297 research outputs found
What value do explicit high level concepts have in vision to language problems?
Much of the recent progress in Vision-to-Language (V2L) problems has been
achieved through a combination of Convolutional Neural Networks (CNNs) and
Recurrent Neural Networks (RNNs). This approach does not explicitly represent
high-level semantic concepts, but rather seeks to progress directly from image
features to text. We propose here a method of incorporating high-level concepts
into the very successful CNN-RNN approach, and show that it achieves a
significant improvement on the state-of-the-art performance in both image
captioning and visual question answering. We also show that the same mechanism
can be used to introduce external semantic information and that doing so
further improves performance. In doing so we provide an analysis of the value
of high level semantic information in V2L problems.Comment: Accepted to IEEE Conf. Computer Vision and Pattern Recognition 2016.
Fixed titl
Learning visual representations with neural networks for video captioning and image generation
La recherche sur les reĢseaux de neurones a permis de reĢaliser de larges progreĢs durant la dernieĢre deĢcennie. Non seulement les reĢseaux de neurones ont eĢteĢ appliqueĢs avec succeĢs pour reĢsoudre des probleĢmes de plus en plus complexes; mais ils sont aussi devenus lāapproche dominante dans les domaines ouĢ ils ont eĢteĢ testeĢs tels que la compreĢhension du langage, les agents jouant aĢ des jeux de manieĢre automatique ou encore la vision par ordinateur, graĢce aĢ leurs capaciteĢs calculatoires et leurs efficaciteĢs statistiques.
La preĢsente theĢse eĢtudie les reĢseaux de neurones appliqueĢs aĢ des probleĢmes en vision par ordinateur, ouĢ les repreĢsentations seĢmantiques abstraites jouent un roĢle fondamental. Nous deĢmontrerons, aĢ la fois par la theĢorie et par lāexpeĢrimentation, la capaciteĢ des reĢseaux de neurones aĢ apprendre de telles repreĢsentations aĢ partir de donneĢes, avec ou sans supervision.
Le contenu de la theĢse est diviseĢ en deux parties. La premieĢre partie eĢtudie les reĢseaux de neurones appliqueĢs aĢ la description de videĢo en langage naturel, neĢcessitant lāapprentissage de repreĢsentation visuelle. Le premier modeĢle proposeĢ permet dāavoir une attention dynamique sur les diffeĢrentes trames de la videĢo lors de la geĢneĢration de la description textuelle pour de courtes videĢos. Ce modeĢle est ensuite ameĢlioreĢ par lāintroduction dāune opeĢration de convolution reĢcurrente. Par la suite, la dernieĢre section de cette partie identifie un probleĢme fondamental dans la description de videĢo en langage naturel et propose un nouveau type de meĢtrique dāeĢvaluation qui peut eĢtre utiliseĢ empiriquement comme un oracle afin dāanalyser les performances de modeĢles concernant cette taĢche.
La deuxieĢme partie se concentre sur lāapprentissage non-superviseĢ et eĢtudie une famille de modeĢles capables de geĢneĢrer des images. En particulier, lāaccent est mis sur les āNeural Autoregressive Density Estimators (NADEs), une famille de modeĢles probabilistes pour les images naturelles. Ce travail met tout dāabord en eĢvidence une connection entre les modeĢles NADEs et les reĢseaux stochastiques geĢneĢratifs (GSN). De plus, une ameĢlioration des modeĢles NADEs standards est proposeĢe. DeĢnommeĢs NADEs iteĢratifs, cette ameĢlioration introduit plusieurs iteĢrations lors de lāinfeĢrence du modeĢle NADEs tout en preĢservant son nombre de parameĢtres.
