Monocular Object Instance Segmentation and Depth Ordering with CNNs

In this paper we tackle the problem of instance-level segmentation and depth ordering from a single monocular image. Towards this goal, we take advantage of convolutional neural nets and train them to directly predict instance-level segmentations where the instance ID encodes the depth ordering within image patches. To provide a coherent single explanation of an image we develop a Markov random field which takes as input the predictions of convolutional neural nets applied at overlapping patches of different resolutions, as well as the output of a connected component algorithm. It aims to predict accurate instance-level segmentation and depth ordering. We demonstrate the effectiveness of our approach on the challenging KITTI benchmark and show good performance on both tasks.Comment: International Conference on Computer Vision (ICCV), 201

Spatial distribution of trace metals in urban soils and road dusts : an example from Manchester, UK

Urban soil quality is of concern under current UK contaminated land legislation in terms of potential impacts on human health, due to the legacy of industrial, mining and waste disposal activities and the fact that soils can act as a sink for potentially harmful substances (PHS) in the urban environment. As part of the the Geochemical Baseline Survey of the Environment (G-BASE) project of the British Geological Survey (BGS), 27 UK cities have been surveyed to establish baselines and assess the quality of urban soils. The G-BASE soil geochemical dataset for Manchester forms the basis of this project. Another medium that is a likely sink for PHS in urban environments is road dust sediment (RDS). RDS forms as an accumulation of particles on pavements and road surfaces, and has been shown to be both spatially and temporally highly variable in composition, as it is more susceptible to remobilisation and transport. RDS has been documented as carrying a high loading of contaminant species, including significant amounts of trace metals. Geochemical data from both soils and RDS, despite having different properties, are essential for environmental assessment in urban areas. Although studies of PHS in RDS and soils have been published, little is known about the spatial, geochemical and mineralogical linkages between these two media. The aim of this research is to define and establish these linkages, and produce novel mineralogical data on the PHS–particulate relationships within soils and RDS

Local integrands for the five-point amplitude in planar N=4 SYM up to five loops

Integrands for colour ordered scattering amplitudes in planar N=4 SYM are dual to those of correlation functions of the energy-momentum multiplet of the theory. The construction can relate amplitudes with different numbers of legs. By graph theory methods the integrand of the four-point function of energy-momentum multiplets has been constructed up to six loops in previous work. In this article we extend this analysis to seven loops and use it to construct the full integrand of the five-point amplitude up to five loops, and in the parity even sector to six loops. All results, both parity even and parity odd, are obtained in a concise local form in dual momentum space and can be displayed efficiently through graphs. We have verified agreement with other local formulae both in terms of supertwistors and scalar momentum integrals as well as BCJ forms where those exist in the literature, i.e. up to three loops. Finally we note that the four-point correlation function can be extracted directly from the four-point amplitude and so this uncovers a direct link from four- to five-point amplitudes.Comment: 29 pages LaTeX, 8 figure

Learning Deep Structured Models

Many problems in real-world applications involve predicting several random variables which are statistically related. Markov random fields (MRFs) are a great mathematical tool to encode such relationships. The goal of this paper is to combine MRFs with deep learning algorithms to estimate complex representations while taking into account the dependencies between the output random variables. Towards this goal, we propose a training algorithm that is able to learn structured models jointly with deep features that form the MRF potentials. Our approach is efficient as it blends learning and inference and makes use of GPU acceleration. We demonstrate the effectiveness of our algorithm in the tasks of predicting words from noisy images, as well as multi-class classification of Flickr photographs. We show that joint learning of the deep features and the MRF parameters results in significant performance gains.Comment: 11 pages including referenc

Retrieval of bilingual Spanish-English information by means of a standard automatic translation system

This paper describes our participation in bilingual retrieval (queries in Spanish on documents in English), by means of an information retrieval system based on the vector model. The queries, formulated in Spanish, were translated into English by means of a commercial automatic translation system; the terms extracted from the resulting translations were filtered in order to get rid of empty words and then they were normalised by stemming. Results are poorer than those obtained through monolingual retrieval with the original queries in English slightly above 15%