A Strong Baseline for Fashion Retrieval with Person Re-Identification Models

Fashion retrieval is the challenging task of finding an exact match for fashion items contained within an image. Difficulties arise from the fine-grained nature of clothing items, very large intra-class and inter-class variance. Additionally, query and source images for the task usually come from different domains - street photos and catalogue photos respectively. Due to these differences, a significant gap in quality, lighting, contrast, background clutter and item presentation exists between domains. As a result, fashion retrieval is an active field of research both in academia and the industry. Inspired by recent advancements in Person Re-Identification research, we adapt leading ReID models to be used in fashion retrieval tasks. We introduce a simple baseline model for fashion retrieval, significantly outperforming previous state-of-the-art results despite a much simpler architecture. We conduct in-depth experiments on Street2Shop and DeepFashion datasets and validate our results. Finally, we propose a cross-domain (cross-dataset) evaluation method to test the robustness of fashion retrieval models.Comment: 33 pages, 14 figure

Multi-modal Embedding Fusion-based Recommender

Recommendation systems have lately been popularized globally, with primary use cases in online interaction systems, with significant focus on e-commerce platforms. We have developed a machine learning-based recommendation platform, which can be easily applied to almost any items and/or actions domain. Contrary to existing recommendation systems, our platform supports multiple types of interaction data with multiple modalities of metadata natively. This is achieved through multi-modal fusion of various data representations. We deployed the platform into multiple e-commerce stores of different kinds, e.g. food and beverages, shoes, fashion items, telecom operators. Here, we present our system, its flexibility and performance. We also show benchmark results on open datasets, that significantly outperform state-of-the-art prior work.Comment: 7 pages, 8 figure

Toward selective detection of reactive oxygen and nitrogen species with the use of fluorogenic probes – Limitations, progress, and perspectives

International audienceOver the last 40 years, there has been tremendous progress in understanding the biological reactions ofreactive oxygen species (ROS) and reactive nitrogen species (RNS). It is widely accepted that the generationof ROS and RNS is involved in physiological and pathophysiological processes. To understand the role of ROSand RNS in a variety of pathologies, the specific detection of ROS and RNS is fundamental. Unfortunately,the intracellular detection and quantitation of ROS and RNS remains a challenge. In this short review, wehave focused on the mechanistic and quantitative aspects of their detection with the use of selectedfluorogenic probes. The challenges, limitations and perspectives of these methods are discussed

Antithrombotic Effects of Pyridinium Compounds Formed from Trigonelline upon Coffee Roasting

Coffee may exert a preventive effect on arterial thrombosis. Trigonelline is one of the most abundant compounds in coffee that undergoes pyrolysis upon roasting of coffee beans. The aim of the present study was to identify pyridinium compounds formed upon trigonelline pyrolysis and coffee roasting and to investigate the effect of three of them, i.e., 1-methylpyridine and 1,3- and 1,4-dimethylpyridine, on experimentally induced arterial thrombosis in rats. 1,3- and 1,4-dimethylpyridine but not 1-methylpyridine inhibited arterial thrombus formation. 1,3-Dimethylpyridine inhibited platelet aggregation and reduced fibrin formation in platelet-rich plasma, whereas 1,4-dimethylpyridine increased the plasma level of 6-keto-PGF_1α. 1,4-Dimethylpyridine slightly increased rat tissue plasminogen activator plasma activity. In summary, we demonstrated that pyridinium compounds display mild antithrombotic properties due to stimulation by prostacyclin release (1,4-dimethylpyridine) and inhibition of platelet aggregation (1,3-dimethylpyridine). Those pyridinium compounds may, to some extent, be responsible for the beneficial effects of coffee drinking

Dataset of B-mode fatty liver ultrasound images

<p>The dataset used and described in: M. Byra, G. Styczynski, C. Szmigielski, P. Kalinowski. Ł. Michałowski4. R. Paluszkiewicz. B. Ziarkiewicz-Wróblewska, K. Zieniewicz. P. Sobieraj, A. Nowicki. Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images. International Journal of Computer Assisted Radiology and Surgery, 2018. DOI: 10.1007/s11548-018-1843-2. </p> <p>Please refer to the above work if you use the dataset in your research. </p> <p>Contact:<br> Michal Byra<br> Department of Ultrasound<br> Institute of Fundamental Technological Research<br> Polish Academy of Sciences, Warsaw, Poland<br> [email protected]<br> [email protected]</p