Papers › VisualBERT: A Simple and Performant Baseline for Vision and Language

VisualBERT: A Simple and Performant Baseline for Vision and Language

9 Aug 2019arXiv:1908.03557archive 2025-07-28

Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang

We propose VisualBERT, a simple and flexible framework for modeling a broad range of vision-and-language tasks. VisualBERT consists of a stack of Transformer layers that implicitly align elements of an input text and regions in an associated input image with self-attention. We further propose two visually-grounded language model objectives for pre-training VisualBERT on image caption data. Experiments on four vision-and-language tasks including VQA, VCR, NLVR2, and Flickr30K show that VisualBERT outperforms or rivals with state-of-the-art models while being significantly simpler. Further analysis demonstrates that VisualBERT can ground elements of language to image regions without any explicit supervision and is even sensitive to syntactic relationships, tracking, for example, associations between verbs and image regions corresponding to their arguments.

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Code

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chenkangyang/paddle_visual_bert mentioned on GitHubpaddle report
gchhablani/multilingual-vqa mentioned on GitHubjax report
lalithjets/surgical_vqa mentioned on GitHubpytorch report
longbai1006/surgical-vqla mentioned on GitHubpytorchMIT report
social-ai-studio/matk mentioned on GitHubpytorch report
uclanlp/visualbert mentioned on GitHubpytorch report
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CLIPVisionBertConfig gchhablani/multilingual-vqa/models/flax_clip_vision_bert/modeling_clip_vision_bert.py community (archive-listed) ran MIT (permissive) · 74b2fcf39fdad75a · report
FlaxCLIPVisionBertModule gchhablani/multilingual-vqa/models/flax_clip_vision_bert/modeling_clip_vision_bert.py community (archive-listed) ran MIT (permissive) · eed1fcab3a96812d · report
perform_truncate chenkangyang/paddle_visual_bert/run_pretrain.py community (archive-listed) ran fingerprinted no licence file found · pointer only · cd39cffdc7b1f9c3 · report
random_word chenkangyang/paddle_visual_bert/run_pretrain.py community (archive-listed) ran no licence file found · pointer only · 1d3d00a8a5932be0 · report
BertEncoder YIKUAN8/Transformers-VQA/src/modeling.py community (archive-listed) unverified no licence file found · pointer only · 59f36be33253afad · report
FlaxCLIPVisionBertEmbeddings gchhablani/multilingual-vqa/models/flax_clip_vision_bert/modeling_clip_vision_bert.py community (archive-listed) unverified MIT (permissive) · bc82bcf6d781550f · report
VBBertModel YIKUAN8/Transformers-VQA/src/modeling.py community (archive-listed) unverified no licence file found · pointer only · 2e705a7d909038d1 · report
VisualBertEmbeddings YIKUAN8/Transformers-VQA/src/modeling.py community (archive-listed) unverified no licence file found · pointer only · 7c7933295057c6b3 · report
prepare_train_features_single chenkangyang/paddle_visual_bert/run_pretrain.py community (archive-listed) unverified no licence file found · pointer only · 49436f86e6503e90 · report

Tasks

Language ModelingLanguage ModellingVisual Question Answering (VQA)Visual Reasoning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Phrase Grounding Flickr30k Entities Dev VisualBERT R@1 70.4 #3 of 3 Archive leaderboard report
Phrase Grounding Flickr30k Entities Dev VisualBERT R@10 86.31 #3 of 3 Archive leaderboard report
Phrase Grounding Flickr30k Entities Dev VisualBERT R@5 84.49 #3 of 3 Archive leaderboard report
Phrase Grounding Flickr30k Entities Test VisualBERT R@1 71.33 #10 of 18 Archive leaderboard report
Phrase Grounding Flickr30k Entities Test VisualBERT R@10 86.51 #10 of 18 Archive leaderboard report
Phrase Grounding Flickr30k Entities Test VisualBERT R@5 84.98 #10 of 18 Archive leaderboard report
Visual Question Answering (VQA) VCR (Q-A) dev VisualBERT Accuracy 70.8 #3 of 3 Archive leaderboard report
Visual Question Answering (VQA) VCR (Q-A) test VisualBERT Accuracy 71.6 #9 of 11 Archive leaderboard report
Visual Question Answering (VQA) VCR (Q-AR) dev VisualBERT Accuracy 52.2 #3 of 3 Archive leaderboard report
Visual Question Answering (VQA) VCR (Q-AR) test VisualBERT Accuracy 52.4 #7 of 7 Archive leaderboard report
Visual Question Answering (VQA) VCR (QA-R) dev VisualBERT Accuracy 73.2 #3 of 3 Archive leaderboard report
Visual Question Answering (VQA) VCR (QA-R) test VisualBERT Accuracy 73.2 #8 of 8 Archive leaderboard report
Visual Question Answering (VQA) VQA v2 test-dev VisualBERT Accuracy 70.8 #28 of 56 Archive leaderboard report
Visual Question Answering (VQA) VQA v2 test-std VisualBERT overall 71 #24 of 38 Archive leaderboard report
Visual Reasoning NLVR VisualBERT Accuracy (Dev) 67.4% #1 of 1 Archive leaderboard report
Visual Reasoning NLVR VisualBERT Accuracy (Test-P) 67% #1 of 1 Archive leaderboard report
Visual Reasoning NLVR VisualBERT Accuracy (Test-U) 67.3% #1 of 1 Archive leaderboard report
Visual Reasoning NLVR2 Dev VisualBERT Accuracy 66.7 #15 of 15 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: VisualBERT

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerReLUResidual ConnectionSoftmaxTransformerVisualBERT

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