QIK: A System for Large-Scale Image Retrieval on Everyday Scenes With Common Objects

In this presentation, we propose a system for large-scale image retrieval on everyday scenes with common objects by leveraging advances in deep learning and natural language processing (NLP). Unlike recent state-of-the-art approaches that extract image features from a convolutional neural network (CNN), our system exploits the predictions made by deep neural networks for image understanding tasks. Our system aims to capture the relationships between objects in an everyday scene rather than just the individual objects in the scene. We also present the performance of our system on the Microsoft COCO dataset containing everyday scenes (with common objects) and prove that our system can outperform state-of-the-art techniques in terms of mean average precision for large-scale image retrieval. [Presentation Recording]