Computer vision beyond the visible : image understanding through language

In the past decade, deep neural networks have revolutionized computer vision. High performing deep neural architectures trained for visual recognition tasks have pushed the field towards methods relying on learned image representations instead of hand-crafted ones, in the seek of designing end-to-end learning methods to solve challenging tasks, ranging from long-lasting ones such as image classification to newly emerging tasks like image captioning. As this thesis is framed in the context of the rapid evolution of computer vision, we present contributions that are aligned with three major changes in paradigm that the field has recently experienced, namely 1) the power of re-utilizing deep features from pre-trained neural networks for different tasks, 2) the advantage of formulating problems with end-to-end solutions given enough training data, and 3) the growing interest of describing visual data with natural language rather than pre-defined categorical label spaces, which can in turn enable visual understanding beyond scene recognition. The first part of the thesis is dedicated to the problem of visual instance search, where we particularly focus on obtaining meaningful and discriminative image representations which allow efficient and effective retrieval of similar images given a visual query. Contributions in this part of the thesis involve the construction of sparse Bag-of-Words image representations from convolutional features from a pre-trained image classification neural network, and an analysis of the advantages of fine-tuning a pre-trained object detection network using query images as training data. The second part of the thesis presents contributions to the problem of image-to-set prediction, understood as the task of predicting a variable-sized collection of unordered elements for an input image. We conduct a thorough analysis of current methods for multi-label image classification, which are able to solve the task in an end-to-end manner by simultaneously estimating both the label distribution and the set cardinality. Further, we extend the analysis of set prediction methods to semantic instance segmentation, and present an end-to-end recurrent model that is able to predict sets of objects (binary masks and categorical labels) in a sequential manner. Finally, the third part of the dissertation takes insights learned in the previous two parts in order to present deep learning solutions to connect images with natural language in the context of cooking recipes and food images. First, we propose a retrieval-based solution in which the written recipe and the image are encoded into compact representations that allow the retrieval of one given the other. Second, as an alternative to the retrieval approach, we propose a generative model to predict recipes directly from food images, which first predicts ingredients as sets and subsequently generates the rest of the recipe one word at a time by conditioning both on the image and the predicted ingredients.En l'última dècada, les xarxes neuronals profundes han revolucionat el camp de la visió per computador. Els resultats favorables obtinguts amb arquitectures neuronals profundes entrenades per resoldre tasques de reconeixement visual han causat un canvi de paradigma cap al disseny de mètodes basats en representacions d'imatges apreses de manera automàtica, deixant enrere les tècniques tradicionals basades en l'enginyeria de representacions. Aquest canvi ha permès l'aparició de tècniques basades en l'aprenentatge d'extrem a extrem (end-to-end), capaces de resoldre de manera efectiva molts dels problemes tradicionals de la visió per computador (e.g. classificació d'imatges o detecció d'objectes), així com nous problemes emergents com la descripció textual d'imatges (image captioning). Donat el context de la ràpida evolució de la visió per computador en el qual aquesta tesi s'emmarca, presentem contribucions alineades amb tres dels canvis més importants que la visió per computador ha experimentat recentment: 1) la reutilització de representacions extretes de models neuronals pre-entrenades per a tasques auxiliars, 2) els avantatges de formular els problemes amb solucions end-to-end entrenades amb grans bases de dades, i 3) el creixent interès en utilitzar llenguatge natural en lloc de conjunts d'etiquetes categòriques pre-definits per descriure el contingut visual de les imatges, facilitant així l'extracció d'informació visual més enllà del reconeixement de l'escena i els elements que la composen La primera part de la tesi està dedicada al problema de la cerca d'imatges (image retrieval), centrada especialment en l'obtenció de representacions visuals significatives i discriminatòries que permetin la recuperació eficient i efectiva d'imatges donada una consulta formulada amb una imatge d'exemple. Les contribucions en aquesta part de la tesi inclouen la construcció de representacions Bag-of-Words a partir de descriptors locals obtinguts d'una xarxa neuronal entrenada per classificació, així com un estudi dels avantatges d'utilitzar xarxes neuronals per a detecció d'objectes entrenades utilitzant les imatges d'exemple, amb l'objectiu de millorar les capacitats discriminatòries de les representacions obtingudes. La segona part de la tesi presenta contribucions al problema de predicció de conjunts a partir d'imatges (image to set prediction), entès com la tasca de predir una col·lecció no ordenada d'elements de longitud variable donada una imatge d'entrada. En aquest context, presentem una anàlisi exhaustiva dels mètodes actuals per a la classificació multi-etiqueta d'imatges, que són capaços de resoldre la tasca de manera integral calculant simultàniament la distribució probabilística sobre etiquetes i la cardinalitat del conjunt. Seguidament, estenem l'anàlisi dels mètodes de predicció de conjunts a la segmentació d'instàncies semàntiques, presentant un model recurrent capaç de predir conjunts d'objectes (representats per màscares binàries i etiquetes categòriques) de manera seqüencial. Finalment, la tercera part de la tesi estén els coneixements apresos en les dues parts anteriors per presentar solucions d'aprenentatge profund per connectar imatges amb llenguatge natural en el context de receptes de cuina i imatges de plats cuinats. En primer lloc, proposem una solució basada en algoritmes de cerca, on la recepta escrita i la imatge es codifiquen amb representacions compactes que permeten la recuperació d'una donada l'altra. En segon lloc, com a alternativa a la solució basada en algoritmes de cerca, proposem un model generatiu capaç de predir receptes (compostes pels seus ingredients, predits com a conjunts, i instruccions) directament a partir d'imatges de menjar.Postprint (published version

