Paper presented at ECIR 2020
Omar presented our paper on Exquisitor as the new state of the art interactive learning approach for large-scale multimedia collections. The paper was presented at ECIR 2020, which was the 42nd edition of the conference. It was held online for the first time due to the COVID-19 pandemic.
You can watch the talk on YouTube here.
Interactive learning has been suggested as a key method for addressing analytic multimedia tasks arising in several domains. Until recently, however, methods to maintain interactive performance at the scale of today’s media collections have not been addressed. We propose an interactive learning approach that builds on and extends the state of the art in user relevance feedback systems and high-dimensional indexing for multimedia. We report on a detailed experimental study using the ImageNet and YFCC100M collections, containing 14 million and 100 million images respectively. The proposed approach outperforms the relevant state-of-the-art approaches in terms of interactive performance, while improving suggestion relevance in some cases. In particular, even on YFCC100M, our approach requires less than 0.3 seconds per interaction round to generate suggestions, using a single computing core and less than 7GB of main memory.