X-Prompt: Towards Universal In-Context Image Generation in Auto-Regressive Vision Language Foundation Models
Zeyi Sun, Ziyang Chu, Pan Zhang, Tong Wu, Xiaoyi Dong, Yuhang Zang, Yuanjun Xiong, Dahua Lin, Jiaqi Wang·December 02, 2024
Summary
X-Prompt is a large-vision language model for universal in-context image generation, excelling in unseen task handling. It supports longer token sequences, unified text and image prediction, and efficient feature compression. Extensive experiments validate its performance across diverse tasks, demonstrating strong generalization capabilities. The model addresses multi-modal generation challenges by compressing examples into fixed-length tokens, enhancing reasoning on new images and improving generalizability. Chameleon, a combined text and image generation model, uses task augmentation and reverse augmentation to improve performance and generalizability. RAIE, a retrieval-augmented image editing method, retrieves relevant examples to guide model performance. The model is trained on a large dataset, incorporating in-context examples for tasks, and achieves competitive results in dense prediction and low-level vision tasks. X-Prompt showcases versatility in arbitrary multi-modal tasks through unified text and image next-token prediction loss.
Introduction
Background
Overview of X-Prompt's development and its significance in the field of language models
Objective
Highlighting X-Prompt's capabilities in handling unseen tasks, its support for longer token sequences, and unified text and image prediction
Method
Data Collection
Description of the dataset used for training and testing X-Prompt
Data Preprocessing
Techniques employed for preparing the data to enhance model performance
Model Architecture
Detailed explanation of X-Prompt's architecture, focusing on its ability to compress examples into fixed-length tokens for improved reasoning and generalizability
X-Prompt's Performance
Multi-Modal Generation Challenges
Explanation of how X-Prompt addresses the challenges in multi-modal generation through efficient feature compression
Generalization Capabilities
Presentation of extensive experiments validating X-Prompt's strong generalization across diverse tasks
Task Augmentation and Reverse Augmentation
Description of Chameleon, a model that leverages task augmentation and reverse augmentation to enhance performance and generalizability
Retrieval-Augmented Image Editing
Overview of RAIE, a method that uses retrieval of relevant examples to guide model performance in image editing tasks
Training and Evaluation
Dataset Utilization
Explanation of how X-Prompt is trained on a large dataset, incorporating in-context examples for various tasks
Dense Prediction and Low-Level Vision Tasks
Demonstration of X-Prompt's competitive results in dense prediction and low-level vision tasks, showcasing its versatility in arbitrary multi-modal tasks
Conclusion
Summary of X-Prompt's Contributions
Recap of X-Prompt's capabilities, including its unified text and image next-token prediction loss
Future Directions
Discussion on potential areas for further research and development of X-Prompt
Basic info
papers
computer vision and pattern recognition
multimedia
machine learning
artificial intelligence
Advanced features