Deep Learning Approach for Cosmetic Product Detection and Classification

As the amount of online video content is increasing,consumers are becoming increasingly interested in various product names appearing in videos,particularly in cosmetic-product names in videos related to fashion,beauty,and style.Thus,the identification of such products by using image recognition technology may aid in the identification of current commercial trends.In this paper,we propose a two-stage deep-learning detection and classification method for cosmetic products.Specifically,variants of the YOLO network are used for detection,where the bounding box for each given input product is predicted and subsequently cropped for classification.We use four state-of-the-art classification networks,namely ResNet,InceptionResNetV2,DenseNet,and EfficientNet,and compare their performance.Furthermore,we employ dilated convolution in these networks to obtain better feature representations and improve performance.Extensive experiments demonstrate that YOLOv3 and its tiny version achieve higher speed and accuracy.Moreover,the dilated networks marginally outperform the base models,or achieve similar performance in the worst case.We conclude that the proposed method can effectively detect and classify cosmetic products.

New Thinking of Traditional Industry Development under the New Economic Condition——Classification of the Industry by the Management Mode of Nike

This paper has announced the arrival of new economic era through an analysis of Nike＇s management mode. The traditional industry classification can＇t meet demands of industry development. We should inherit and improve traditional economy in order to adapt to the development demand of new economy.

How Media Opinion Influences Imports in the US

Does public opinion influence US imports?Do countries with a good reputation export more to the US?And vice versa?Based on an extended trade gravity model,this paper employs news data from the GDELT database and US monthly import data to create an indicator of the influence of public opinion to examine the effects of US domestic public opinion on imports.Our research findings suggest that:(i)US public opinion influences US imports.Specifically,(ii)when public opinion turned negative during 2013-2017,it exerted a significantly negative effect on US imports;when public opinion was favorable during 2008-2012,it exerted an insignificantly positive effect on US imports.(iii)According to the pulse response function and variance decomposition,negative public opinion exerted a more significant and more lasting effect on US imports compared with positive public opinion.(iv)It can be discovered after further decomposing such effects on product categories that significant product heterogeneity exists in the public opinion effects on US imports:Complex and differentiated products are more influenced by negative public opinion while homogeneous and intermediate products are more influenced by positive public opinion.

A dynamic two-zone model of continuous fluidized bed layering granulation with internal product classification

A dynamic two-zone model is proposed to address the formation of granulation and drying zones in fluidized bed layering granulation processes with internal product classification. The model assumes a constant volume for the granulation zone, but a variable overall volume for the fluidized bed to account for classified product removal. The model is used to study the effect of various process parameters on dynamics and process stability. Stability is shown to depend on the separation diameter of product removal and the flow rate of the injected liquid. A lower and upper range of separation diameters with stable process behavior are found. In an intermediate range instability in the form of self-sustained oscillations is observed. The lower stability boundary is in qualitative agreement with recent experimental observations （Schmidt, Bück, ＆ Tsotsas, 2015）; the upper boundary was reported in a theoretical paper by Vreman, Van Lare, and Hounslow （2009） based on a single zone model.

Automatic Product Image Classification with Multiple Support Vector Machine Classifiers

For the task of visual-based automatic product image classification for e-commerce,this paper constructs a set of support vector machine(SVM) classifiers with different model representations.Each base SVM classifier is trained with either different types of features or different spatial levels.The probability outputs of these SVM classifiers are concatenated into feature vectors for training another SVM classifier with a Gaussian radial basis function(RBF) kernel.This scheme achieves state-of-the-art average accuracy of 86.9%for product image classification on the public product dataset PI 100.