Main product detection with graph networks for fashion

Altres ajuts: acord transformatiu CRUE-CSICAltres ajuts: Industrial Doctorate Grant 2016 DI 039Computer vision has established a foothold in the online fashion retail industry. Main product detection is a crucial step of vision-based fashion product feed parsing pipelines, focused on identifying the bounding boxes that contain the product being sold in the gallery of images of the product page. The current state-of-the-art approach does not leverage the relations between regions in the image, and treats images of the same product independently, therefore not fully exploiting visual and product contextual information. In this paper, we propose a model that incorporates Graph Convolutional Networks (GCN) that jointly represent all detected bounding boxes in the gallery as nodes. We show that the proposed method is better than the state-of-the-art, especially, when we consider the scenario where title-input is missing at inference time and for cross-dataset evaluation, our method outperforms previous approaches by a large margin

Investigating the role of hypothetical protein (AAB33144.1) in HIV-1 virus pathogenicity: A comparative study with FDA-Approved inhibitor compounds through In silico analysis and molecular docking

Aim and objective: Due to the a lot of unexplored proteins in HIV-1, this research aimed to explore the functional roles of a hypothetical protein (AAB33144.1) that might play a key role in HIV-1 pathogenicity. Methods: The homologous protein was identified along with building and validating the 3D structure by searching several bioinformatics tools. Results: Retroviral aspartyl protease and retropepsin like functional domains and motifs, folding pattern (cupredoxins), and subcellular localization in cytoplasmic membrane were determined as biological activity. Besides, the functional annotation revealed that the chosen hypothetical protein possessed protease-like activity. To validate our generated protein 3D structure, molecular docking was performed with five compounds where nelfinavir showed (− 8.2 kcal/mol) best binding affinity against HXB2 viral protease (PDB ID: 7SJX) and main protease (PDB ID: 4EYR) protein. Conclusions: This study suggests that the annotated hypothetical protein related to protease action, which may be useful in viral genetics and drug discovery

Int J Comput Vis DOI 10.1007/s11263-012-0561-4 Optimization of Robust Loss Functions for Weakly-Labeled Image Taxonomies

Abstract The recently proposed ImageNet dataset consists of several million images, each annotated with a single object category. These annotations may be imperfect, in the sense that many images contain multiple objects belonging to the label vocabulary. In other words, we have a multilabel problem but the annotations include only a single label (which is not necessarily the most prominent). Such a setting motivates the use of a robust evaluation measure, which allows for a limited number of labels to be predicted and, so long as one of the predicted labels is correct, the overall prediction should be considered correct. This is indeed the type of evaluation measure used to assess algorithm performance in a recent competition on ImageNet data. Optimizing such types of performance measures presents several hurdles even with existing structured output learning methods. Indeed, many of the current state-of-the-art methods optimize the prediction of only a single output label, ignoring this ‘structure ’ altogether. In this paper, we show how to directly optimize continuous surrogates of such performance measures using structured output learning techniques with latent variables. We use the output of existing binary classifiers as input features in a new learning stage which optimizes the structured loss corresponding to the robust per

Large-scale image classification using ensembles of

nested dichotomie

BreakingNews: article annotation by image and text processing

Building upon recent Deep Neural Network architectures, current approaches lying in the intersection of Computer Vision and Natural Language Processing have achieved unprecedented breakthroughs in tasks like automatic captioning or image retrieval. Most of these learning methods, though, rely on large training sets of images associated with human annotations that specifically describe the visual content. In this paper we propose to go a step further and explore the more complex cases where textual descriptions are loosely related to the images. We focus on the particular domain of news articles in which the textual content often expresses connotative and ambiguous relations that are only suggested but not directly inferred from images. We introduce an adaptive CNN architecture that shares most of the structure for multiple tasks including source detection, article illustration and geolocation of articles. Deep Canonical Correlation Analysis is deployed for article illustration, and a new loss function based on Great Circle Distance is proposed for geolocation. Furthermore, we present BreakingNews, a novel dataset with approximately 100K news articles including images, text and captions, and enriched with heterogeneous meta-data (such as GPS coordinates and user comments). We show this dataset to be appropriate to explore all aforementioned problems, for which we provide a baseline performance using various Deep Learning architectures, and different representations of the textual and visual features. We report very promising results and bring to light several limitations of current state-of-the-art in this kind of domain, which we hope will help spur progress in the field

Determining Where to Grasp Cloth Using Depth Information

Abstract. In this paper we address the problem of finding an initial good grasping point for the task of robotic manipulation of textile objects lying on a flat surface. Given an input point cloud of the cloth, acquired with a 3D camera, we propose choosing the grasping points as those that maximize a new measure of wrinkledness, computed from the distribution of normal directions over local neighborhoods. Real grasping experiments using a robotic arm are performed, showing promising results of the proposed measure

Verksamhetsmodell för klinisk specialistsjukskötare inom samjour vid Vasa centralsjukhus : - en kvalitativ studie

Syftet med studien var att utveckla en verksamhetsmodell för klinisk specialistsjukskötare inom samjour vid Vasa centralsjukhus. Frågeställningen för studien var: Hur skall verksamhetsmodellen utformas för en klinisk specialistsjukskötare? Vilka ansvarsområden kan en klinisk specialistsjukskötare inneha vid samjouren? Vilka arbetsuppgifter kan en klinisk specialistsjukskötare ha inom samjouren vid Vasa centralsjukhus? Metoden som användes var aktionsforskning med kvalitativ ansats. Datainsamlingsmetoden var enkät med öppna frågor till klinisk specialistsjukskötare i expertfunktion inom specialsjukvården och inom primärhälsovården vid olika sjukvårdsdistrikt i Finland. Data analyserades med innehållsanalys. För att utvärdera verksamhetsmodellen användes enkätsvaren och forskningar. Utgående från svaren bearbetades verksamhetsmodellen till det slutliga formatet. Resultatet av studien visar att klinisk specialistsjukskötaren arbetar självständigt, innehar en fördjupad medicinsk kompetens och har ett ansvar för att patienten skall få en evidensbaserad vård. Resultatet i studien visar också att om en klinisk specialistsjukskötare implementeras inom organisationen så utvecklas verksamhetsmodeller enligt de internationella kraven. Verksamhetsmodellens tyngdpunkt sätts på en god och trygg vård till patienterna. Målgruppen för klinisk specialistsjukskötare i denhär studien är främst patienter som besöker samjouren vid Vasa centralsjukhus.The aim of the study was to develop a case of management model for a clinical nurse specialist in primary health care at Vaasa Central Hospital. The research question was the following: How will the operational model be designed for a clinical nurse specialist? What responsibilities can be given to clinical nurse specialists in primary health care? What duties can clinical nurse specialists have within primary health care at Vaasa Central Hospital? The method used was action research with a qualitative approach. The instrument was a questionnaire with open-ended questions for nurses performing expert duties within specialist health care and primary health care, in various medical care districts in Finland. The data was analysed by means of content analysis. In order to evaluate the management model, the questionnaire responses and previous research were used, and based on the responses the management model was developed into its final format. The results of the study show that the clinical nurse specialist works independently, possesses in-depth medical skills and has a responsibility to ensure that the patient receives evidence-based care. The results of the study also show that the clinical nurse specialist is implemented within the organization to develop management models with the international requirements. The emphasis of the management model is good and safe care for patients. The target group for the clinical nurse specialist in this study is primarily patients who visit the primary health care at Vaasa Central Hospital