Cultural China 2021

Cultural China is a unique annual publication for up-to-date, informed and accessible commentary about Chinese and Sinophone languages, cultural practices, politics and production, and their critical analysis. It builds on the University of Westminster’s Contemporary China Centre Blog, providing additional reflective introductory pieces to contextualise each of the seven chapters. The articles in this Review speak to the challenging and eventful year that was 2021 as it unfolded across cultural China. Thematically, they range from health and medicine, environment, food, children and parenting, via film, red culture and calls for action. Many of the articles in this book focus on the People’s Republic of China, but they also draw attention to the multiple Chinese and Sinophone cultural practices that exist within, across, and beyond national borders. The Review is distinctive in its cultural studies-based approach and contributes a much-needed critical perspective from the Humanities to the study of cultural China. It aims to promote interdisciplinary dialogue and debate about the social, cultural, political, and historical dynamics that inform life in cultural China today, offering academics, activists, practitioners, and politicians a key reference with which to situate current events in and relating to cultural China in a wider context

Event structures in knowledge, pictures and text

This thesis proposes new techniques for mining scripts. Scripts are essential pieces of common sense knowledge that contain information about everyday scenarios (like going to a restaurant), namely the events that usually happen in a scenario (entering, sitting down, reading the menu...), their typical order (ordering happens before eating), and the participants of these events (customer, waiter, food...). Because many conventionalized scenarios are shared common sense knowledge and thus are usually not described in standard texts, we propose to elicit sequential descriptions of typical scenario instances via crowdsourcing over the internet. This approach overcomes the implicitness problem and, at the same time, is scalable to large data collections. To generalize over the input data, we need to mine event and participant paraphrases from the textual sequences. For this task we make use of the structural commonalities in the collected sequential descriptions, which yields much more accurate paraphrases than approaches that do not take structural constraints into account. We further apply the algorithm we developed for event paraphrasing to parallel standard texts for extracting sentential paraphrases and paraphrase fragments. In this case we consider the discourse structure in a text as a sequential event structure. As for event paraphrasing, the structure-aware paraphrasing approach clearly outperforms systems that do not consider discourse structure. As a multimodal application, we develop a new resource in which textual event descriptions are grounded in videos, which enables new investigations on action description semantics and a more accurate modeling of event description similarities. This grounding approach also opens up new possibilities for applying the computed script knowledge for automated event recognition in videos.Die vorliegende Dissertation schlägt neue Techniken zur Berechnung von Skripten vor. Skripte sind essentielle Teile des Allgemeinwissens, die Informationen über alltägliche Szenarien (wie im Restaurant essen) enthalten, nämlich die Ereignisse, die typischerweise in einem Szenario vorkommen (eintreten, sich setzen, die Karte lesen...), deren typische zeitliche Abfolge (man bestellt bevor man isst), und die Teilnehmer der Ereignisse (ein Gast, der Kellner, das Essen,...). Da viele konventionalisierte