  Video paragraph captioning (VPC) involves generating detailed narratives for
long videos, utilizing supportive modalities such as speech and event
boundaries. However, the existing models are constrained by the assumption of
constant availability of a single auxiliary modality, which is impractical
given the diversity and unpredictable nature of real-world scenarios. To this
end, we propose a Missing-Resistant framework MR-VPC that effectively harnesses
all available auxiliary inputs and maintains resilience even in the absence of
certain modalities. Under this framework, we propose the Multimodal VPC (MVPC)
architecture integrating video, speech, and event boundary inputs in a unified
manner to process various auxiliary inputs. Moreover, to fortify the model
against incomplete data, we introduce DropAM, a data augmentation strategy that
randomly omits auxiliary inputs, paired with DistillAM, a regularization target
that distills knowledge from teacher models trained on modality-complete data,
enabling efficient learning in modality-deficient environments. Through
exhaustive experimentation on YouCook2 and ActivityNet Captions, MR-VPC has
proven to deliver superior performance on modality-complete and
modality-missing test data. This work highlights the significance of developing
resilient VPC models and paves the way for more adaptive, robust multimodal
video understanding.
