Enhanced MultiMAE introduces the integration of prompt tuning into the Multi-modal Multi-task Masked Autoencoders (MultiMAE) framework for more efficient and adaptable multi-modal, multi-task learning, particularly focusing on vision tasks such as depth estimation and semantic segmentation.
- Introduction
- Installation
- Usage
- Features
- Data
- Methodology
- Experiments and Results
- Future Research Directions
- Acknowledgements
- License
- Citations
Building upon the original MultiMAE, this version extends its capabilities by incorporating prompt tuning to efficiently apply pre-trained models to downstream tasks. This approach enables effective parameter tuning without the need for extensive re-training, particularly addressing the challenges of applying traditional natural language processing techniques to vision tasks.
Refer to the original MultiMAE setup instructions in SETUP.md for basic environment setup.
Usage instructions remain consistent with the original MultiMAE documentation. See USAGE.md for comprehensive guidelines on pre-training, fine-tuning, and utilizing the models.
- Multi-modal, Multi-task Learning: Leverage pre-trained models across different tasks including depth estimation and semantic segmentation.
- Prompt Tuning Integration: Introduces the ability to fine-tune models for specific tasks with minimal parameter adjustments.
- Efficient Learning: Reduces the need for extensive computational resources, enabling faster adaptation to new tasks.
Utilizes the NYU Depth Dataset V2 (NYUv2), a benchmark for segmentation and depth estimation in various indoor environments.
Follows the MultiMAE's approach, pre-training on the ImageNet dataset with masked autoencoder models to learn rich feature representations.
Includes task-specific fine-tuning for depth estimation and semantic segmentation, employing task-specific decoders and evaluation metrics.
Introduces deep prompt tuning and prompt pool strategies to adapt the model to various tasks with minimal adjustments, significantly reducing the number of parameters needed for effective learning.
Conducted experiments demonstrate the effectiveness of prompt tuning in multi-task settings, achieving competitive performance with reduced model complexity and training time.
Suggests further exploration into task-specific prompt pooling and efficient normalization techniques to enhance model adaptability and performance across diverse tasks.
Credits to the original MultiMAE project and its contributors. This enhancement also builds upon foundational works in prompt tuning and multi-task learning in the vision domain.
This project is licensed under the same terms as the original MultiMAE project. See LICENSE for more details.
Refer to the original MultiMAE citation and additional references related to prompt tuning and its application to vision tasks as documented in the research document.