VLP: A Survey on Vision-Language Pre-training

Abstract

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 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 from 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.

A Survey on Vision-Language Pre-training

References in corpus (25)

• Learning Transferable Visual Models From Natural Language Supervision
• A Survey on Visual Transformer
• Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision
• Align before Fuse: Vision and Language Representation Learning with Momentum Distillation
• ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
• SimVLM: Simple Visual Language Model Pretraining with Weak Supervision
• VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text
• VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts
• Reducing Transformer Depth on Demand with Structured Dropout
• You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection
• Unifying Vision-and-Language Tasks via Text Generation
• Visual Entailment: A Novel Task for Fine-Grained Image Understanding
• ImageBERT: Cross-modal Pre-training with Large-scale Weak-supervised Image-Text Data
• CLIP2Video: Mastering Video-Text Retrieval via Image CLIP
• Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm
• Vision-and-Language Navigation: A Survey of Tasks, Methods, and Future Directions
• Multi-Grained Vision Language Pre-Training: Aligning Texts with Visual Concepts
• Multimodal Few-Shot Learning with Frozen Language Models
• DeVLBert: Learning Deconfounded Visio-Linguistic Representations
• Visual Relationship Detection with Visual-Linguistic Knowledge from Multimodal Representations
• OPT: Omni-Perception Pre-Trainer for Cross-Modal Understanding and Generation
• Heterogeneous Graph Learning for Visual Commonsense Reasoning
• Probing Inter-modality: Visual Parsing with Self-Attention for Vision-Language Pre-training
• Population-coding and Dynamic-neurons improved Spiking Actor Network for Reinforcement Learning
• HiVLP: Hierarchical Vision-Language Pre-Training for Fast Image-Text Retrieval

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