Learning to rank in person re-identification with metric ensembles

We propose an effective structured learning based approach to the problem of person re-identification which outperforms the current state-of-the-art on most benchmark data sets evaluated. Our framework is built on the basis of multiple low-level hand-crafted and high-level visual features. We then formulate two optimization algorithms, which directly optimize evaluation measures commonly used in person re-identification, also known as the Cumulative Matching Characteristic (CMC) curve. Our new approach is practical to many real-world surveillance applications as the re-identification performance can be concentrated in the range of most practical importance. The combination of these factors leads to a person re-identification system which outperforms most existing algorithms. More importantly, we advance state-of-the-art results on person re-identification by improving the rank-$1$ recognition rates from $40\%$ to $50\%$ on the iLIDS benchmark, $16\%$ to $18\%$ on the PRID2011 benchmark, $43\%$ to $46\%$ on the VIPeR benchmark, $34\%$ to $53\%$ on the CUHK01 benchmark and $21\%$ to $62\%$ on the CUHK03 benchmark.Comment: 10 page

Plot-based urbanism and urban morphometrics : measuring the evolution of blocks, street fronts and plots in cities

Generative urban design has been always conceived as a creation-centered process, i.e. a process mainly concerned with the creation phase of a spatial transformation. We argue that, though the way we create a space is important, how that space evolves in time is ways more important when it comes to providing livable places gifted by identity and sense of attachment. We are presenting in this paper this idea and its major consequences for urban design under the title of “Plot-Based Urbanism”. We will argue that however, in order for a place to be adaptable in time, the right structure must be provided “by design” from the outset. We conceive urban design as the activity aimed at designing that structure. The force that shapes (has always shaped) the adaptability in time of livable urban places is the restless activity of ordinary people doing their own ordinary business, a kind of participation to the common good, which has hardly been acknowledged as such, that we term “informal participation”. Investigating what spatial components belong to the spatial structure and how they relate to each other is of crucial importance for urban design and that is the scope of our research. In this paper a methodology to represent and measure form-related properties of streets, blocks, plots and buildings in cities is presented. Several dozens of urban blocks of different historic formation in Milan (IT) and Glasgow (UK) are surveyed and analyzed. Effort is posed to identify those spatial properties that are shared by clusters of cases in history and therefore constitute the set of spatial relationships that determine the morphological identity of places. To do so, we investigate the analogy that links the evolution of urban form as a cultural construct to that of living organisms, outlining a conceptual framework of reference for the further investigation of “the DNA of places”. In this sense, we identify in the year 1950 the nominal watershed that marks the first “speciation” in urban history and we find that factors of location/centrality, scale and street permeability are the main drivers of that transition towards the entirely new urban forms of contemporary cities

Tile2Vec: Unsupervised representation learning for spatially distributed data

Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks. To fill this gap, we introduce Tile2Vec, an unsupervised representation learning algorithm that extends the distributional hypothesis from natural language -- words appearing in similar contexts tend to have similar meanings -- to spatially distributed data. We demonstrate empirically that Tile2Vec learns semantically meaningful representations on three datasets. Our learned representations significantly improve performance in downstream classification tasks and, similar to word vectors, visual analogies can be obtained via simple arithmetic in the latent space.Comment: 8 pages, 4 figures in main text; 9 pages, 11 figures in appendi

FVQA: Fact-based Visual Question Answering

Visual Question Answering (VQA) has attracted a lot of attention in both Computer Vision and Natural Language Processing communities, not least because it offers insight into the relationships between two important sources of information. Current datasets, and the models built upon them, have focused on questions which are answerable by direct analysis of the question and image alone. The set of such questions that require no external information to answer is interesting, but very limited. It excludes questions which require common sense, or basic factual knowledge to answer, for example. Here we introduce FVQA, a VQA dataset which requires, and supports, much deeper reasoning. FVQA only contains questions which require external information to answer. We thus extend a conventional visual question answering dataset, which contains image-question-answerg triplets, through additional image-question-answer-supporting fact tuples. The supporting fact is represented as a structural triplet, such as . We evaluate several baseline models on the FVQA dataset, and describe a novel model which is capable of reasoning about an image on the basis of supporting facts.Comment: 16 page

Typological Differences Influence the Bilingual Advantage in Metacognitive Processing

This article was published Online First June 6, 2022.Previous studies showed a bilingual advantage in metacognitive processing (tracking one’s own cognitive performance) in linguistic tasks. However, bilinguals do not constitute a homogeneous population, and it was unclear which aspects of bilingualism affect metacognition. In this project, we tested the hypothesis that simultaneous acquisition and use of typologically different languages leads to development of diverse processing strategies and enhances metacognition. The hypothesis was tested in the visual and auditory modalities in language and nonlanguage domains, in an artificial language learning task. In the auditory modality, the hypothesis was confirmed for linguistic stimuli, with no between-domain transfer of metacognitive abilities was observed at the individual level. In the visual modality, no differences in metacognitive efficiency were observed. Moreover, we found that bilingualism per se and the use of typologically different languages modulated separate metacognitive processes engaged in monitoring cognitive performance in statistical learning task