A Baseline for Visual Instance Retrieval with Deep Convolutional Networks
Ali Sharif Razavian, Josephine Sullivan, Atsuto Maki, Stefan Carlsson · arXiv (Cornell University) · 2014
This paper presents a simple pipeline for visual instance retrieval exploiting image representations based on convolutional networks (ConvNets), and demonstrates that ConvNet image representations outperform other state-of-the-art image representations on six standard image retrieval datasets for the first time. Unlike existing design choices, our image representation does not require fine-tuning or learning with data similar to the test set. Furthermore, we consider the challenge Can you construct a tiny image representation with memory requirements less than or equal to 32 bytes that can successfully perform retrieval? We report the promising performance of our tiny ConvNet based representation.