Abstract
Cross-modal hashing (CMH) models are introduced to significantly reduce the cost of large-scale
cross-modal data retrieval systems. In many realworld applications, however, data of new categories
arrive continuously, which requires the model has
good extensibility. That is the model should be updated to accommodate data of new categories but
still retain good performance for the old categories
with minimum computation cost. Unfortunately,
existing CMH methods fail to satisfy the extensibility requirements. In this work, we propose a novel
extensible cross-modal hashing (ECMH) to enable
highly efficient and low-cost model extension. Our
proposed ECMH has several desired features: 1) it
has good forward compatibility, so there is no need
to update old hash codes; 2) the ECMH model is
extended to support new data categories using only
new data by a well-designed “weak constraint incremental learning” algorithm, which saves up to
91% time cost comparing with retraining the model
with both new and old data; 3) the extended model
achieves high precision and recall on both old and
new tasks. Our extensive experiments show the effectiveness of our design