Learning from Very Few Samples: A Survey

Few sample learning (FSL) is significant and challenging in the field of machine learning. The capability of learning and generalizing from very few samples successfully is a noticeable demarcation separating artificial intelligence and human intelligence since humans can readily establish their cognition to novelty from just a single or a handful of examples whereas machine learning algorithms typically entail hundreds or thousands of supervised samples to guarantee generalization ability. Despite the long history dated back to the early 2000s and the widespread attention in recent years with booming deep learning technologies, little surveys or reviews for FSL are available until now. In this context, we extensively review 300+ papers of FSL spanning from the 2000s to 2019 and provide a timely and comprehensive survey for FSL. In this survey, we review the evolution history as well as the current progress on FSL, categorize FSL approaches into the generative model based and discriminative model based kinds in principle, and emphasize particularly on the meta learning based FSL approaches. We also summarize several recently emerging extensional topics of FSL and review the latest advances on these topics. Furthermore, we highlight the important FSL applications covering many research hotspots in computer vision, natural language processing, audio and speech, reinforcement learning and robotic, data analysis, etc. Finally, we conclude the survey with a discussion on promising trends in the hope of providing guidance and insights to follow-up researches.Comment: 30 page

CP3: Unifying Point Cloud Completion by Pretrain-Prompt-Predict Paradigm

Point cloud completion aims to predict complete shape from its partial observation. Current approaches mainly consist of generation and refinement stages in a coarse-to-fine style. However, the generation stage often lacks robustness to tackle different incomplete variations, while the refinement stage blindly recovers point clouds without the semantic awareness. To tackle these challenges, we unify point cloud Completion by a generic Pretrain-Prompt-Predict paradigm, namely CP3. Inspired by prompting approaches from NLP, we creatively reinterpret point cloud generation and refinement as the prompting and predicting stages, respectively. Then, we introduce a concise self-supervised pretraining stage before prompting. It can effectively increase robustness of point cloud generation, by an Incompletion-Of-Incompletion (IOI) pretext task. Moreover, we develop a novel Semantic Conditional Refinement (SCR) network at the predicting stage. It can discriminatively modulate multi-scale refinement with the guidance of semantics. Finally, extensive experiments demonstrate that our CP3 outperforms the state-of-the-art methods with a large margin

Multimodal representation and learning

Recent years have seen an explosion in multimodal data on the web. It is therefore important to perform multimodal learning to understand the web. However, it is challenging to join various modalities because each modality has a different representation and correlational structure. In addition, various modalities generally carry different kinds of information that may provide enrich understanding; for example, the visual signal of a flower may provide happiness; however, its scent might not be pleasant. Multimodal information may be useful to make an informed decision. Therefore, we focus on improving representations from individual modalities to enhance multimodal representation and learning. In this doctoral thesis, we presented techniques to enhance representations from individual and multiple modalities for multimodal applications including classification, cross-modal retrieval, matching and verification on various benchmark datasets

Deep Representation Learning with Limited Data for Biomedical Image Synthesis, Segmentation, and Detection

