Awesome CLIP

This repo collects the research resources based on CLIP (Contrastive Language-Image Pre-Training) proposed by OpenAI. If you would like to contribute, please open an issue.

CLIP

• Learning Transferable Visual Models From Natural Language Supervision [code]
• CLIP: Connecting Text and Images
• Multimodal Neurons in Artificial Neural Networks

Training

• OpenCLIP (3rd-party, PyTorch) [code]
• Train-CLIP (3rd-party, PyTorch) [code]
• Paddle-CLIP (3rd-party, PaddlePaddle) [code]

Applications

GAN

• VQGAN-CLIP [code]
• StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery [code]
• CLIP Guided Diffusion [code]
• CLIP2StyleGAN: Unsupervised Extraction of StyleGAN Edit Directions [code]
• TargetCLIP: Image-Based CLIP-Guided Essence Transfer [code]
• DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation [code]
• Clip2latent: Text driven sampling of a pre-trained StyleGAN using denoising diffusion and CLIP [code]

Object Detection

• Roboflow Zero-shot Object Tracking [code]
• Zero-Shot Detection via Vision and Language Knowledge Distillation [code]
• Crop-CLIP [code]
• Detic: Detecting Twenty-thousand Classes using Image-level Supervision [code]
• CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks
• SLIP: Self-supervision meets Language-Image Pre-training [code]
• ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension [code]

Information Retrieval

• Unsplash Image Search [code]
• CLIP4Clip: An Empirical Study of CLIP for End to End Video Clip Retrieval [code]
• Less is More: ClipBERT for Video-and-Language Learning via Sparse Sampling [code]
• Natural Language YouTube Search [code]
• CLIP-as-service: Embed images and sentences into fixed-length vectors with CLIP [code]
• clip-retrieval [code]
• A CLIP-Hitchhiker’s Guide to Long Video Retrieval [code]
• CLIP2Video: Mastering Video-Text Retrieval via Image CLIP [code]
• X-CLIP: End-to-End Multi-grained Contrastive Learning for Video-Text Retrieval [code]
• Extending CLIP for Category-to-image Retrieval in E-commerce [code]

Representation Learning

• Wav2CLIP: Learning Robust Audio Representations From CLIP [code]
• CLIP-Lite: Information Efficient Visual Representation Learning from Textual Annotation [code]
• RegionCLIP: Region-based Language-Image Pretraining [code]
• CMA-CLIP: Cross-Modality Attention CLIP for Image-Text Classification [code]
• DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting [code]
• CyCLIP: Cyclic Contrastive Language-Image Pretraining [code]
• CLIP-ViP: Adapting Pre-trained Image-Text Model to Video-Language Representation Alignment [code]
• DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection [code]
• UniCLIP: Unified Framework for Contrastive Language–Image Pre-training [code]
• SpeechCLIP: Integrating Speech with Pre-Trained Vision and Language Model [code]
• Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese [code]
• PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining [code]
• Learning Visual Representation from Modality-Shared Contrastive Language-Image Pre-training [code]
• Fine-tuned CLIP Models are Efficient Video Learners[code]
• MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining [code]
• Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm [code]
• Democratizing Contrastive Language-Image Pre-training: A CLIP Benchmark of Data, Model, and Supervision [code]

Text-to-3D Generation

• CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation [code]
• Text2Mesh: Text-Driven Neural Stylization for Meshes [code]
• CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP [code]
• CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders [code]
• CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields [code]
• MotionCLIP: Exposing Human Motion Generation to CLIP Space [code]
• AvatarCLIP: Zero-Shot Text-Driven Generation and Animation of 3D Avatars [code]
• ClipFace: Text-guided Editing of Textured 3D Morphable Models [code]

Text-to-Image Generation

• Big Sleep: A simple command line tool for text to image generation [code]
• Deep Daze: A simple command line tool for text to image generation [code]
• CLIP-CLOP: CLIP-Guided Collage and Photomontage [code]
• CLIP-GEN: Language-Free Training of a Text-to-Image Generator with CLIP [code]

