  Multi-task learns multiple tasks, while sharing knowledge and computation
among them. However, it suffers from catastrophic forgetting of previous
knowledge when learned incrementally without access to the old data. Most
existing object detectors are domain-specific and static, while some are
learned incrementally but only within a single domain. Training an object
detector incrementally across various domains has rarely been explored. In this
work, we propose three incremental learning scenarios across various domains
and categories for object detection. To mitigate catastrophic forgetting,
attentive feature distillation is proposed to leverages both bottom-up and
top-down attentions to extract important information for distillation. We then
systematically analyze the proposed distillation method in different scenarios.
We find out that, contrary to common understanding, domain gaps have smaller
negative impact on incremental detection, while category differences are
problematic. For the difficult cases, where the domain gaps and especially
category differences are large, we explore three different exemplar sampling
methods and show the proposed adaptive sampling method is effective to select
diverse and informative samples from entire datasets, to further prevent
forgetting. Experimental results show that we achieve the significant
improvement in three different scenarios across seven object detection
benchmark datasets.
