Logo Detection Using Deep Learning with Pretrained CNN Models

Scalable Deep Learning Logo Detection

Existing logo detection methods usually consider a small number of logo classes and limited images per class with a strong assumption of requiring tedious object bounding box annotations, therefore not scalable to real-world dynamic applications. In this work, we tackle these challenges by exploring the webly data learning principle without the need for exhaustive manual labelling. Specifically...

Vehicle Detection Using Alex Net and Faster R-CNN Deep Learning Models: A Comparative Study

Learning Semantic Prediction using Pretrained Deep Feedforward Networks

The ability to predict future environment states is crucial for anticipative behavior of autonomous agents. Deep learning based methods have proven to solve key perception challenges but currently mainly operate in a non-predictive fashion. We bridge this gap by proposing an approach to transform trained feed-forward networks into predictive ones via a combination of a recurrent predictive modu...

Melanoma detection with a deep learning model

Background: Skin cancer is one of the most common forms of cancer in the world and melanoma is the deadliest type of skin cancer. Both melanoma and melanocytic nevi begin in melanocytes (cells that produce melanin). However, melanocytic nevi are benign whereas melanoma is malignant. This work proposes a deep learning model for classification of these two lesions.    Methods: In this analytic s...

Deep learning for logo recognition

In this paper we propose a method for logo recognition using deep learning. Our recognition pipeline is composed of a logo region proposal followed by a Convolutional Neural Network (CNN) specifically trained for logo classification, even if they are not precisely localized. Experiments are carried out on the FlickrLogos-32 database, and we evaluate the effect on recognition performance of synt...