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Generalization to unseen tasks is an important ability for few-shot learnersto achieve better zero-/few-shot performance on diverse tasks. However, suchgeneralization to vision-language tasks including grounding and generationtasks has been under-explored; existing few-shot VL models struggle to handletasks that involve object grounding and multiple images such as visualcommonsense reasoning or NLVR2. In this paper, we introduce GRILL, GRoundedvIsion Language aLigning, a novel VL model that can be generalized to diversetasks including visual question answering, captioning, and grounding tasks withno or very few training instances. Specifically, GRILL learns object groundingand localization by exploiting object-text alignments, which enables it totransfer to grounding tasks in a zero-/few-shot fashion. We evaluate our modelon various zero-/few-shot VL tasks and show that it consistently surpasses thestate-of-the-art few-shot methods.

GRILL: Grounded Vision-language Pre-training via Aligning Text and Image Regions