mlsanityData to 7 Oct 2026

VL-BERT: Pre-training of Generic Visual-Linguistic Representations

arXiv 1908.08530 ↗First posted 22 Aug 2019cs.CV cs.CL cs.LGcited by 954

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Cited by 176 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce a new pre-trainable generic representation for visual-linguistic tasks, called Visual-Linguistic BERT (VL-BERT for short). VL-BERT adopts the simple yet powerful Transformer model as the backbone, and extends it to take both visual and linguistic embedded features as input. In it, each element of the input is either of a word from the input sentence, or a region-of-interest (RoI) from the input image. It is designed to fit for most of the visual-linguistic downstream tasks. To better exploit the generic representation, we pre-train VL-BERT on the massive-scale Conceptual Captions dataset, together with text-only corpus. Extensive empirical analysis demonstrates that the pre-training procedure can better align the visual-linguistic clues and benefit the downstream tasks, such as visual commonsense reasoning, visual question answering and referring expression comprehension. It is worth noting that VL-BERT achieved the first place of single model on the leaderboard of the VCR benchmark. Code is released at urlhttps://github.com/jackroos/VL-BERT.

Key facts. Cited by 954 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Aug 2019.

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Citing papers per year: 2021: 248; 2022: 273; 2023: 246; 2024: 119; 2025: 55; 2026: 13.

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