Currently, many face forgery detection methods aggregate spatial and frequency features to enhance the generalization ability gain promising performance under cross-dataset scenario. However, these only leverage one level information which limits their expressive ability. To overcome limitations, we propose a multi-scale wavelet transformer framework for detection. Specifically, take full advan...

in this study, the optimization of energy consumption and reduction of greenhouse gas emissions of irrigated lentil production in esfahan province were conducted using data envelopment analysis (dea) method. the used data were collected randomly from 140 lentil producers using a face to face questionnaire method in the cropping season of 2014-2015. based on two dea models of constant returns to...

Empirical studies concerning face recognition suggest that faces may be stored in memory by a few canonical representations. Models of visual perception are based on image representations in cortical area V1 and beyond, which contain many cell layers for feature extractions. Simple, complex and end-stopped cells tuned to different spatial frequencies (scales) and/or orientations provide input f...

With millions of users and billions of photos, web-scale face recognition is a challenging task that demands speed, accuracy, and scalability. Most current approaches do not address and do not scale well to Internet-sized scenarios such as tagging friends or finding celebrities. Focusing on web-scale face identification, we gather an 800,000 face dataset from the Facebook social network that mo...

Due to the prevalence of social media websites, one challenge facing computer vision researchers is to devise methods to process and search for persons of interest among the billions of shared photos on these websites. Facebook revealed in a 2013 white paper that its users have uploaded more than 250 billion photos, and are uploading 350 million new photos each day. Due to this humongous amount...