand but incorporate the temporal movement information of a hand in motion into the learning framework for better 3D hand pose estimation performance, which leads to the necessity of a large scale dataset with sequential RGB hand images. We propose a novel method that generates a synthetic dataset that mimics natural human hand movements by re-engineering annotations of an extant static hand pose dataset into pose-flows. With the generated dataset, we train a newly proposed recurrent framework, exploiting visuo-temporal features from sequential images of synthetic hands in motion and emphasizing temporal smoothness of estimations with a temporal consistency constraint. Our novel training strategy of detaching the recurrent layer of the framework during domain finetuning from synthetic to real allows preservation of the visuo-temporal features learned from sequential synthetic hand images. Hand poses that are sequentially estimated consequently produce natural and smooth hand movements which lead to more robust estimations. We show that utilizing temporal information for 3D hand pose estimation significantly enhances general pose estimations by outperforming state-of-the-art methods in experiments on hand pose estimation benchmarks. Since a fixed set of dataset provides a finite distribution of data samples, the generalization of a learning pose estimation network is limited in terms of pose, RGB and viewpoint spaces. We further propose to augment the data automatically such that the augmented pose sampling is performed in favor of training pose estimators generalization performance. Such auto-augmentation of poses is performed within a learning feature space in order to avoid computational burden of generating synthetic sample for every iteration of updates. The proposed effort can be considered as generating and utilizing synthetic samples for network training in the feature space. This allows training efficiency by requiring less number of real data samples, enhanced generalization power over multiple dataset domains and estimation performance caused by efficient augmentation.2D 이미지에서 사람의 손 모양과 포즈를 인식하고 구현흐는 연구는 각 손가락 조인트들의 3D 위치를 검출하는 것을 목표로한다. 손 포즈는 손가락 조인트들로 구성되어 있고 손목 관절부터 MCP, PIP, DIP 조인트들로 사람 손을 구성하는 신체적 요소들을 의미한다. 손 포즈 정보는 다양한 분야에서 활용될수 있고 손 제스쳐 감지 연구 분야에서 손 포즈 정보가 매우 훌륭한 입력 특징 값으로 사용된다. 사람의 손 포즈 검출 연구를 실제 시스템에 적용하기 위해서는 높은 정확도, 실시간성, 다양한 기기에 사용 가능하도록 가벼운 모델이 필요하고, 이것을 가능케 하기 위해서 학습한 인공신경망 모델을 학습하는데에는 많은 데이터가 필요로 한다. 하지만 사람 손 포즈를 측정하는 기계들이 꽤 불안정하고, 이 기계들을 장착하고 있는 이미지는 사람 손 피부 색과는 많이 달라 학습에 사용하기가 적절하지 않다. 그러기 때문에 본 논문에서는 이러한 문제를 해결하기 위해 인공적으로 만들어낸 데이터를 재가공 및 증량하여 학습에 사용하고, 그것을 통해 더 좋은 학습성과를 이루려고 한다. 인공적으로 만들어낸 사람 손 이미지 데이터들은 실제 사람 손 피부색과는 비슷할지언정 디테일한 텍스쳐가 많이 달라, 실제로 인공 데이터를 학습한 모델은 실제 손 데이터에서 성능이 현저히 많이 떨어진다. 이 두 데이타의 도메인을 줄이기 위해서 첫번째로는 사람손의 구조를 먼저 학습 시키기위해, 손 모션을 재가공하여 그 움직임 구조를 학스한 시간적 정보를 뺀 나머지만 실제 손 이미지 데이터에 학습하였고 크게 효과를 내었다. 이때 실제 사람 손모션을 모방하는 방법론을 제시하였다. 두번째로는 두 도메인이 다른 데이터를 네트워크 피쳐 공간에서 align시켰다. 그뿐만아니라 인공 포즈를 특정 데이터들로 augment하지 않고 네트워크가 많이 보지 못한 포즈가 만들어지도록 하나의 확률 모델로서 설정하여 그것에서 샘플링하는 구조를 제안하였다. 본 논문에서는 인공 데이터를 더 효과적으로 사용하여 annotation이 어려운 실제 데이터를 더 모으는 수고스러움 없이 인공 데이터들을 더 효과적으로 만들어 내는 것 뿐만 아니라, 더 안전하고 지역적 특징과 시간적 특징을 활용해서 포즈의 성능을 개선하는 방법들을 제안했다. 또한, 네트워크가 스스로 필요한 데이터를 찾아서 학습할수 있는 자동 데이터 증량 방법론도 함께 제안하였다. 이렇게 제안된 방법을 결합해서 더 나은 손 포즈의 성능을 향상 할 수 있다.1. Introduction 1 2. Related Works 14 3. Preliminaries: 3D Hand Mesh Model 27 4. SeqHAND: RGB-sequence-based 3D Hand Pose and Shape Estimation 31 5. Hand Pose Auto-Augment 66 6. Conclusion 85 Abstract (Korea) 101 감사의 글 103박