  Current state-of-the-art vision-and-language models are evaluated on tasks
either individually or in a multi-task setting, overlooking the challenges of
continually learning (CL) tasks as they arrive. Existing CL benchmarks have
facilitated research on task adaptation and mitigating "catastrophic
forgetting", but are limited to vision-only and language-only tasks. We present
CLiMB, a benchmark to study the challenge of learning multimodal tasks in a CL
setting, and to systematically evaluate how upstream continual learning can
rapidly generalize to new multimodal and unimodal tasks. CLiMB includes
implementations of several CL algorithms and a modified Vision-Language
Transformer (ViLT) model that can be deployed on both multimodal and unimodal
tasks. We find that common CL methods can help mitigate forgetting during
multimodal task learning, but do not enable cross-task knowledge transfer. We
envision that CLiMB will facilitate research on a new class of CL algorithms
for this challenging multimodal setting.
