

4M
Overview :
4M is a framework for training multi-modal and multi-task models capable of handling various visual tasks and performing multi-modal conditional generation. The model demonstrates its generalizability and scalability through experimental analysis, laying the foundation for further exploration of multi-modal learning in vision and other domains.
Target Users :
The 4M model is targeted towards researchers and developers in the computer vision and machine learning domains, especially those interested in multi-modal data processing and generative models. This technology has applications in image and video analysis, content creation, data augmentation, and multi-modal interaction scenarios.
Use Cases
Use the 4M model to generate a depth map and surface normal from an RGB image.
Use 4M for image editing, such as reconstructing a complete RGB image based on partial input.
In multi-modal retrieval, use the 4M model to retrieve corresponding images based on text descriptions.
Features
Multi-modal and multi-task training paradigm, capable of predicting or generating any modality.
Transforms modalities into discrete token sequences for training on a unified Transformer encoder-decoder.
Supports prediction from partial inputs, enabling chained multi-modal generation.
Can generate any modality based on other modalities in any subset, achieving self-consistent prediction.
Supports fine-grained multi-modal generation and editing tasks, such as semantic segmentation or depth map generation.
Performs controllable multi-modal generation by weighting different conditions to control the output.
Supports multi-modal retrieval by predicting global embeddings of DINOv2 and ImageBind models.
How to Use
Visit the 4M GitHub repository to access the code and pre-trained models.
Install the required dependencies and environment according to the documentation.
Download and load the pre-trained 4M model.
Prepare input data, which can be text, image, or other modalities.
Choose the desired generation task or retrieval task.
Run the model and observe the results, adjusting parameters as needed.
Post-process the generated output, such as converting generated tokens back to images or other modalities.
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