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

Enhancing scene text recognition with visual context information

This thesis addresses the problem of improving text spotting systems, which aim to detect and recognize text in unrestricted images (e.g. a street sign, an advertisement, a bus destination, etc.). The goal is to improve the performance of off-the-shelf vision systems by exploiting the semantic information derived from the image itself. The rationale is that knowing the content of the image or the visual context can help to decide which words are the correct andidate words. For example, the fact that an image shows a coffee shop makes it more likely that a word on a signboard reads as Dunkin and not unkind. We address this problem by drawing on successful developments in natural language processing and machine learning, in particular, learning to re-rank and neural networks, to present post-process frameworks that improve state-of-the-art text spotting systems without the need for costly data-driven re-training or tuning procedures. Discovering the degree of semantic relatedness of candidate words and their image context is a task related to assessing the semantic similarity between words or text fragments. However, semantic relatedness is more general than similarity (e.g. car, road, and traffic light are related but not similar) and requires certain adaptations. To meet the requirements of these broader perspectives of semantic similarity, we develop two approaches to learn the semantic related-ness of the spotted word and its environmental context: word-to-word (object) or word-to-sentence (caption). In the word-to-word approach, word embed-ding based re-rankers are developed. The re-ranker takes the words from the text spotting baseline and re-ranks them based on the visual context from the object classifier. For the second, an end-to-end neural approach is designed to drive image description (caption) at the sentence-level as well as the word-level (objects) and re-rank them based not only on the visual context but also on the co-occurrence between them. As an additional contribution, to meet the requirements of data-driven ap-proaches such as neural networks, we propose a visual context dataset for this task, in which the publicly available COCO-text dataset [Veit et al. 2016] has been extended with information about the scene (including the objects and places appearing in the image) to enable researchers to include the semantic relations between texts and scene in their Text Spotting systems, and to offer a common evaluation baseline for such approaches.Aquesta tesi aborda el problema de millorar els sistemes de reconeixement de text, que permeten detectar i reconèixer text en imatges no restringides (per exemple, un cartell al carrer, un anunci, una destinació d’autobús, etc.). L’objectiu és millorar el rendiment dels sistemes de visió existents explotant la informació semàntica derivada de la pròpia imatge. La idea principal és que conèixer el contingut de la imatge o el context visual en el que un text apareix, pot ajudar a decidir quines són les paraules correctes. Per exemple, el fet que una imatge mostri una cafeteria fa que sigui més probable que una paraula en un rètol es llegeixi com a Dunkin que no pas com unkind. Abordem aquest problema recorrent a avenços en el processament del llenguatge natural i l’aprenentatge automàtic, en particular, aprenent re-rankers i xarxes neuronals, per presentar solucions de postprocés que milloren els sistemes de l’estat de l’art de reconeixement de text, sense necessitat de costosos procediments de reentrenament o afinació que requereixin grans quantitats de dades. Descobrir el grau de relació semàntica entre les paraules candidates i el seu context d’imatge és una tasca relacionada amb l’avaluació de la semblança semàntica entre paraules o fragments de text. Tanmateix, determinar l’existència d’una relació semàntica és una tasca més general que avaluar la semblança (per exemple, cotxe, carretera i semàfor estan relacionats però no són similars) i per tant els mètodes existents requereixen certes adaptacions. Per satisfer els requisits d’aquestes perspectives més àmplies de relació semàntica, desenvolupem dos enfocaments per aprendre la relació semàntica de la paraula reconeguda i el seu context: paraula-a-paraula (amb els objectes a la imatge) o paraula-a-frase (subtítol de la imatge). En l’enfocament de paraula-a-paraula s’usen re-rankers basats en word-embeddings. El re-ranker pren les paraules proposades pel sistema base i les torna a reordenar en funció del context visual proporcionat pel classificador d’objectes. Per al segon cas, s’ha dissenyat un enfocament neuronal d’extrem a extrem per explotar la descripció de la imatge (subtítol) tant a nivell de frase com a nivell de paraula i re-ordenar les paraules candidates basant-se tant en el context visual com en les co-ocurrències amb el subtítol. Com a contribució addicional, per satisfer els requisits dels enfocs basats en dades com ara les xarxes neuronals, presentem un conjunt de dades de contextos visuals per a aquesta tasca, en el què el conjunt de dades COCO-text disponible públicament [Veit et al. 2016] s’ha ampliat amb informació sobre l’escena (inclosos els objectes i els llocs que apareixen a la imatge) per permetre als investigadors incloure les relacions semàntiques entre textos i escena als seus sistemes de reconeixement de text, i oferir una base d’avaluació comuna per a aquests enfocaments

