CVFeb 26, 2020
Graphcore C2 Card performance for image-based deep learning application: A ReportIlyes Kacher, Maxime Portaz, Hicham Randrianarivo et al.
Recently, Graphcore has introduced an IPU Processor for accelerating machine learning applications. The architecture of the processor has been designed to achieve state of the art performance on current machine intelligence models for both training and inference. In this paper, we report on a benchmark in which we have evaluated the performance of IPU processors on deep neural networks for inference. We focus on deep vision models such as ResNeXt. We report the observed latency, throughput and energy efficiency.
CVMar 27, 2019
Image search using multilingual texts: a cross-modal learning approach between image and textMaxime Portaz, Hicham Randrianarivo, Adrien Nivaggioli et al.
Multilingual (or cross-lingual) embeddings represent several languages in a unique vector space. Using a common embedding space enables for a shared semantic between words from different languages. In this paper, we propose to embed images and texts into a unique distributional vector space, enabling to search images by using text queries expressing information needs related to the (visual) content of images, as well as using image similarity. Our framework forces the representation of an image to be similar to the representation of the text that describes it. Moreover, by using multilingual embeddings we ensure that words from two different languages have close descriptors and thus are attached to similar images. We provide experimental evidence of the efficiency of our approach by experimenting it on two datasets: Common Objects in COntext (COCO) [19] and Multi30K [7].
IRDec 1, 2015
Efficient filtering of adult content using textual informationThomas Largillier, Guillaume Peyronnet, Sylvain Peyronnet
Nowadays adult content represents a non negligible proportion of the Web content. It is of the utmost importance to protect children from this content. Search engines, as an entry point for Web navigation are ideally placed to deal with this issue. In this paper, we propose a method that builds a safe index i.e. adult-content free for search engines. This method is based on a filter that uses only textual information from the web page and the associated URL.