This repository is an "Awesome List" curating research resources, papers, and code related to CLIP (Contrastive Language-Image Pre-Training). It serves as a comprehensive catalog for researchers and practitioners exploring CLIP's capabilities and applications across various domains like image generation, object detection, and video understanding.

How It Works

The list organizes projects based on their application area, providing links to associated research papers and their corresponding code repositories. It highlights how CLIP's ability to connect text and image modalities is leveraged for tasks ranging from zero-shot learning to complex generative processes.

Quick Start & Requirements

This is a curated list, not a runnable project. To use any of the listed resources, refer to the individual project's repository for installation and usage instructions.

Highlighted Details

• Extensive coverage of CLIP applications including GANs, object detection, information retrieval, representation learning, text-to-3D generation, prompt learning, video understanding, image captioning, image editing, segmentation, 3D recognition, audio, and language tasks.
• Includes links to foundational CLIP papers, training implementations (OpenCLIP, Paddle-CLIP), and numerous downstream task adaptations.
• Features projects demonstrating CLIP's zero-shot capabilities and its use in guiding generative models like diffusion and StyleGAN.
• Covers research on adapting CLIP for specific modalities like audio and point clouds, as well as multilingual and fine-tuned versions.

Maintenance & Community

The list is maintained by yzhuoning and welcomes contributions via issues. It is inspired by the "Awesome Visual-Transformer" list.

Licensing & Compatibility

The licensing of individual projects varies and must be checked within each linked repository. This list itself does not impose specific licensing restrictions.

Limitations & Caveats

This is a static list of resources; it does not provide a unified interface or framework for using CLIP. Users must engage with each individual project's setup and dependencies.