Szenarien implizit geteiltes Allgemeinwissen sind und üblicherweise nicht detailliert in Texten beschrieben werden, schlagen wir vor, Beschreibungen von typischen Szenario-Instanzen durch sog. “Crowdsourcing” über das Internet zu sammeln. Dieser Ansatz löst das Implizitheits-Problem und lässt sich gleichzeitig zu großen Daten-Sammlungen hochskalieren. Um über die Eingabe-Daten zu generalisieren, müssen wir in den Text-Sequenzen Paraphrasen für Ereignisse und Teilnehmer finden. Hierfür nutzen wir die strukturellen Gemeinsamkeiten dieser Sequenzen, was viel präzisere Paraphrasen-Information ergibt als Standard-Ansätze, die strukturelle Einschränkungen nicht beachten. Die Techniken, die wir für die Ereignis-Paraphrasierung entwickelt haben, wenden wir auch auf parallele Standard-Texte an, um Paraphrasen auf Satz-Ebene sowie Paraphrasen-Fragmente zu extrahieren. Hier betrachten wir die Diskurs-Struktur eines Textes als sequentielle Ereignis-Struktur. Auch hier liefert der strukturell informierte Ansatz klar bessere Ergebnisse als herkömmliche Systeme, die Diskurs-Struktur nicht in die Berechnung mit einbeziehen. Als multimodale Anwendung entwickeln wir eine neue Ressource, in der Text-Beschreibungen von Ereignissen mittels zeitlicher Synchronisierung in Videos verankert sind. Dies ermöglicht neue Ansätze für die Erforschung der Semantik von Ereignisbeschreibungen, und erlaubt außerdem die Modellierung treffenderer Ereignis-Ähnlichkeiten. Dieser Schritt der visuellen Verankerung von Text in Videos eröffnet auch neue Möglichkeiten für die Anwendung des berechneten Skript-Wissen bei der automatischen Ereigniserkennung in Videos

Eating and repeating : mimesis in food rhetorics

textThere is emerging, in the discipline of rhetoric and composition, a rhetoric of food. In this dissertation, I map the various approaches to food rhetorics, and I look at three different foods: burritos, kale, and kombucha. Using these foods as commonplaces, I explore the social and rhetorical discourse around them. I use “a cultural biography of things” methodology to describe the history of the burrito and use that history to contextualize Chipotle Mexican Grill’s new media strategies. Throughout the cultural biography of the burrito and the analysis of Chipotle’s marketing, I highlight a theatrical mimesis that blurs the lines between imitation and reality. I suggest that kale can be associated the books of Michael Pollan, whose work, I argue, constitutes a genre that establishes a set of conventions for how we think and communicate about food. I begin by looking at how Chipotle builds its corporate ethos by citing Michael Pollan’s books on its website. Then I approach Pollan’s body of work as a genre, showing how it establishes certain conventions in food discourse. We see transmissions of these conventions throughout food networks. I look at how fermented foods, like kombucha, travel through alternative food networks, like groups of “fermentos” led by Sandor Katz, until they have proliferated to the point of becoming mainstream. I show how Michael Pollan engages with the world of countercultural food movements like fermentos and argue that Pollan’s engagement with fermentos signals a move into posthuman rhetorics. Building on the idea of micropolitics, I posit a compostmodern micro(be)politics that re-articulates the human not as an agentive individual governed by autonomy, but as an ecology itself, situated within other ecologies. I conclude by reading “nobody cares what you ate for lunch” memes as a response to and provocation to an abundance of online food talk. We can read these memes as evidence of the significance of online food discourse. Instead of taking the memes at face value, we can ask, “who does care about food in online networks?”Englis

