Boosted Curriculum Multi-View Hashing for Multimedia Retrieval

The multi-view hash method plays a pivotal role in multimedia retrieval, transforming diverse data from multiple perspectives into binary hash codes. While existing methods primarily emphasize complementarity across multiple views, they often face challenges associated with the non-convex nature of deep neural networks, ultimately causing a decrease in generalization ability. To overcome this limitation, we propose a novel curriculum called
Automatic Multiple Loss Curriculum
(AMLC). In AMLC, the deep multi-view hashing network undergoes training with data presented sequentially, progressing from simple to complex samples. This training strategy mirrors the human learning process, commencing with fundamental concepts and progressively advancing to tackle more intricate and abstract ideas. Building upon AMLC, we propose the
Boosted Curriculum Multi-View Hashing
(BCMVH) method. BCMVH facilitates the positioning of the learned model in a more flat region, enhancing its overall generalization capability. Extensive experiments conducted on three public datasets demonstrate that the proposed BCMVH outperforms state-of-the-art methods, achieving a maximum improvement of 3.17% in terms of mean Average Precision.