Biomedical imaging requires accurate expert annotation and interpretation that can aid medical staff and clinicians in automating differential diagnosis and solving underlying health conditions. With the advent of Deep learning, it has become a standard for reaching expert-level performance in non-invasive biomedical imaging tasks by training with large image datasets. However, with the need for large publicly available datasets, training a deep learning model to learn intrinsic representations becomes harder. Representation learning with limited data has introduced new learning techniques, such as Generative Adversarial Networks, Semi-supervised Learning, and Self-supervised Learning, that can be applied to various biomedical applications. For example, ophthalmologists use color funduscopy (CF) and fluorescein angiography (FA) to diagnose retinal degenerative diseases. However, fluorescein angiography requires injecting a dye, which can create adverse reactions in the patients. So, to alleviate this, a non-invasive technique needs to be developed that can translate fluorescein angiography from fundus images. Similarly, color funduscopy and optical coherence tomography (OCT) are also utilized to semantically segment the vasculature and fluid build-up in spatial and volumetric retinal imaging, which can help with the future prognosis of diseases. Although many automated techniques have been proposed for medical image segmentation, the main drawback is the model's precision in pixel-wise predictions. Another critical challenge in the biomedical imaging field is accurately segmenting and quantifying dynamic behaviors of calcium signals in cells. Calcium imaging is a widely utilized approach to studying subcellular calcium activity and cell function; however, large datasets have yielded a profound need for fast, accurate, and standardized analyses of calcium signals. For example, image sequences from calcium signals in colonic pacemaker cells ICC (Interstitial cells of Cajal) suffer from motion artifacts and high periodic and sensor noise, making it difficult to accurately segment and quantify calcium signal events. Moreover, it is time-consuming and tedious to annotate such a large volume of calcium image stacks or videos and extract their associated spatiotemporal maps. To address these problems, we propose various deep representation learning architectures that utilize limited labels and annotations to address the critical challenges in these biomedical applications. To this end, we detail our proposed semi-supervised, generative adversarial networks and transformer-based architectures for individual learning tasks such as retinal image-to-image translation, vessel and fluid segmentation from fundus and OCT images, breast micro-mass segmentation, and sub-cellular calcium events tracking from videos and spatiotemporal map quantification. We also illustrate two multi-modal multi-task learning frameworks with applications that can be extended to other domains of biomedical applications. The main idea is to incorporate each of these as individual modules to our proposed multi-modal frameworks to solve the existing challenges with 1) Fluorescein angiography synthesis, 2) Retinal vessel and fluid segmentation, 3) Breast micro-mass segmentation, and 4) Dynamic quantification of calcium imaging datasets

Multimodal representation and learning

Recent years have seen an explosion in multimodal data on the web. It is therefore important to perform multimodal learning to understand the web. However, it is challenging to join various modalities because each modality has a different representation and correlational structure. In addition, various modalities generally carry different kinds of information that may provide enrich understanding; for example, the visual signal of a flower may provide happiness; however, its scent might not be pleasant. Multimodal information may be useful to make an informed decision. Therefore, we focus on improving representations from individual modalities to enhance multimodal representation and learning. In this doctoral thesis, we presented techniques to enhance representations from individual and multiple modalities for multimodal applications including classification, cross-modal retrieval, matching and verification on various benchmark datasets

A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

Dsfer-Net: A Deep Supervision and Feature Retrieval Network for Bitemporal Change Detection Using Modern Hopfield Networks

Change detection, as an important application for high-resolution remote sensing images, aims to monitor and analyze changes in the land surface over time. With the rapid growth in the quantity of high-resolution remote sensing data and the complexity of texture features, a number of quantitative deep learning-based methods have been proposed. Although these methods outperform traditional change detection methods by extracting deep features and combining spatial-temporal information, reasonable explanations about how deep features work on improving the detection performance are still lacking. In our investigations, we find that modern Hopfield network layers achieve considerable performance in semantic understandings. In this paper, we propose a Deep Supervision and FEature Retrieval network (Dsfer-Net) for bitemporal change detection. Specifically, the highly representative deep features of bitemporal images are jointly extracted through a fully convolutional Siamese network. Based on the sequential geo-information of the bitemporal images, we then design a feature retrieval module to retrieve the difference feature and leverage discriminative information in a deeply supervised manner. We also note that the deeply supervised feature retrieval module gives explainable proofs about the semantic understandings of the proposed network in its deep layers. Finally, this end-to-end network achieves a novel framework by aggregating the retrieved features and feature pairs from different layers. Experiments conducted on three public datasets (LEVIR-CD, WHU-CD, and CDD) confirm the superiority of the proposed Dsfer-Net over other state-of-the-art methods. Code will be available online (https://github.com/ShizhenChang/Dsfer-Net)

