# UNIMO-2: End-to-End Unified Vision-Language Grounded Learning

arXiv: https://arxiv.org/abs/2203.09067 · HTML: https://mlsanity.com/paper/2203.09067/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in VLM benchmarks and visual reasoning, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 37 of its 56 references that are in the map.
- First posted on arXiv 17 Mar 2022.

## Placed in

- Multimodal LLMs / vision-language models / [VLM benchmarks and visual reasoning](https://mlsanity.com/mllm/vlm-benchmarks-and-visual-reasoning/)

## Builds on (most cited first, 30 of 37)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Microsoft COCO Captions: Data Collection and Evaluation Server](https://mlsanity.com/paper/1504.00325/) (2015)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://mlsanity.com/paper/1908.03557/) (2019)
- [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://mlsanity.com/paper/2102.03334/) (2021)
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://mlsanity.com/paper/2004.06165/) (2020)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [Deep Visual-Semantic Alignments for Generating Image Descriptions](https://mlsanity.com/paper/1412.2306/) (2014)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [SimVLM: Simple Visual Language Model Pretraining with Weak Supervision](https://mlsanity.com/paper/2108.10904/) (2021)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [Unicoder-VL: A Universal Encoder for Vision and Language by Cross-modal Pre-training](https://mlsanity.com/paper/1908.06066/) (2019)
- [Large-Scale Adversarial Training for Vision-and-Language Representation Learning](https://mlsanity.com/paper/2006.06195/) (2020)
- [Visual Entailment: A Novel Task for Fine-Grained Image Understanding](https://mlsanity.com/paper/1901.06706/) (2019)
- [UNIMO: Towards Unified-Modal Understanding and Generation via Cross-Modal Contrastive Learning](https://mlsanity.com/paper/2012.15409/) (2020)
- [ERNIE-ViL: Knowledge Enhanced Vision-Language Representations Through Scene Graph](https://mlsanity.com/paper/2006.16934/) (2020)

## Built on by (most cited first, 19 of 19)

- [CREPE: Can Vision-Language Foundation Models Reason Compositionally?](https://mlsanity.com/paper/2212.07796/) (2022)
- [BridgeTower: Building Bridges Between Encoders in Vision-Language Representation Learning](https://mlsanity.com/paper/2206.08657/) (2022)
- [Online Clustered Codebook](https://mlsanity.com/paper/2307.15139/) (2023)
- [Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens](https://mlsanity.com/paper/2303.14865/) (2023)
- [UPainting: Unified Text-to-Image Diffusion Generation with Cross-modal Guidance](https://mlsanity.com/paper/2210.16031/) (2022)
- [Single-Stream Multi-Level Alignment for Vision-Language Pretraining](https://mlsanity.com/paper/2203.14395/) (2022)
- [UNIMO-G: Unified Image Generation through Multimodal Conditional Diffusion](https://mlsanity.com/paper/2401.13388/) (2024)
- [Bridged Semantic Alignment for Zero-shot 3D Medical Image Diagnosis](https://mlsanity.com/paper/2501.03565/) (2025)
- [MGVQ: Could VQ-VAE Beat VAE? A Generalizable Tokenizer with Multi-group Quantization](https://mlsanity.com/paper/2507.07997/) (2025)
- [Masked Autoencoding Does Not Help Natural Language Supervision at Scale](https://mlsanity.com/paper/2301.07836/) (2023)
- [Enhancing Fine-Grained Vision-Language Pretraining with Negative Augmented Samples](https://mlsanity.com/paper/2412.10029/) (2024)
- [Multimodal Representation Learning of Cardiovascular Magnetic Resonance Imaging](https://mlsanity.com/paper/2304.07675/) (2023)
- [A Survey on Image-text Multimodal Models](https://mlsanity.com/paper/2309.15857/) (2023)
- [PiTL: Cross-modal Retrieval with Weakly-supervised Vision-language Pre-training via Prompting](https://mlsanity.com/paper/2307.07341/) (2023)
- [ManagerTower: Aggregating the Insights of Uni-Modal Experts for Vision-Language Representation Learning](https://mlsanity.com/paper/2306.00103/) (2023)
- [UNIMO-3: Multi-granularity Interaction for Vision-Language Representation Learning](https://mlsanity.com/paper/2305.13697/) (2023)
- [Decoupled Hierarchical Distillation for Multimodal Emotion Recognition](https://mlsanity.com/paper/2602.04260/) (2026)
- [Manager: Aggregating Insights from Unimodal Experts in Two-Tower VLMs and MLLMs](https://mlsanity.com/paper/2506.11515/) (2025)
- [CodeBind: Decoupled Representation Learning for Multimodal Alignment with Unified Compositional Codebook](https://mlsanity.com/paper/2605.18257/) (2026)

## Abstract (arXiv)

Vision-Language Pre-training (VLP) has achieved impressive performance on various cross-modal downstream tasks. However, most existing methods can only learn from aligned image-caption data and rely heavily on expensive regional features, which greatly limits their scalability and performance. In this paper, we propose an end-to-end unified-modal pre-training framework, namely UNIMO-2, for joint learning on both aligned image-caption data and unaligned image-only and text-only corpus. We build a unified Transformer model to jointly learn visual representations, textual representations and semantic alignment between images and texts. In particular, we propose to conduct grounded learning on both images and texts via a sharing grounded space, which helps bridge unaligned images and texts, and align the visual and textual semantic spaces on different types of corpora. The experiments show that our grounded learning method can improve textual and visual semantic alignment for improving performance on various cross-modal tasks. Moreover, benefiting from effective joint modeling of different types of corpora, our model also achieves impressive performance on single-modal visual and textual tasks. Our code and models are public at the UNIMO project page https://unimo-ptm.github.io/.