  To address the problem of catastrophic forgetting due to the invisibility of
old categories in sequential input, existing work based on relatively simple
categorization tasks has made some progress. In contrast, video captioning is a
more complex task in multimodal scenario, which has not been explored in the
field of incremental learning. After identifying this stability-plasticity
problem when analyzing video with sequential input, we originally propose a
method to Mitigate Catastrophic Forgetting in class-incremental learning for
multimodal Video Captioning (MCF-VC). As for effectively maintaining good
performance on old tasks at the macro level, we design Fine-grained Sensitivity
Selection (FgSS) based on the Mask of Linear's Parameters and Fisher
Sensitivity to pick useful knowledge from old tasks. Further, in order to
better constrain the knowledge characteristics of old and new tasks at the
specific feature level, we have created the Two-stage Knowledge Distillation
(TsKD), which is able to learn the new task well while weighing the old task.
Specifically, we design two distillation losses, which constrain the cross
modal semantic information of semantic attention feature map and the textual
information of the final outputs respectively, so that the inter-model and
intra-model stylized knowledge of the old class is retained while learning the
new class. In order to illustrate the ability of our model to resist
forgetting, we designed a metric CIDER_t to detect the stage forgetting rate.
Our experiments on the public dataset MSR-VTT show that the proposed method
significantly resists the forgetting of previous tasks without replaying old
samples, and performs well on the new task.