Towards accurate multi-person pose estimation in the wild

In this thesis we are concerned with the problem of articulated human pose estimation and pose tracking in images and video sequences. Human pose estimation is a task of localising major joints of a human skeleton in natural images and is one of the most important visual recognition tasks in the scenes containing humans with numerous applications in robotics, virtual and augmented reality, gaming and healthcare among others. Articulated human pose tracking requires tracking multiple persons in the video sequence while simultaneously estimating full body poses. This task is important for analysing surveillance footage, activity recognition, sports analytics, etc. Most of the prior work focused on the pose estimation of single pre-localised humans whereas here we address a case with multiple people in real world images which entails several challenges such as person-person overlaps in highly crowded scenes, unknown number of people or people entering and leaving video sequences. The first contribution is a multi-person pose estimation algorithm based on the bottom-up detection-by-grouping paradigm. Unlike the widespread top-down approaches our method detects body joints and pairwise relations between them in a single forward pass of a convolutional neural network. Multi-person parsing is performed by optimizing a joint objective based on a multicut graph partitioning framework. Secondly, we extend our pose estimation approach to articulated multi-person pose tracking in videos. Our approach performs multi-target tracking and pose estimation in a holistic manner by optimising a single objective. We further simplify and refine the formulation which allows us to reach close to the real-time performance. Thirdly, we propose a large scale dataset and a benchmark for articulated multi-person tracking. It is the first dataset of video sequences comprising complex multi-person scenes and fully annotated tracks with 2D keypoints. Our fourth contribution is a method for estimating 3D body pose using on-body wearable cameras. Our approach uses a pair of downward facing, head-mounted cameras and captures an entire body. This egocentric approach is free of limitations of traditional setups with external cameras and can estimate body poses in very crowded environments. Our final contribution goes beyond human pose estimation and is in the field of deep learning of 3D object shapes. In particular, we address the case of reconstructing 3D objects from weak supervision. Our approach represents objects as 3D point clouds and is able to learn them with 2D supervision only and without requiring camera pose information at training time. We design a differentiable renderer of point clouds as well as a novel loss formulation for dealing with camera pose ambiguity.In dieser Arbeit behandeln wir das Problem der Schätzung und Verfolgung artikulierter menschlicher Posen in Bildern und Video-Sequenzen. Die Schätzung menschlicher Posen besteht darin die Hauptgelenke des menschlichen Skeletts in natürlichen Bildern zu lokalisieren und ist eine der wichtigsten Aufgaben der visuellen Erkennung in Szenen, die Menschen beinhalten. Sie hat zahlreiche Anwendungen in der Robotik, virtueller und erweiterter Realität, in Videospielen, in der Medizin und weiteren Bereichen. Die Verfolgung artikulierter menschlicher Posen erfordert die Verfolgung mehrerer Personen in einer Videosequenz bei gleichzeitiger Schätzung vollständiger Körperhaltungen. Diese Aufgabe ist besonders wichtig für die Analyse von Video-Überwachungsaufnahmen, Aktivitätenerkennung, digitale Sportanalyse etc. Die meisten vorherigen Arbeiten sind auf die Schätzung einzelner Posen vorlokalisierter Menschen fokussiert, wohingegen wir den Fall mehrerer Personen in natürlichen Aufnahmen betrachten. Dies bringt einige Herausforderungen mit sich, wie die Überlappung verschiedener Personen in dicht gedrängten Szenen, eine unbekannte Anzahl an Personen oder Personen die das Sichtfeld der Video-Sequenz verlassen oder betreten. Der erste Beitrag ist ein Algorithmus zur Schätzung der Posen mehrerer Personen, welcher auf dem Paradigma der Erkennung durch Gruppierung aufbaut. Im Gegensatz zu den verbreiteten Verfeinerungs-Ansätzen erkennt unsere Methode Körpergelenke and paarweise Beziehungen zwischen ihnen in einer einzelnen Vorwärtsrechnung eines faltenden neuronalen Netzwerkes. Die Gliederung in mehrere Personen erfolgt durch Optimierung einer gemeinsamen Zielfunktion, die auf dem Mehrfachschnitt-Problem in der Graphenzerlegung basiert. Zweitens erweitern wir unseren Ansatz zur Posen-Bestimmung auf das Verfolgen mehrerer Personen und deren Artikulation in Videos. Unser Ansatz führt eine Verfolgung mehrerer Ziele und die Schätzung der zugehörigen Posen in ganzheitlicher Weise durch, indem eine einzelne Zielfunktion optimiert wird. Desweiteren vereinfachen und verfeinern wir die Formulierung, was unsere Methode nah an Echtzeit-Leistung bringt. Drittens schlagen wir einen großen Datensatz und einen Bewertungsmaßstab für die Verfolgung mehrerer artikulierter Personen vor. Dies ist der erste Datensatz der Video-Sequenzen von komplexen Szenen mit mehreren Personen beinhaltet und deren Spuren komplett mit zwei-dimensionalen Markierungen der Schlüsselpunkte versehen sind. Unser vierter Beitrag ist eine Methode zur Schätzung von drei-dimensionalen Körperhaltungen mittels am Körper tragbarer Kameras. Unser Ansatz verwendet ein Paar nach unten gerichteter, am Kopf befestigter Kameras und erfasst den gesamten Körper. Dieser egozentrische Ansatz ist frei von jeglichen Limitierungen traditioneller Konfigurationen mit externen Kameras und kann Körperhaltungen in sehr dicht gedrängten Umgebungen bestimmen. Unser letzter Beitrag geht über die Schätzung menschlicher Posen hinaus in den Bereich des tiefen Lernens der Gestalt von drei-dimensionalen Objekten. Insbesondere befassen wir uns mit dem Fall drei-dimensionale Objekte unter schwacher Überwachung zu rekonstruieren. Unser Ansatz repräsentiert Objekte als drei-dimensionale Punktwolken and ist im Stande diese nur mittels zwei-dimensionaler Überwachung und ohne Informationen über die Kamera-Ausrichtung zur Trainingszeit zu lernen. Wir entwerfen einen differenzierbaren Renderer für Punktwolken sowie eine neue Formulierung um mit uneindeutigen Kamera-Ausrichtungen umzugehen