Temporal Segmentation of Human Actions in Videos

Understanding human actions in videos is of great interest in various scenarios ranging from surveillance over quality control in production processes to content-based video search. Algorithms for automatic temporal action segmentation need to overcome severe difficulties in order to be reliable and provide sufficiently good quality. Not only can human actions occur in different scenes and surroundings, the definition on an action itself is also inherently fuzzy, leading to a significant amount of inter-class variations. Moreover, besides finding the correct action label for a pre-defined temporal segment in a video, localizing an action in the first place is anything but trivial. Different actions not only vary in their appearance and duration but also can have long-range temporal dependencies that span over the complete video. Further, getting reliable annotations of large amounts of video data is time consuming and expensive. The goal of this thesis is to advance current approaches to temporal action segmentation. We therefore propose a generic framework that models the three components of the task explicitly, ie long-range temporal dependencies are handled by a context model, variations in segment durations are represented by a length model, and short-term appearance and motion of actions are addressed with a visual model. While the inspiration for the context model mainly comes from word sequence models in natural language processing, the visual model builds upon recent advances in the classification of pre-segmented action clips. Considering that long-range temporal context is crucial, we avoid local segmentation decisions and find the globally optimal temporal segmentation of a video under the explicit models. Throughout the thesis, we provide explicit formulations and training strategies for the proposed generic action segmentation framework under different supervision conditions. First, we address the task of fully supervised temporal action segmentation, where frame-level annotations are available during training. We show that our approach can outperform early sliding window baselines and recent deep architectures and that explicit length and context modeling leads to substantial improvements. Considering that full frame-level annotation is expensive to obtain, we then formulate a weakly supervised training algorithm that uses ordered sequences of actions occurring in the video as only supervision. While a first approach reduces the weakly supervised setup to a fully supervised setup by generating a pseudo ground-truth during training, we propose a second approach that avoids this intermediate step and allows to directly optimize a loss based on the weak supervision. Closing the gap between the fully and the weakly supervised setup, we moreover evaluate semi-supervised learning, where video frames are sparsely annotated. With the motivation that the vast amount of video data on the Internet only comes with meta-tags or content keywords that do not provide any temporal ordering information, we finally propose a method for action segmentation that learns from unordered sets of actions only. All approaches are evaluated on several commonly used benchmark datasets. With the proposed methods, we reach state-of-the-art performance for both, fully and weakly supervised action segmentation

Cultural China 2021

Cultural China is a unique annual publication for up-to-date, informed and accessible commentary about Chinese and Sinophone languages, cultural practices, politics and production, and their critical analysis. It builds on the University of Westminster’s Contemporary China Centre Blog, providing additional reflective introductory pieces to contextualise each of the seven chapters. The articles in this Review speak to the challenging and eventful year that was 2021 as it unfolded across cultural China. Thematically, they range from health and medicine, environment, food, children and parenting, via film, red culture and calls for action. Many of the articles in this book focus on the People’s Republic of China, but they also draw attention to the multiple Chinese and Sinophone cultural practices that exist within, across, and beyond national borders. The Review is distinctive in its cultural studies-based approach and contributes a much-needed critical perspective from the Humanities to the study of cultural China. It aims to promote interdisciplinary dialogue and debate about the social, cultural, political, and historical dynamics that inform life in cultural China today, offering academics, activists, practitioners, and politicians a key reference with which to situate current events in and relating to cultural China in a wider context

Neural models for stepwise text illustration

In this thesis, we investigate the task of sequence-to-sequence (seq2seq) retrieval: given a sequence (of text passages) as the query, retrieve a sequence (of images) that best describes and aligns with the query. This is a step beyond the traditional cross-modal retrieval which treats each image-text pair independently and ignores broader context. Since this is a difficult task, we break it into steps. We start with caption generation for images in news articles. Different from traditional image captioning task where a text description is generated given an image, here, a caption is generated conditional on both image and the news articles where it appears. We propose a novel neural-networks based methodology to take into account both news article content and image semantics to generate a caption best describing the image and its surrounding text context. Our results outperform existing approaches to image captioning generation. We then introduce two new novel datasets, GutenStories and Stepwise Recipe datasets for the task of story picturing and sequential text illustration. GutenStories consists of around 90k text paragraphs, each accompanied with an image, aligned in around 18k visual stories. It consists of a wide variety of images and story content styles. StepwiseRecipe is a similar dataset having sequenced image-text pairs, but having only domain-constrained images, namely food-related. It consists of 67k text paragraphs (cooking instructions), each accompanied by an image describing the step, aligned in 10k recipes. Both datasets are web-scrawled and systematically filtered and cleaned. We propose a novel variational recurrent seq2seq (VRSS) retrieval model. xii The model encodes two streams of information at every step: the contextual information from both text and images retrieved in previous steps, and the semantic meaning of the current input (text) as a latent vector. These together guide the retrieval of a relevant image from the repository to match the semantics of the given text. The model has been evaluated on both the Stepwise Recipe and GutenStories datasets. The results on several automatic evaluation measures show that our model outperforms several competitive and relevant baselines. We also qualitatively analyse the model both using human evaluation and by visualizing the representation space to judge the semantical meaningfulness. We further discuss the challenges faced on the more difficult GutenStories and outline possible solutions

