Joint Learning of Intrinsic Images and Semantic Segmentation

Semantic segmentation of outdoor scenes is problematic when there are variations in imaging conditions. It is known that albedo (reflectance) is invariant to all kinds of illumination effects. Thus, using reflectance images for semantic segmentation task can be favorable. Additionally, not only segmentation may benefit from reflectance, but also segmentation may be useful for reflectance computation. Therefore, in this paper, the tasks of semantic segmentation and intrinsic image decomposition are considered as a combined process by exploring their mutual relationship in a joint fashion. To that end, we propose a supervised end-to-end CNN architecture to jointly learn intrinsic image decomposition and semantic segmentation. We analyze the gains of addressing those two problems jointly. Moreover, new cascade CNN architectures for intrinsic-for-segmentation and segmentation-for-intrinsic are proposed as single tasks. Furthermore, a dataset of 35K synthetic images of natural environments is created with corresponding albedo and shading (intrinsics), as well as semantic labels (segmentation) assigned to each object/scene. The experiments show that joint learning of intrinsic image decomposition and semantic segmentation is beneficial for both tasks for natural scenes. Dataset and models are available at: https://ivi.fnwi.uva.nl/cv/intrinsegComment: ECCV 201

Grown furniture: a move towards design for sustainability

This thesis deals with the proposal that environmentally benign items of free standing furniture may be produced by the use of such well established techniques as training and grafting natural tree growth to shape. The project has been driven by the growing environmental concerns of which mankind has become aware in the late twentieth century, and which are starting to exert such a powerful influence in the twenty first. A broad history of man's use and control of natural tree growth, ranging geographically from Europe to Australia, and in size from hand held agricultural picks to eighteenth century sailing ships, is followed by a brief description of the ways in which the explosive increase in world popuanon. together with the expanding industrial activities of the Western consumer society, are feared to be threatening the stability of the natural environment. The various disasters and catastrophic accidents which have brought this situation to the attention of the general public are briefly surveyed, together with National, International and a range of Industrial responses. As one of the professions most closely concerned with the production of consumer items, the various reactions of the Design Community are similarly examined. In conclusion, the author's proposal for an experimental item of furnitureenvironmentally benign in production, use and disposal - is described and illustrated. A simple free standing three legged stool, the form of both the item itself and that of the jig required to control it's growth, are described and illustrated. The growth of examples of this, carried out on three sites across southern Britain are documented, experimental results reported and discussed. A further range of designs suitable to be produced using this method of controlling and grafting natural growth is proposed, and suggestions made for further experimentation

Modélisation de vignes à partir d'une séquence d'images

National audienceCet article présente des travaux sur la modélisation de plantes à géométries fortement contraintes à partir d'images. A partir de séquences d'images acquises dans un vignoble, nous instancions un modèle paramétré des parcelles, des rangs, et des pieds de vignes. Le modèle est déduit des connaissances a priori ; à partir des images, des paramètres sont extraits. Ces paramètres sont ensuite fournis au modèle qui génère une représentation de la plante, du rang ou de la parcelle filmée

The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey

Recently, various neural encoder-decoder models pioneered by Seq2Seq framework have been proposed to achieve the goal of generating more abstractive summaries by learning to map input text to output text. At a high level, such neural models can freely generate summaries without any constraint on the words or phrases used. Moreover, their format is closer to human-edited summaries and output is more readable and fluent. However, the neural model's abstraction ability is a double-edged sword. A commonly observed problem with the generated summaries is the distortion or fabrication of factual information in the article. This inconsistency between the original text and the summary has caused various concerns over its applicability, and the previous evaluation methods of text summarization are not suitable for this issue. In response to the above problems, the current research direction is predominantly divided into two categories, one is to design fact-aware evaluation metrics to select outputs without factual inconsistency errors, and the other is to develop new summarization systems towards factual consistency. In this survey, we focus on presenting a comprehensive review of these fact-specific evaluation methods and text summarization models.Comment: 9 pages, 5 figure