4M

Preview：

Introduce：

4M is a framework for training multimodal and multitask models, capable of handling multiple visual tasks, and capable of multimodal condition generation. The model shows its universality and extensibility in visual tasks through experimental analysis, which lays a foundation for further exploration of multi-modal learning in vision and other fields.

Stakeholders:
The target audience for 4M models is researchers and developers in the field of computer vision and machine learning, especially those professionals interested in multimodal data processing and generation models. The technology can be applied to scenarios such as image and video analysis, content creation, data enhancement, and multimodal interaction.
Usage Scenario Examples:

• The 4M model is used to generate depth maps and surface normals from RGB images.
• Use 4M for image editing, such as reconstructing a complete RGB image from partial inputs.
• In multimodal retrieval, 4M model is used to retrieve the corresponding image according to the text description.

The features of the tool:

• Multimodal and multitask training solutions that predict or generate any mode.
• By converting the modes into discrete tag sequences, you can train on a unified Transformer encoder-decoder.
• Support prediction from partial input to achieve multi-modal chain generation.
• The ability to generate any mode based on any subset of other modes for self-consistent predictions.
• Supports fine-grained multimodal generation and editing tasks such as semantic segmentation or depth maps.
• It can be controlled by multi-mode generation, and the output can be generated by weight control of different conditions.
• Supports multi-modal retrieval by predicting global embeddings of DINOv2 and ImageBind models.

Steps for Use:

• Visit 4M’s GitHub repository for code and pre-trained models.
• Follow the documentation to install the required dependencies and environments.
• Download and load the pre-trained 4M model.
• Prepare for input data, which can be text, images, or other modes.
• Select the build task or retrieve task as required.
• Run the model and observe the results, adjusting the parameters as needed.
• Post-processing of the generated output, such as converting the generated markup back to an image or other mode.

Tool’s Tabs: Multimodal learning,Transformer model