Factors Influencing the Performance of Image Captioning Model: An Evaluation To appear in The 14th International Conference on Advances in Mobile Computing and Multimedia, 2016
Recently, neural network-based methods have shown impressive performances in captioning task. There have been numerous attempts with many proposed architectures to solve this captioning problem. In this paper, we present the evaluation of different alternatives in architecture and optimization algorithms for a neural image captioning model. First, we present the study of a image captioning model that is comprised of two modules -- a convolutional neural network which encodes the input image into a fixed-dimensional feature vector and a recurrent neural network to decode that representation into a sequence of words describing the input image. After that, we consider different alternatives regarding architecture and optimization algorithm to train the model. We conduct a set of experiments on standard benchmark datasets to evaluate different aspects of the captioning system using standard evaluation methods that are utilized in image captioning literatures. Based on the results of those experiments, we propose several suggestions on architecture and optimization algorithm of the image captioning model that is balanced in terms of the performance and the feasibility to be deployed on real-world problems with commodity hardware.
Maritime Images Classification using Deep Convolutional Neural Networks In Proceedings of The 6th International Symposium on Information and Communication Technology, 2015
The ability to identify maritime vessels and their type is an important component of modern maritime safety and security. In this work, we present the application of deep convolutional neural networks to the classification of maritime vessel images. We use the AlexNet deep convolutional neural network as our base model and propose a new model that is twice smaller then the AlexNet. We conduct experiments on different configurations of the model on commodity hardware. We comparatively evaluate and analyse the performance of different configurations the model. We measure the top-1 and top-5 accuracy rates. The contribution of this work is the implementation, tuning and evaluation of automatic image classifier for the specific domain of maritime vessels with deep convolutional neural networks under the constraints imposed by commodity hardware and size of the image collection.