Prompt Learning

• Learning to Prompt for Vision-Language Models [code]
• Conditional Prompt Learning for Vision-Language Models [code]
• Prompt-aligned Gradient for Prompt Tuning [code]
• CLIP-Adapter: Better Vision-Language Models with Feature Adapters [code]
• Learning to Compose Soft Prompts for Compositional Zero-Shot Learning [code]

Video Understanding

• VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding [code]
• FitCLIP: Refining Large-Scale Pretrained Image-Text Models for Zero-Shot Video Understanding Tasks [code]
• Frozen CLIP Models are Efficient Video Learners [code]
• Towards Real-Time Text2Video via CLIP-Guided, Pixel-Level Optimization [code]
• MovieCLIP: Visual Scene Recognition in Movies [code]

Image Captioning

• CLIP prefix captioning [code]
• CLIPScore: A Reference-free Evaluation Metric for Image Captioning [code]
• ClipCap: CLIP Prefix for Image Captioning [code]
• Text-Only Training for Image Captioning using Noise-Injected CLIP [code]
• Fine-grained Image Captioning with CLIP Reward [code]

Image Editing

• HairCLIP: Design Your Hair by Text and Reference Image [code]
• CLIPstyler: Image Style Transfer with a Single Text Condition [code]
• CLIPasso: Semantically-Aware Object Sketching [code]
• Image-based CLIP-Guided Essence Transfer [code]
• CLIPDraw: Synthesize drawings to match a text prompt! [code]
• CLIP-CLOP: CLIP-Guided Collage and Photomontage [code]
• Towards Counterfactual Image Manipulation via CLIP [code]
• ClipCrop: Conditioned Cropping Driven by Vision-Language Model [code]
• CLIPascene: Scene Sketching with Different Types and Levels of Abstraction [code]

Image Segmentation

• CLIMS: Cross Language Image Matching for Weakly Supervised Semantic Segmentation [code]
• Image Segmentation Using Text and Image Prompts [code]
• Extract Free Dense Labels from CLIP [code]
• Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP [code]

3D Recognition

• PointCLIP: Point Cloud Understanding by CLIP [code]
• CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training [code]
• MotionCLIP: Exposing Human Motion Generation to CLIP Space [code]
• LidarCLIP or: How I Learned to Talk to Point Clouds[code]
• CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP [code]

Audio

• AudioCLIP: Extending CLIP to Image, Text and Audio [code]
• Wav2CLIP: Learning Robust Audio Representations from Clip [code]
• AVE-CLIP: AudioCLIP-based Multi-window Temporal Transformer for Audio Visual Event Localization [code]

Language Tasks

• CLIP Models are Few-shot Learners: Empirical Studies on VQA and Visual Entailment [code]

Object Navigation

• CLIP on Wheels: Zero-Shot Object Navigation as Object Localization and Exploration [code]

Localization

• Adapting CLIP For Phrase Localization Without Further Training [code]

Others

• Multilingual-CLIP [code]
• CLIP (With Haiku + Jax!) [code]
• CLIP-Event: Connecting Text and Images with Event Structures [code]
• How Much Can CLIP Benefit Vision-and-Language Tasks? [code]
• CLIP meets GamePhysics: Towards bug identification in gameplay videos using zero-shot transfer learning [code]
• CLIP-Fields: Weakly Supervised Semantic Fields for Robotic Memory [code]
• CLIP-Event: Connecting Text and Images with Event Structures [code]
• CLIP Itself is a Strong Fine-tuner: Achieving 85.7% and 88.0% Top-1 Accuracywith ViT-B and ViT-L on ImageNet [code]
• Task Residual for Tuning Vision-Language Models [code]

Acknowledgment

Inspired by Awesome Visual-Transformer.