Awesome Masked Modeling for Self-supervised Vision Representation and Beyond
Introduction
The Awesome Masked Modeling for Self-supervised Vision Representation and Beyond project is a comprehensive repository dedicated to summarizing and curating methodologies and advancements in Masked Image Modeling (MIM) and other masked modeling approaches used in self-supervised representation learning. It invites contributions from the community to expand its collection of research works related to masked modeling, further enhancing the repository's depth.
This project is a component of an extensive survey aimed at exploring masked modeling methods. It is meticulously organized in chronological order, ensuring that new developments are continually integrated. The latest version of the survey can be accessed through a provided link for those who are keen on staying up-to-date with the current research landscape.
Research Context
Self-supervised learning (SSL) is an innovative approach in machine learning where the model learns to understand data without relying on human-labeled outputs. Historically, SSL research can be split into two main paradigms: Generative and Discriminative. Since 2008, these methodologies have evolved, significantly impacting fields like natural language processing (NLP) and computer vision.
In NLP, generative masked language modeling has become the dominant trend since 2018. Meanwhile, in computer vision, discriminative contrastive learning maintained popularity from 2018 to 2021 until masked image modeling started gaining traction post-2022.
Project Structure
The project is structured to provide a holistic overview of the core MIM methods. It supports a variety of applications in both computer vision and other fields by building upon four fundamental components: Masking, Encoder, Target, and Head. These components collectively form the backbone of the MIM frameworks.
Fundamental MIM Methods
Under the umbrella of MIM, various subcategories illustrate the versatility and adaptability of masked modeling:
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MIM for Transformers: Includes frameworks like iGPT, ViT, BEiT, and others. These models leverage the power of transformers to enhance image modeling through masked inputs.
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MIM with Contrastive Learning: Merges contrastive methods with MIM to enhance learning efficiency.
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MIM for Transformers and CNNs: Explores hybrid models that combine the strengths of transformers and convolutional neural networks (CNNs).
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MIM with Advanced Masking: Innovates on the basic masking technique to improve model performance.
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MIM for Multi-Modality: Extends MIM to work with multiple data types, such as images, text, and audio.
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MIM for Vision Generalist Model: Creates versatile vision models that perform effectively across diverse tasks.
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Image Generation: Leverages MIM approaches in the context of image synthesis.
Applications Beyond Computer Vision
The project also highlights how MIM techniques are employed in downstream tasks beyond typical image recognition, such as:
- Object Detection
- Video Representation
- Knowledge Distillation and Few-shot Classification
- Medical Imaging
- Remote Sensing
- 3D Representation Learning
Expanding Horizons to Different Modalities
Beyond vision, MIM methodologies are applied in areas like audio processing, AI for scientific research, and even neuroscience, demonstrating their broad applicability.
Contribution and Community
The project thrives on community involvement, welcoming suggestions and additions to maintain its status as a forward-thinking and comprehensive resource. Contributions can include new research findings, corrections, or enhancements to existing frameworks.
For those interested in further exploring or contributing to the repository, there are detailed project links and paper citations facilitated by tools like Connected Papers and professional reference formats.
Conclusion
The Awesome Masked Modeling for Self-supervised Vision Representation and Beyond project serves as a valuable resource for researchers and practitioners. It encapsulates a rich diversity of methodologies under the masked modeling paradigm, reflecting the ongoing evolution and innovation in self-supervised learning.
As the field continues to grow and diversify, this project stands at the forefront, aiding the community in discovering new insights and applications of masked modeling across various domains.