Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views

This paper presents an end-to-end convolutional neural network (CNN) for 2D-3D exemplar detection. We demonstrate that the ability to adapt the features of natural images to better align with those of CAD rendered views is critical to the success of our technique. We show that the adaptation can be learned by compositing rendered views of textured object models on natural images. Our approach can be naturally incorporated into a CNN detection pipeline and extends the accuracy and speed benefits from recent advances in deep learning to 2D-3D exemplar detection. We applied our method to two tasks: instance detection, where we evaluated on the IKEA dataset, and object category detection, where we out-perform Aubry et al. for "chair" detection on a subset of the Pascal VOC dataset.Comment: To appear in CVPR 201

Choosing between foreign investment and subcontracting: Strategies of Italian firms in Romania

Vertical disintegration in most industries and the globalization of markets has led to significant changes in the pattern of international division of labour among manufacturing firms. At the same time increased competition from low cost producers, exchange rate constraints, the opening up of CEE countries have had huge consequences for the Italian industrial system. This paper deals with the Veneto footwear, furniture and refrigeraion industries and examines the effects of foreign direct investments and subcontracting in Romania. The reorganization of the division of labour, in the most dynamic suppliers induced a change in the “nature of subcontracting”, upgrading along the ladder of the value chain as more and more operations are offshored.Foreign direct investment; International subcontracting;

Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling

We study 3D shape modeling from a single image and make contributions to it in three aspects. First, we present Pix3D, a large-scale benchmark of diverse image-shape pairs with pixel-level 2D-3D alignment. Pix3D has wide applications in shape-related tasks including reconstruction, retrieval, viewpoint estimation, etc. Building such a large-scale dataset, however, is highly challenging; existing datasets either contain only synthetic data, or lack precise alignment between 2D images and 3D shapes, or only have a small number of images. Second, we calibrate the evaluation criteria for 3D shape reconstruction through behavioral studies, and use them to objectively and systematically benchmark cutting-edge reconstruction algorithms on Pix3D. Third, we design a novel model that simultaneously performs 3D reconstruction and pose estimation; our multi-task learning approach achieves state-of-the-art performance on both tasks.Comment: CVPR 2018. The first two authors contributed equally to this work. Project page: http://pix3d.csail.mit.ed

Standardized Marketing Strategies in Retailing? IKEA's marketing strategies in Sweden, the UK and China

IKEA is often cited as an example of a “global” retailer which pursues a similar “standardized” approach in every market. This paper systematically assesses the degree of standardization (and adaptation) of four commonly identified retail marketing mix activities – merchandise, location and store format, the selling and service environment, and market communication – within three countries. These countries – Sweden, the UK and China – represent different cultural settings and are markets in which IKEA has been operating for different lengths of time. The data upon which the comparison is based was generated from personal interviews, in-country consumer research, company documentation and third party commentaries. The conclusions drawn suggest that whilst IKEA operates a standardized concept, degrees of adaptation can be observed in customer facing elements, and in the supporting “back office” processes which support these elements. These adaptations arise from differences in consumer cultures and the length of time, and subsequent exposure to and experience of, the market. This suggests that standardization in international retailing should be considered from the perspective of replicating the concept, rather than replicating the activities

