Papers › Emu: Generative Pretraining in Multimodality

Emu: Generative Pretraining in Multimodality

11 Jul 2023arXiv:2307.05222archive 2025-07-28

Quan Sun, Qiying Yu, Yufeng Cui, Fan Zhang, Xiaosong Zhang, Yueze Wang, Hongcheng Gao, Jingjing Liu, Tiejun Huang, Xinlong Wang

We present Emu, a Transformer-based multimodal foundation model, which can seamlessly generate images and texts in multimodal context. This omnivore model can take in any single-modality or multimodal data input indiscriminately (e.g., interleaved image, text and video) through a one-model-for-all autoregressive training process. First, visual signals are encoded into embeddings, and together with text tokens form an interleaved input sequence. Emu is then end-to-end trained with a unified objective of classifying the next text token or regressing the next visual embedding in the multimodal sequence. This versatile multimodality empowers the exploration of diverse pretraining data sources at scale, such as videos with interleaved frames and text, webpages with interleaved images and text, as well as web-scale image-text pairs and video-text pairs. Emu can serve as a generalist multimodal interface for both image-to-text and text-to-image tasks, and supports in-context image and text generation. Across a broad range of zero-shot/few-shot tasks including image captioning, visual question answering, video question answering and text-to-image generation, Emu demonstrates superb performance compared to state-of-the-art large multimodal models. Extended capabilities such as multimodal assistants via instruction tuning are also demonstrated with impressive performance.

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baaivision/emu officialmentioned in papermentioned on GitHubpytorchApache-2.0 report
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SelfTransformer doc-doc/NExT-OE/networks/q_v_transformer.py community (archive-listed) unverified MIT (permissive) · 7d54f53c9fd4f3cf · report
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Tasks

Image CaptioningImage GenerationImage to textQuestion AnsweringTemporal/Casual QAText GenerationText to Image GenerationText-to-Image GenerationVideo Question AnsweringVisual Question AnsweringVisual Question Answering (VQA)

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Temporal/Casual QA NExT-QA Emu(0-shot) WUPS 23.4 #8 of 8 Archive leaderboard report
Visual Question Answering MM-Vet Emu-14B GPT-4 score 36.3±0.3 #146 of 231 Archive leaderboard report
Visual Question Answering MM-Vet Emu-14B Params 14B #146 of 231 Archive leaderboard report
Visual Question Answering MM-Vet (w/o External Tools) Emu-14B GPT-4 score 36.3±0.3 #1 of 1 Archive leaderboard report
Visual Question Answering VQA v2 Emu-I * Accuracy 57.5 #2 of 2 Archive leaderboard report
Visual Question Answering VizWiz Emu-I * Accuracy 38.1 #1 of 1 Archive leaderboard report
Visual Question Answering (VQA) InfiMM-Eval Emu Abductive 36.57 #7 of 14 Archive leaderboard report
Visual Question Answering (VQA) InfiMM-Eval Emu Analogical 18.19 #7 of 14 Archive leaderboard report
Visual Question Answering (VQA) InfiMM-Eval Emu Deductive 28.9 #7 of 14 Archive leaderboard report
Visual Question Answering (VQA) InfiMM-Eval Emu Overall score 28.24 #7 of 14 Archive leaderboard report
Visual Question Answering (VQA) InfiMM-Eval Emu Params 14B #7 of 14 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.

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