Workload-aware systems and interfaces for cognitive augmentation

In today's society, our cognition is constantly influenced by information intake, attention switching, and task interruptions. This increases the difficulty of a given task, adding to the existing workload and leading to compromised cognitive performances. The human body expresses the use of cognitive resources through physiological responses when confronted with a plethora of cognitive workload. This temporarily mobilizes additional resources to deal with the workload at the cost of accelerated mental exhaustion. We predict that recent developments in physiological sensing will increasingly create user interfaces that are aware of the user’s cognitive capacities, hence able to intervene when high or low states of cognitive workload are detected. In this thesis, we initially focus on determining opportune moments for cognitive assistance. Subsequently, we investigate suitable feedback modalities in a user-centric design process which are desirable for cognitive assistance. We present design requirements for how cognitive augmentation can be achieved using interfaces that sense cognitive workload. We then investigate different physiological sensing modalities to enable suitable real-time assessments of cognitive workload. We provide empirical evidence that the human brain is sensitive to fluctuations in cognitive resting states, hence making cognitive effort measurable. Firstly, we show that electroencephalography is a reliable modality to assess the mental workload generated during the user interface operation. Secondly, we use eye tracking to evaluate changes in eye movements and pupil dilation to quantify different workload states. The combination of machine learning and physiological sensing resulted in suitable real-time assessments of cognitive workload. The use of physiological sensing enables us to derive when cognitive augmentation is suitable. Based on our inquiries, we present applications that regulate cognitive workload in home and work settings. We deployed an assistive system in a field study to investigate the validity of our derived design requirements. Finding that workload is mitigated, we investigated how cognitive workload can be visualized to the user. We present an implementation of a biofeedback visualization that helps to improve the understanding of brain activity. A final study shows how cognitive workload measurements can be used to predict the efficiency of information intake through reading interfaces. Here, we conclude with use cases and applications which benefit from cognitive augmentation. This thesis investigates how assistive systems can be designed to implicitly sense and utilize cognitive workload for input and output. To do so, we measure cognitive workload in real-time by collecting behavioral and physiological data from users and analyze this data to support users through assistive systems that adapt their interface according to the currently measured workload. Our overall goal is to extend new and existing context-aware applications by the factor cognitive workload. We envision Workload-Aware Systems and Workload-Aware Interfaces as an extension in the context-aware paradigm. To this end, we conducted eight research inquiries during this thesis to investigate how to design and create workload-aware systems. Finally, we present our vision of future workload-aware systems and workload-aware interfaces. Due to the scarce availability of open physiological data sets, reference implementations, and methods, previous context-aware systems were limited in their ability to utilize cognitive workload for user interaction. Together with the collected data sets, we expect this thesis to pave the way for methodical and technical tools that integrate workload-awareness as a factor for context-aware systems.Tagtäglich werden unsere kognitiven Fähigkeiten durch die Verarbeitung von unzähligen Informationen in Anspruch genommen. Dies kann die Schwierigkeit einer Aufgabe durch mehr oder weniger Arbeitslast beeinflussen. Der menschliche Körper drückt die Nutzung kognitiver Ressourcen durch physiologische Reaktionen aus, wenn dieser mit kognitiver