Going Deeper with Semantics: Video Activity Interpretation using Semantic Contextualization

A deeper understanding of video activities extends beyond recognition of underlying concepts such as actions and objects: constructing deep semantic representations requires reasoning about the semantic relationships among these concepts, often beyond what is directly observed in the data. To this end, we propose an energy minimization framework that leverages large-scale commonsense knowledge bases, such as ConceptNet, to provide contextual cues to establish semantic relationships among entities directly hypothesized from video signal. We mathematically express this using the language of Grenander's canonical pattern generator theory. We show that the use of prior encoded commonsense knowledge alleviate the need for large annotated training datasets and help tackle imbalance in training through prior knowledge. Using three different publicly available datasets - Charades, Microsoft Visual Description Corpus and Breakfast Actions datasets, we show that the proposed model can generate video interpretations whose quality is better than those reported by state-of-the-art approaches, which have substantial training needs. Through extensive experiments, we show that the use of commonsense knowledge from ConceptNet allows the proposed approach to handle various challenges such as training data imbalance, weak features, and complex semantic relationships and visual scenes.Comment: Accepted to WACV 201

Language and Perceptual Categorization in Computational Visual Recognition

Computational visual recognition or giving computers the ability to understand images as well as humans do is a core problem in Computer Vision. Traditional recognition systems often describe visual content by producing a set of isolated labels, object locations, or by even trying to annotate every pixel in an image with a category. People instead describe the visual world using language. The rich visually descriptive language produced by people incorporates information from human intuition, world knowledge, visual saliency, and common sense that go beyond detecting individual visual concepts like objects, attributes, or scenes. Moreover, due to the rising popularity of social media, there exist billions of images with associated text on the web, yet systems that can leverage this type of annotations or try to connect language and vision are scarce. In this dissertation, we propose new approaches that explore the connections between language and vision at several levels of detail by combining techniques from Computer Vision and Natural Language Understanding. We first present a data-driven technique for understanding and generating image descriptions using natural language, including automatically collecting a big-scale dataset of images with visually descriptive captions. Then we introduce a system for retrieving short visually descriptive phrases for describing some part or aspect of an image, and a simple technique to generate full image descriptions by stitching short phrases. Next we introduce an approach for collecting and generating referring expressions for objects in natural scenes at a much larger scale than previous studies. Finally, we describe methods for learning how to name objects by using intuitions from perceptual categorization related to basic-level and entry-level categories. The main contribution of this thesis is in advancing our knowledge on how to leverage language and intuitions from human perception to create visual recognition systems that can better learn from and communicate with people.Doctor of Philosoph

Learning Social Image Embedding with Deep Multimodal Attention Networks

Learning social media data embedding by deep models has attracted extensive research interest as well as boomed a lot of applications, such as link prediction, classification, and cross-modal search. However, for social images which contain both link information and multimodal contents (e.g., text description, and visual content), simply employing the embedding learnt from network structure or data content results in sub-optimal social image representation. In this paper, we propose a novel social image embedding approach called Deep Multimodal Attention Networks (DMAN), which employs a deep model to jointly embed multimodal contents and link information. Specifically, to effectively capture the correlations between multimodal contents, we propose a multimodal attention network to encode the fine-granularity relation between image regions and textual words. To leverage the network structure for embedding learning, a novel Siamese-Triplet neural network is proposed to model the links among images. With the joint deep model, the learnt embedding can capture both the multimodal contents and the nonlinear network information. Extensive experiments are conducted to investigate the effectiveness of our approach in the applications of multi-label classification and cross-modal search. Compared to state-of-the-art image embeddings, our proposed DMAN achieves significant improvement in the tasks of multi-label classification and cross-modal search