DeĢbutant par une revue chronologique, ce travail se termine par un reĢsumeĢ des reĢcents deĢveloppements en lien avec les contributions preĢsenteĢes dans les deux parties principales, concernant les probleĢmes dāapprentissage de repreĢsentation seĢmantiques pour les images et les videĢos. De prometteuses directions de recherche sont envisageĢes.The past decade has been marked as a golden era of neural network research. Not only have neural networks been successfully applied to solve more and more challenging real- world problems, but also they have become the dominant approach in many of the places where they have been tested. These places include, for instance, language understanding, game playing, and computer vision, thanks to neural networksā superiority in computational efficiency and statistical capacity. This thesis applies neural networks to problems in computer vision where high-level and semantically meaningful representations play a fundamental role. It demonstrates both in theory and in experiment the ability to learn such representations from data with and without supervision. The main content of the thesis is divided into two parts. The first part studies neural networks in the context of learning visual representations for the task of video captioning. Models are developed to dynamically focus on different frames while generating a natural language description of a short video. Such a model is further improved by recurrent convolutional operations. The end of this part identifies fundamental challenges in video captioning and proposes a new type of evaluation metric that may be used experimentally as an oracle to benchmark performance. The second part studies the family of models that generate images. While the first part is supervised, this part is unsupervised. The focus of it is the popular family of Neural Autoregressive Density Estimators (NADEs), a tractable probabilistic model for natural images. This work first makes a connection between NADEs and Generative Stochastic Networks (GSNs). The standard NADE is improved by introducing multiple iterations in its inference without increasing the number of parameters, which is dubbed iterative NADE. With a historical view at the beginning, this work ends with a summary of recent development for work discussed in the first two parts around the central topic of learning visual representations for images and videos. A bright future is envisioned at the end
Speaking the Same Language: Matching Machine to Human Captions by Adversarial Training
While strong progress has been made in image captioning over the last years,
machine and human captions are still quite distinct. A closer look reveals that
this is due to the deficiencies in the generated word distribution, vocabulary
size, and strong bias in the generators towards frequent captions. Furthermore,
humans -- rightfully so -- generate multiple, diverse captions, due to the
inherent ambiguity in the captioning task which is not considered in today's
systems.
To address these challenges, we change the training objective of the caption
generator from reproducing groundtruth captions to generating a set of captions
that is indistinguishable from human generated captions. Instead of
handcrafting such a learning target, we employ adversarial training in
combination with an approximate Gumbel sampler to implicitly match the
generated distribution to the human one. While our method achieves comparable
performance to the state-of-the-art in terms of the correctness of the
captions, we generate a set of diverse captions, that are significantly less
biased and match the word statistics better in several aspects.Comment: 16 pages, Published in ICCV 201
Recent Trends in Computational Intelligence
Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications
Language-Driven Video Understanding
Video understanding has advanced quite a long way in the past decade, accomplishing tasks including low-level segmentation and tracking that study objects as pixel-level segments or bounding boxes to more high-level activity recognition or classification tasks that classify a video scene to a categorical action label. Despite the progress that has been made, much of this work remains a proxy for an eventual task or application that requires a holistic view of the video, such as objects, actions, attributes, and other semantic components.
In this dissertation, we argue that language could deliver the required holistic representation. It plays a significant role in video understanding by allowing machines to communicate with humans and to understand our requests, as shown in tasks such as text-to-video search engine, voice-guided robot manipulation, to name a few. Our language-driven video understanding focuses on two specific problems: video description and visual grounding. What marks our viewpoint different from prior literature is twofold. First, we propose a bottom-up structured learning scheme by decomposing a long video into individual procedure steps and representing each step with a description. Second, we propose to have both explicit (i.e., supervised) and implicit (i.e., weakly-supervised and self-supervised) grounding between words and visual concepts which enables interpretable modeling of the two spaces.
We start by drawing attention to the shortage of large benchmarks on long video-language and propose the largest-of-its-kind YouCook2 dataset and ActivityNet-Entities dataset in Chap. II and III. The rest of the chapters circle around two main problems: video description and visual grounding. For video description, we first address the problem of decomposing a long video into compact and self-contained event segments in Chap. IV. Given an event segment or short video clip in general, we propose a non-recurrent approach (i.e., Transformer) for video description generation in Chap. V as opposed to prior RNN-based methods and demonstrate superior performance. Moving forward, we notice one potential issue in end-to-end video description generation, i.e., lack of visual grounding ability and model interpretability that would allow humans to directly interact with machine vision models. To address this issue, we transition our focus from end-to-end, video-to-text systems to systems that could explicitly capture the grounding between the two modalities, with a novel grounded video description framework in Chap. VI. So far, all the methods are fully-supervised, i.e., the model training signal comes directly from heavy & expensive human annotations. In the following chapter, we answer the question "Can we perform visual grounding without explicit supervision?" with a weakly-supervised framework where models learn grounding from (weak) description signal. Finally, in Chap. VIII, we conclude the technical work by exploring a self-supervised grounding approachāvision-language pre-trainingāthat implicitly learns visual grounding from web multi-modal data. This mimics how humans obtain their commonsense from the environment through multi-modal interactions.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155174/1/luozhou_1.pd
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