Naver Corporation

About: Naver Corporation is a company organization based out in Seongnam-si, South Korea. It is known for research contribution in the topics: Terminal (electronics) & Computer science. The organization has 4038 authors who have published 4294 publications receiving 35045 citations. The organization is also known as: NAVER Corporation & NAVER.

Personalized search method and system for enabling the method

Abstract: A method and system for storing information on a search result selected by a user with respect to a predetermined query, reflecting the user's preference by utilizing the information when the user inputs an identical query and requests a search, and providing a personalized search result in which user preferences are reflected. More particularly, a personalized search method and system utilizing favorites of a web browser in which a personalized search result is provided to each user by utilizing information on a website that the user bookmarked as the user's favorites is provided. According to the present invention, there is provided a personalized search service method and system which can provide a user with a personalized search service, even when user information is not stored in a search server and also, even when the user does not log in.

LA-VITON: A Network for Looking-Attractive Virtual Try-On

Abstract: In this paper, we propose an image-based virtual try-on network, LA-VITON, which allows the generation of high fidelity try-on images that preserves both the overall appearance and the characteristics of clothing items. The proposed network consists of two modules: Geometric Matching Module (GMM) and Try-On Module (TOM). To warp in-shop clothing item to the desired image of a person with high accuracy in GMM, grid interval consistency loss and an occlusion handling technique are proposed. Grid interval consistency loss regularizes transformation to prevent distortion of patterns in clothes and an occlusion handling technique encourages proper warping despite target bodies are covered by hair or arms. The following TOM synthesizes the final try-on image of the target person seamlessly with the warped clothes from GMM. Extensive experiments on fashion datasets show that the proposed method outperforms the state-of-the-art methods.

Using Microwave Heating to Completely Recycle Concrete

Abstract: The aim of this study was to develop a technique for the complete recycling of concrete based on microwave heating of surface modification coarse aggregate (SMCA) with only inorganic materials such as cement and pozzolanic materials (silica fume, fly ash). The mechanical properties of SMCA, which was produced using original coarse aggregate (OCA) and inorganic admixtures, as well as its separation from the cement matrix and recovery performance were quantitatively assessed. The experimental results showed that micro structural reinforcement of the interfacial transition zone, which is a weak part of concrete, by coating the surface of the OCA with cement and admixtures such as pozzolanic materials can help suppress the occurrence of micro-cracks and improve the mechanical performance of the OCA. Microwave heating was observed to cause micro-cracking and hydrate decomposition. Increasing the void volume and weakening the hydrated cement paste led to the effective recovery of recycled coarse aggregate.

Spatial Dependency Parsing for Semi-Structured Document Information Extraction

Abstract: Information Extraction (IE) for semi-structured document images is often approached as a sequence tagging problem by classifying each recognized input token into one of the IOB (Inside, Outside, and Beginning) categories. However, such problem setup has two inherent limitations that (1) it cannot easily handle complex spatial relationships and (2) it is not suitable for highly structured information, which are nevertheless frequently observed in real-world document images. To tackle these issues, we first formulate the IE task as spatial dependency parsing problem that focuses on the relationship among text tokens in the documents. Under this setup, we then propose SPADE (SPAtial DEpendency parser) that models highly complex spatial relationships and an arbitrary number of information layers in the documents in an end-to-end manner. We evaluate it on various kinds of documents such as receipts, name cards, forms, and invoices, and show that it achieves a similar or better performance compared to strong baselines including BERT-based IOB taggger.