In visual search, the gallery set could be incrementally growing and added to
the database in practice. However, existing methods rely on the model trained
on the entire dataset, ignoring the continual updating of the model. Besides,
as the model updates, the new model must re-extract features for the entire
gallery set to maintain compatible feature space, imposing a high computational
cost for a large gallery set. To address the issues of long-term visual search,
we introduce a continual learning (CL) approach that can handle the
incrementally growing gallery set with backward embedding consistency. We
enforce the losses of inter-session data coherence, neighbor-session model
coherence, and intra-session discrimination to conduct a continual learner. In
addition to the disjoint setup, our CL solution also tackles the situation of
increasingly adding new classes for the blurry boundary without assuming all
categories known in the beginning and during model update. To our knowledge,
this is the first CL method both tackling the issue of backward-consistent
feature embedding and allowing novel classes to occur in the new sessions.
Extensive experiments on various benchmarks show the efficacy of our approach
under a wide range of setups.

本文介绍了一种基于 continual learning 方法的视觉搜索模型，它可以不断更新模型以处理增量式的图库集，同时不会影响模型的特征空间并且可以适用于新的图像类别。