project
X-Fusion - A multimodal fusion framework developed by the University of California in collaboration with Adobe and other institutions.
X-Fusion is a multimodal fusion framework jointly proposed by UCLA, UC Wisconsin-Madison, and Adobe Research. It extends pre-trained large language models (LLMs) to multimodal tasks...
What is X-Fusion?
X-Fusion is a multimodal fusion framework jointly proposed by UCLA, UC Wisconsin-Madison, and Adobe Research. It extends pre-trained large language models (LLMs) to multimodal tasks while preserving their language capabilities. The framework employs a dual-tower architecture, freezing the parameters of the language model while introducing independent weights for the visual modality to process visual information. It aligns textual and visual features at the input and output levels, and at the intermediate processing level, achieving efficient multimodal fusion.
Main functions of X-Fusion
- Multimodal task extensionIt can handle various multimodal tasks such as image-to-text (e.g., image description) and text-to-image (e.g., image generation).
- Performance optimizationBy reducing noise in image data, overall performance can be improved; at the same time, understanding the data of the task can significantly improve the quality of the generated task.
- Multi-task trainingIt supports simultaneous training of multiple visual language tasks (such as image editing, localization, visual question answering, etc.) without the need to create specific weights for each task.
- Pre-trained model transferThe capabilities of pre-trained diffusion models can be transferred to the visual tower to further enhance image generation capabilities.
X-Fusion's technical principles
- Dual-tower architecture designX-Fusion employs a dual-tower architecture, freezing the parameters of the language model while introducing independent weights for the visual modality. This allows the model to process language and visual information separately when handling multimodal tasks, aligning features of both modalities at an intermediate level to achieve efficient multimodal understanding and generation.
- Modality-specific weightsIn the dual-tower architecture, the language tower and the vision tower handle text and visual input respectively. The language tower retains its pre-trained parameters, while the vision tower introduces new weights to process visual information. This separate processing approach ensures the preservation of language capabilities while enhancing visual understanding.
- Feature alignment and fusionX-Fusion aligns and fuses features at the input, intermediate processing, and output levels. Through this multi-layered alignment, the model can better integrate linguistic and visual information, improving performance on multimodal tasks.
- Training strategy optimizationX-Fusion investigated the impact of noise level and data ratio on performance during training. Experiments showed that reducing image data noise can significantly improve overall performance, and understanding the data for the task also has a positive impact on the generation task.
X-Fusion's project address
- Project official website:https://sichengmo.github.io/XFusion/
- arXiv technical paper:https://arxiv.org/pdf/2504.20996
Application scenarios of X-Fusion
- autonomous drivingBy integrating data from multiple sensors such as cameras and radar, X-Fusion can provide more comprehensive environmental perception capabilities, improving the safety and reliability of autonomous driving.
- Robot NavigationIt helps robots perform precise positioning and path planning in complex environments, enhancing their autonomous navigation capabilities.
- Human-computer interactionBy combining multimodal inputs such as voice, gestures, and facial expressions, X-Fusion enables more natural and intelligent human-computer interaction. For example, in smart home scenarios, voice assistants can use visual data to recognize users' gestures and expressions, providing more accurate services.
- Sentiment AnalysisIn sentiment analysis, X-Fusion can combine voice and visual data to more accurately identify a user's emotional state.
- Medical image analysisBy fusing different modalities of medical images (such as MRI and CT), X-Fusion can help doctors gain a more comprehensive understanding of a patient's condition, improving the accuracy of disease diagnosis and the ability to detect diseases early.