VLP: A Survey on Vision-language Pre-training

By Feilong Chen, Duzhen Zhang, Xiuyi Chen, Jing Shi, Shuang Xu, Bo Xu. Machine Intelligence Research, 2022

352 citations recorded by the source corpus.

In the past few years, the emergence of pre-training models has brought uni-modal fields such as computer vision (CV) and natural language processing (NLP) to a new era. Substantial works have shown that they are beneficial for downstream uni-modal tasks and avoid training a new model from scratch. So can such pre-trained models be applied to multi-modal tasks? Researchers have explored this problem and made significant progress. This paper surveys recent advances and new frontiers in vision-language pre-training (VLP), including image-text and video-text pre-training. To give readers a better overall grasp of VLP, we first review its recent advances in five aspects: feature extraction, model architecture, pre-training objectives, pre-training datasets, and downstream tasks. Then, we summarize the specific VLP models in detail. Finally, we discuss the new frontiers in VLP. To the best of our knowledge, this is the first survey focused on VLP. We hope that this survey can shed light on future research in the VLP field.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/24ed74ed29c057cba8b52fff4edd2c0d7f408716

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Citations in the corpus

Multimodal Learning With Transformers: A Survey (2022, background): “knowledge, only a few surveys on video-language pretraining (VLP) [57], [58], [59] are relevant to MML.”
Vision-Language Pre-Training with Triple Contrastive Learning (2022, background): “Inspired by the success of self-supervision in individual modalities, there is a surging interest in self-supervised vision-language pre-training (VLP) [4,14], which is essential for multi-modal tasks such as visual question answering (VQA), image-text retrieval, and visual entailment.”
Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey (2023, methodology): “• Sequence-to-Sequence LM (Seq2seq) is a pre-training objective used in VLP [24], etc.”
A Survey on Vision--Language--Action Models for Embodied AI (2024, neutral): “However, there is a growing need for more versatile multi-task policies, akin to recent trends in large language models (LLMs) and vision-language models (VLMs).”
Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis (2024, neutral): “Advancements in MLLMs. Recent advancements in MLLMs have seen notable progress [7, 19, 22, 59, 66].”
Sigmoid Loss for Language Image Pre-Training (2023, background): “Language-Image pre-training is a very active ﬁeld and surveys [8] rapidly become outdated.”