Exploiting multimodality and structure in world representations

An essential aim of artificial intelligence research is to design agents that will eventually cooperate with humans within the real world. To this end, embodied learning is emerging as one of the most important efforts contributed by the machine learning community towards this goal. Recently developing sub-fields concern various aspects of such systems---visual reasoning, language representations, causal mechanisms, robustness to out-of-distribution inputs, to name only a few. In particular, multimodal learning and language grounding are vital to achieving a strong understanding of the real world. Humans build internal representations via interacting with their environment, learning complex associations between visual, auditory and linguistic concepts. Since the world abounds with structure, graph-based encodings are also likely to be incorporated in reasoning and decision-making modules. Furthermore, these relational representations are rather symbolic in nature---providing advantages over other formats, such as raw pixels---and can encode various types of links (temporal, causal, spatial) which can be essential for understanding and acting in the real world. This thesis presents three research works that study and develop likely aspects of future intelligent agents. The first contribution centers on vision-and-language learning, introducing a challenging embodied task that shifts the focus of an existing one to the visual reasoning problem. By extending popular visual question answering (VQA) paradigms, I also designed several models that were evaluated on the novel dataset. This produced initial performance estimates for environment understanding, through the lens of a more challenging VQA downstream task. The second work presents two ways of obtaining hierarchical representations of graph-structured data. These methods either scaled to much larger graphs than the ones processed by the best-performing method at the time, or incorporated theoretical properties via the use of topological data analysis algorithms. Both approaches competed with contemporary state-of-the-art graph classification methods, even outside social domains in the second case, where the inductive bias was PageRank-driven. Finally, the third contribution delves further into relational learning, presenting a probabilistic treatment of graph representations in complex settings such as few-shot, multi-task learning and scarce-labelled data regimes. By adding relational inductive biases to neural processes, the resulting framework can model an entire distribution of functions which generate datasets with structure. This yielded significant performance gains, especially in the aforementioned complex scenarios, with semantically-accurate uncertainty estimates that drastically improved over the neural process baseline. This type of framework may eventually contribute to developing lifelong-learning systems, due to its ability to adapt to novel tasks and distributions. The benchmark, methods and frameworks that I have devised during my doctoral studies suggest important future directions for embodied and graph representation learning research. These areas have increasingly proved their relevance to designing intelligent and collaborative agents, which we may interact with in the near future. By addressing several challenges in this problem space, my contributions therefore take a few steps towards building machine learning systems to be deployed in real-life settings.DREAM CD