Arbeitsbelastung konfrontiert oder überfordert wird. Dadurch werden weitere Ressourcen mobilisiert, um die Arbeitsbelastung vorübergehend zu bewältigen. Wir prognostizieren, dass die derzeitige Entwicklung physiologischer Messverfahren kognitive Leistungsmessungen stets möglich machen wird, um die kognitive Arbeitslast des Nutzers jederzeit zu messen. Diese sind in der Lage, einzugreifen wenn eine zu hohe oder zu niedrige kognitive Belastung erkannt wird. Wir konzentrieren uns zunächst auf die Erkennung passender Momente für kognitive Unterstützung welche sich der gegenwärtigen kognitiven Arbeitslast bewusst sind. Anschließend untersuchen wir in einem nutzerzentrierten Designprozess geeignete Feedbackmechanismen, die zur kognitiven Assistenz beitragen. Wir präsentieren Designanforderungen, welche zeigen wie Schnittstellen eine kognitive Augmentierung durch die Messung kognitiver Arbeitslast erreichen können. Anschließend untersuchen wir verschiedene physiologische Messmodalitäten, welche Bewertungen der kognitiven Arbeitsbelastung in Realzeit ermöglichen. Zunächst validieren wir empirisch, dass das menschliche Gehirn auf kognitive Arbeitslast reagiert. Es zeigt sich, dass die Ableitung der kognitiven Arbeitsbelastung über Elektroenzephalographie eine geeignete Methode ist, um den kognitiven Anspruch neuartiger Assistenzsysteme zu evaluieren. Anschließend verwenden wir Eye-Tracking, um Veränderungen in den Augenbewegungen und dem Durchmesser der Pupille unter verschiedenen Intensitäten kognitiver Arbeitslast zu bewerten. Das Anwenden von maschinellem Lernen führt zu zuverlässigen Echtzeit-Bewertungen kognitiver Arbeitsbelastung. Auf der Grundlage der bisherigen Forschungsarbeiten stellen wir Anwendungen vor, welche die Kognition im häuslichen und beruflichen Umfeld unterstützen. Die physiologischen Messungen stellen fest, wann eine kognitive Augmentierung sich als günstig erweist. In einer Feldstudie setzen wir ein Assistenzsystem ein, um die erhobenen Designanforderungen zur Reduktion kognitiver Arbeitslast zu validieren. Unsere Ergebnisse zeigen, dass die Arbeitsbelastung durch den Einsatz von Assistenzsystemen reduziert wird. Im Anschluss untersuchen wir, wie kognitive Arbeitsbelastung visualisiert werden kann. Wir stellen eine Implementierung einer Biofeedback-Visualisierung vor, die das Nutzerverständnis zum Verlauf und zur Entstehung von kognitiver Arbeitslast unterstützt. Eine abschließende Studie zeigt, wie Messungen kognitiver Arbeitslast zur Vorhersage der aktuellen Leseeffizienz benutzt werden können. Wir schließen hierbei mit einer Reihe von Applikationen ab, welche sich kognitive Arbeitslast als Eingabe zunutze machen. Die vorliegende wissenschaftliche Arbeit befasst sich mit dem Design von Assistenzsystemen, welche die kognitive Arbeitslast der Nutzer implizit erfasst und diese bei der Durchführung alltäglicher Aufgaben unterstützt. Dabei werden physiologische Daten erfasst, um Rückschlüsse in Realzeit auf die derzeitige kognitive Arbeitsbelastung zu erlauben. Anschließend werden diese Daten analysiert, um dem Nutzer strategisch zu assistieren. Das Ziel dieser Arbeit ist die Erweiterung neuartiger und bestehender kontextbewusster Benutzerschnittstellen um den Faktor kognitive Arbeitslast. Daher werden in dieser Arbeit arbeitslastbewusste Systeme und arbeitslastbewusste Benutzerschnittstellen als eine zusätzliche Dimension innerhalb des Paradigmas kontextbewusster Systeme präsentiert. Wir stellen acht Forschungsstudien vor, um die Designanforderungen und die Implementierung von kognitiv arbeitslastbewussten Systemen zu untersuchen. Schließlich stellen wir unsere Vision von zukünftigen kognitiven arbeitslastbewussten Systemen und Benutzerschnittstellen vor. Durch die knappe Verfügbarkeit öffentlich zugänglicher Datensätze, Referenzimplementierungen, und Methoden, waren Kontextbewusste Systeme in der Auswertung kognitiver Arbeitslast bezüglich der Nutzerinteraktion limitiert. Ergänzt durch die in dieser Arbeit gesammelten Datensätze erwarten wir, dass diese Arbeit den Weg für methodische und technische Werkzeuge ebnet, welche kognitive Arbeitslast als Faktor in das Kontextbewusstsein von Computersystemen integriert

