CTP: Towards Vision-Language Continual Pretraining via Compatible
  Momentum Contrast and Topology Preservation
Hongguang Zhu Yunchao Wei Xiaodan Liang Chunjie Zhang Yao Zhao
Vision-Language Pretraining (VLP) has shown impressive results on diverse
downstream tasks by offline training on large-scale datasets. Regarding the
growing nature of real-world data, such an offline training paradigm on
ever-expanding data is unsustainable, because models lack the continual
learning ability to accumulate knowledge constantly. However, most continual
learning studies are limited to uni-modal classification and existing
multi-modal datasets cannot simulate continual non-stationary data stream
scenarios. To support the study of Vision-Language Continual Pretraining
(VLCP), we first contribute a comprehensive and unified benchmark dataset P9D
which contains over one million product image-text pairs from 9 industries. The
data from each industry as an independent task supports continual learning and
conforms to the real-world long-tail nature to simulate pretraining on web
data. We comprehensively study the characteristics and challenges of VLCP, and
propose a new algorithm: Compatible momentum contrast with Topology
Preservation, dubbed CTP. The compatible momentum model absorbs the knowledge
of the current and previous-task models to flexibly update the modal feature.
Moreover, Topology Preservation transfers the knowledge of embedding across
tasks while preserving the flexibility of feature adjustment. The experimental
results demonstrate our method not only achieves superior performance compared
with other baselines but also does not bring an expensive training burden.
Dataset and codes are available at https://github.com/KevinLight831/CTP.