Deep learning for large-scale fine-grained recognition of cars

Deep learning (DL) is widely used nowadays, with several applications in image classification and object detection. Among many of these applications is the use of Convolutional Neural Networks (CNNs) whose operation is: for a given input (image) and output (label/class), generate representations that define and allow to distinguish different kinds of objects. Neural Networks are computationally demanding, taking hours to train. Convolutional Neural Networks are even more demanding since their input data are usually images – a rich data type that holds a lot of information. The fast evolution in Computer Vision, using deep learning techniques, and computing power recently allowed to train CNNs which can classify images with high precision. In car classifieds websites images are one of the most important types of content. However, until today, little knowledge/metadata is produced from such images. In order to insert an advert in the platform, the user must upload an image of the car for sale and fill a certain number of fields, among them the vehicle category, the color of the car and its respective make, model and version. In this dissertation, CNNs are used for the recognition of the make, model and version of cars where transfer learning and fine-tuning are two approaches used for transferring the knowledge learned in one task and adapting it to another. We extend the work to also validate the efficacy of these neural networks on the tasks of vehicle category and cars’ color recognition. We pretend to validate how CNNs behave in these different tasks. Approaches like background removal and data augmentation are explored for reducing overfitting. We collected one of the largest datasets to date for the task of make, model and version recognition of cars, composed of 1.2 million images belonging to 790 labels.The results obtained in the scope of this dissertation set a new state-of-the-art performance for this type of task (accuracy of 92.7% on an ensemble method) considering the number of classes to classify and the number of images used. It is demonstrated the efficacy of the recent advances in CNN architectures in fine-grained classification where intra-class variation is small and viewpoint variation is high, when a largescale dataset is used.Deep Learning (DL) é um termo cada vez mais mencionado nos dias de hoje, com vastas aplicações em classificação de imagens e detecção de objectos. Por detrás de muitas destas aplicações está a utilização de Convolutional Neural Networks (CNN) cujo funcionamento é, para um dado input (imagem) e output (nome do objecto representado/classe), produzir representações que definem e permitem distinguir vários tipos de objectos. As redes neuronais são computacionalmente exigentes e podem levar horas a ser treinadas. Convolutional Neural Networks são ainda mais exigentes visto o seu input ser, usualmente, imagens - um tipo de dados rico que contém muita informação. Com a rápida evolução do poder computacional aliada à evolução no campo de Computer Vision com recurso a CNNs é possível, somente nos últimos anos, treinar CNNs para classificação de imagens com alto nível de precisão. Em sites de classificados de carros as imagens são um dos tipos de conteúdo mais importante. Todavia até aos dias de hoje, pouco conhecimento/metadados são gerados a partir das mesmas. O utilizador tem sempre que, para inserir um anúncio na plataforma, preencher um vasto número de campos, entre eles a categoria do veículo, a cor do carro e a respectiva marca, modelo e versão, e inserir uma imagem do carro para venda. Nesta dissertação são utilizadas CNNs para o reconhecimento da marca, modelo e versão de carros em que se utiliza transfer learning e fine-tuning para transferir o conhecimento “aprendido” numa tarefa e adaptá-lo para outra. O trabalho é estendido de forma a demonstrar, também, a eficácia destas redes neuronais para as tarefas de reconhecimento da categoria do veículo e reconhecimento de cor de carros. Pretendemos validar como as CNNs se comportam nestes diferentes tipos de tarefas. Abordagens como remoção do fundo da imagem e data augmentation são utilizadas para reduzir overfitting.É obtido um dos maiores datasets para a tarefa de reconhecimento de marca, modelo e versão de carros, composto por 1,2 milhões de imagens pertencentes a 790 classes. Os resultados apresentados são dos melhores para este tipo de tarefa (precisão de 92.7% com um ensemble) considerando tanto o número de classes a classificar como o número de imagens utilizadas. Os resultados obtidos evidenciam a eficácia das arquitecturas de CNNs modernas para a classificação granular onde a variação intra-classe é reduzida e a variação da perspectiva é elevada, quando é utilizado um dataset de grandes dimensões

Do informal caregivers of people with dementia mirror the cognitive deficits of their demented patients?:A pilot study

Recent research suggests that informal caregivers of people with dementia (ICs) experience more cognitive deficits than noncaregivers. The reason for this is not yet clear. Objective: to test the hypothesis that ICs ‘mirror' the cognitive deficits of the demented people they care for. Participants and methods: 105 adult ICs were asked to complete three neuropsychological tests: letter fluency, category fluency, and the logical memory test from the WMS-III. The ICs were grouped according to the diagnosis of their demented patients. One-sample ttests were conducted to investigate if the standardized mean scores (t-scores) of the ICs were different from normative data. A Bonferroni correction was used to correct for multiple comparisons. Results: 82 ICs cared for people with Alzheimer's dementia and 23 ICs cared for people with vascular dementia. Mean letter fluency score of the ICs of people with Alzheimer's dementia was significantly lower than the normative mean letter fluency score, p = .002. The other tests yielded no significant results. Conclusion: our data shows that ICs of Alzheimer patients have cognitive deficits on the letter fluency test. This test primarily measures executive functioning and it has been found to be sensitive to mild cognitive impairment in recent research. Our data tentatively suggests that ICs who care for Alzheimer patients also show signs of cognitive impairment but that it is too early to tell if this is cause for concern or not

The progression of political censorship : Hong Kong cinema from colonial rule to Chinese-style socialist hegemony

Censorship is an important cultural regulatory instrument for the government of a society, or even a state. In certain socio-political settings, it can become a powerful administrative appartus (dispositif) and technique (techne) designed to render society governable. Censorship decisions often embody hegemonic views on social and political issues. No matter how virtuous the original intent maybe, the practice of censorship is inevitably geared to the social tensions surrounding issues of human rights and political dissent. The theory behind film censorship may once have been benign but banning or cutting a movie always involves an unnatural set of procedures and actions. This study examines this problem in the context of socio-political changes in Hong Kong. It is an enquiry into the evolution of political film censoship in its more conventional form to its full-fledged integration into other institutions and policies under today\u27s \u27on country, two systems\u27 policy. It also analyses the discourse surrounding the changes in film censorship practices from the days of early cinema to Hong Kong in the 21st century. By contextualizing Hong Kong cinema from a historical and political perspective, the study of the Hong Kong experience aims to shed light on censorship\u27s socio-political meanings for, and effects on, filmmakers and film production