A machine learning based recommendation system for furniture selection

Artificial Intelligence (AI) is one of today's fastest growing technologies, and has been evolving for decades. It allows machines to have the ability to "learn", and self-correct. This technology is used in many fields, such as decision making, diagnostics in medicine, pattern recognition and virtual reality among others. This project has been carried out at StageInHome, a startup company specialized in AI and Deep Learning for interior decoration. In this case, the proposal is to build a bed recommendation system. For this project, we have created databases of both images and metadata, an image retrieval according to resemblance, a classifier to differentiate bed types and finally a user interface that allows an easy use of the whole implemented system, including also a price filter.La Inteligencia Artificial (IA) es una de las tecnologías con más perspectivas de crecimiento de hoy en día, y que lleva evolucionando desde décadas atrás. Esta tecnología permite que máquinas tengan la capacidad de "aprender", "razonar" o autocorregirse y es utilizada en muchos campos, como en toma de decisiones , dignósticos en medicina, reconocimiento de patrones y realidad virtual entre otros. Este proyecto se ha llevado acabo en StageInHome, empresa especializada en IA y Deep Learning para la decoración de espacios interiores. En este caso, se propone construir un sistema recomendador de camas. Para este proyecto, se han creado bases de datos tanto de imágenes como de metadatos, un recomendador de imágenes según el parecido, un clasificador para diferenciar los tipos de cama y finalmente una interfaz de usuario que permite un fácil uso de todo el sistema implementado incluyendo también un filtro de precio.La Inteligència Artificial (IA) és una de les tecnologies amb més perspectiva de creixement d'avuí en dia, i que porta evolucionant desde fa dècades. Aquesta tecnologia permet que màquines tinguin la capacitat d'aprendre, raonar o autocorretgir-se i s'utilitza en camps molt diversos com en presa de decisions, diagnòstics de medicina, reconeixement de patrons i realitat virtual entre d'altres. Aquest projecte s'ha d'esenvolupat a StageInHome, una empresa especialitzada en IA i Deep Learning per a la decoració automàtica d'espais interiors. En aquest cas, es proposa un sistema recommanador de llits. Per aquest projecte, s'han creat bases de dades, tant de imatges com de metadades, un recomanador d'imatges segons la semblança, un classificador per diferenciar els tipus de llit i finalment una interfície d'usuari que permet un fàcil ús de tot el sistema implementat també un filtre de preu

The preferred Complex Purchase Process in-store – A case study on IKEA

The purpose of this study is to find what opportunities retailers should capture in order to improve the complex purchase process in-store, through integrating online channels. This in order to create the best possible way to meet customers’ demands in the ever-changing multi-channel retail environment.Syftet med denna studie är att hitta vilka möjligheter detaljister bör fånga för att förbättra den komplexa inköpprocessen i butik, genom att integrera online-kanaler. Detta för att möta kundens krav i den ständigt föränderliga multi-kanal miljön

Furniture Stability: A Review of Data and Testing Results

This report by Kids In Danger (KID) and Shane's Foundation focuses on tip-overs of dressers and chests. ASTM International, which has developed thousands of voluntary industry consensus technical standards, has a standard in place to test furniture stability. However, furniture on the market is not required to conform, resulting in widespread non-compliance. Additionally, these standards are too lenient and require reform, as testing protocols have remained virtually unchanged for over a decade, despite continuing injuries and deaths. Units may pass the standard, but still present a significant risk. KID advocates for a two-pronged approach to decreasing tip-over incidents:Increasing consumer awareness of the danger of furniture tip-overs and knowledge of the actions needed to keep children safe, andImproving furniture stability by strengthening standards, making those standards mandatory and enforceable and promoting changes in furniture design.KID compiled data from incidents reported to the U.S. Consumer Product Safety Commission (CPSC) by various sources and from the National Electronic Injury Surveillance System (NEISS). These include reports from January 1, 2010 to October 14, 2015. Findings of the data analysis include:Two-year-olds are the age group most affected by tip-overs, especially in regard to fatal incidents.Children age 2 to 5 accounted for 77% of total incidents.The age range of children injured is wider than the age range of children killed by tip-overs.Fatalities accounted for 12% of total incidents.Head injuries (37%) were the most common category of injury.Almost all (98.7%) of head injuries are related to a television tipping over on a child.KID conducted performance tests on a sample of 19 dressers and chests. Testing was run at the UL Furniture Center of Excellence in Holland, Michigan. UL laboratory technicians followed a testing protocol developed by KID. The protocol included tests based on the current voluntary standard for furniture stability. KID added tests that, among other things, evaluated for tip-overs when more weight was added (simulating larger children), drawers were full of clothes, furniture was placed on carpeting as opposed to bare flooring, televisions were placed on top of the furniture, and additional drawers were opened simultaneous with weighting one drawer. These additional tests were intended to be more representative of real-world scenarios.Test results include:Only nine of the 19 units passed performance tests based on the current tip-over safety standard, ASTM F2057.Only two units passed all tests, including the additional testing protocols added by KID.The weight of a television or any type placed on top of the unit did not decrease the stability of furniture.Furniture placed on carpet is less stable than furniture placed on hard floors.Many units remained stable when more than 70 pounds was placed on an open drawer, while others tipped with less than half that weight