Reimagining unfinished architectures: ruin perspectives between art and heritage

For the past five decades, hundreds of unfinished public works have been erected in Italy as the result of inconsistent planning and the presence of corruption and organised crime. A third of these constructions are located in Sicily alone, and so, in 2007, a group of artists labelled this phenomenon an architectural style: ‘Incompiuto Siciliano’. Through this creative approach, the artists’ objective is to put incompletion back on the agenda by viewing it from a heritage perspective. This article reviews the different approaches that the artists have envisaged to handle unfinished public works; whether to finish them, demolish them, leave them as they are or opt for an ‘active’ arrested decay. The critical implications of these strategies are analysed in order to, ultimately, conclude that incompletion is such a vast and complex issue that it will surely have more than one single solution; but rather a combination of these four. This is important because it opens up a debate on the broad spectrum of possibilities to tackle incompletion – establishing this as one of the key contemporary urban themes not only in Italy but also in those countries affected by unfinished geographies after the 2008 financial crisis

ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity

We present ALADIN (All Layer AdaIN); a novel architecture for searching images based on the similarity of their artistic style. Representation learning is critical to visual search, where distance in the learned search embedding reflects image similarity. Learning an embedding that discriminates fine-grained variations in style is hard, due to the difficulty of defining and labelling style. ALADIN takes a weakly supervised approach to learning a representation for fine-grained style similarity of digital artworks, leveraging BAM-FG, a novel large-scale dataset of user generated content groupings gathered from the web. ALADIN sets a new state of the art accuracy for style-based visual search over both coarse labelled style data (BAM) and BAM-FG; a new 2.62 million image dataset of 310,000 fine-grained style groupings also contributed by this work

A Survey for Graphic Design Intelligence

Graphic design is an effective language for visual communication. Using complex composition of visual elements (e.g., shape, color, font) guided by design principles and aesthetics, design helps produce more visually-appealing content. The creation of a harmonious design requires carefully selecting and combining different visual elements, which can be challenging and time-consuming. To expedite the design process, emerging AI techniques have been proposed to automatize tedious tasks and facilitate human creativity. However, most current works only focus on specific tasks targeting at different scenarios without a high-level abstraction. This paper aims to provide a systematic overview of graphic design intelligence and summarize literature in the taxonomy of representation, understanding and generation. Specifically we consider related works for individual visual elements as well as the overall design composition. Furthermore, we highlight some of the potential directions for future explorations.Comment: 10 pages, 2 figure

Solving Jigsaw Puzzles with Eroded Boundaries

Jigsaw puzzle solving is an intriguing problem which has been explored in computer vision for decades. This paper focuses on a specific variant of the problem - solving puzzles with eroded boundaries. Such erosion makes the problem extremely difficult, since most existing solvers utilize solely the information at the boundaries. Nevertheless, this variant is important since erosion and missing data often occur at the boundaries. The key idea of our proposed approach is to inpaint the eroded boundaries between puzzle pieces and later leverage the quality of the inpainted area to classify a pair of pieces as 'neighbors or not'. An interesting feature of our architecture is that the same GAN discriminator is used for both inpainting and classification; Training of the second task is simply a continuation of the training of the first, beginning from the point it left off. We show that our approach outperforms other SOTA methodsComment: 8 page