Digitally enhanced consumer packaged goods: a data-inspired ideation approach

Consumer packaged goods (CPG) are disposable, relatively low-price, frequently-purchased products such as a bottle of milk or a bar of chocolate. CPGs have a pervasive presence in our everyday practices, and a number of instances have shown the potential of integrating their existing functionalities into the Internet of Things (IoT). Such innovations as, for example, a pill container which reminds one when to take their medication, or a disposable toothbrush which teaches children about oral hygiene, illustrate the capacity of digitally enhanced CPGs to have a positive impact in countless aspects of our lives. However, despite recent research in human-computer interaction (HCI) aimed specifically at enhancing interactions with CPGs, devising enhanced versions of these goods which meet people's needs and reflect their values remains quite elusive. Many challenges in the design of enhanced CPGs stem from their defining characteristics, including their disposability and frequent need to be replenished, as well as from the fact that they are rarely used in isolation, but rather in conjunction with one another as sets. While it has been demonstrated that providing data about item usage during the design process represents a substantially powerful approach for creating effective products, this has not yet been applied in the creation of enhanced CPGs, as we currently lack even a rudimentary understanding of their use. This thesis represents the body of knowledge gathered through the completion of two fieldwork studies focused on how CPGs are used in the practice of cooking. Furthermore, it utilises an understanding of CPG interactions and, through two participatory design workshops, explores how such insights can inspire the conceptualisation of enhanced CPGs. The fieldwork study of this thesis focused on the interactions of CPGs in cooking, which was chosen due to it being one of the most prevalent everyday practices involving CPGs. We examined cooking in two situational contexts: the preparation of familiar meals (those which could be prepared from memory) and that of unfamiliar meals (those which people had never cooked before). The first analysis was concerned with only the preparation of the unfamiliar meals, while in the second analysis we conducted a comparative analysis between familiar and unfamiliar meals. We employed a mixed-methods approach for blending quantitative and qualitative analysis methods. Overall, these studies revealed different characteristics of CPG interactions, including aspects of information-gathering, frequency of task saturation, and the sets of CPGs and utensils which appear together often. One example of our findings was that meal preparation was generally similar regardless of familiarity, as revealed by the repeated use of a select few CPGs across many meals and the consistency of their number of interactions. We then discussed the implications these findings have for the design of digitally-enhanced CPGs with the overall goal of promoting enhancements which fit our routines and habits rather than require us to adapt our practices to the IoT. Inspired by frameworks which have placed data at the centre of the design process, the participatory designs employed in this thesis, made use of the data gathered from the above mentioned fieldwork studies as a tool for participants to inspire the design of enhanced CPGs. We devised a structured workshop to study how participants drew upon the data, as well as how they perceive the influence this approach had on their ideation process. To facilitate their use of the data, we devised an array of design resources including data visualisations and design cards. We explored our approach in two studies: one which consisted of participants from the general public, and the other which consisted of professional designers. Analysing the role of data as expressed through participants’ comments and designs, we found that data served as a basis for the creation of unique concepts imbued with a sense of empathy and a greater consideration for the experiences and interests of others. Furthermore, we found that participants considered possible negative ramifications of the use of data for design, including ethical and privacy issues which may stem from such data collection, as well as a potential bias towards focusing on aspects highlighted by the data. This thesis makes a number of contributions in showing that a detailed understanding of CPG interactions in practice can lead to insights which inspire the design of technologically-enhanced CPGs. It also presents analysis methods to further study the use of CPGs in practice, as well as an approach which enables people with no relevant formal training to utilise data effectively. In addition, this work provides implications for designing enhanced versions of CPGs which fit their practical contexts of use. For an accurate view of this research and its contributions, its limitations must be acknowledged, such as the relatively small size of our data sample and our bias towards the use of technologies to provide product enhancements. Nevertheless, our work highlights the need for an understanding of the practical use of objects to better design technological innovations which fit well into their real-world interactions, and serves to emphasise the need to continue research on CPG innovations. This work represents merely the first steps towards CPGs which are designed using a solid foundation of an empirical working knowledge of the practices in which CPGs play a role