Blackthorn: Large-Scale Interactive Multimodal Learning

Abstract

This paper presents Blackthorn, an efficient interactive multimodal learning approach facilitating analysis of multimedia collections of up to 100 million items on a single high-end workstation. Blackthorn features efficient data compression, feature selection, and optimizations to the interactive learning process. The Ratio-64 data representation introduced in this paper only costs tens of bytes per item yet preserves most of the visual and textual semantic information with good accuracy. The optimized interactive learning model scores the Ratio-64-compressed data directly, greatly reducing the computational requirements. The experiments compare Blackthorn with two baselines: Conventional relevance feedback, and relevance feedback using product quantization to compress the features. The results show that Blackthorn is up to 77.5 $\times$ faster than the conventional relevance feedback alternative, while outperforming the baseline with respect to the relevance of results: It vastly outperforms the baseline on recall over time and reaches up to 108% of its precision. Compared to the product quantization variant, Blackthorn is just as fast, while producing more relevant results. On the full YFCC100M dataset, Blackthorn performs one complete interaction round in roughly 1 s while maintaining adequate relevance of results, thus opening multimedia collections comprising up to 100 million items to fully interactive learning-based analysis.