Collect recent papers on CLIP and prompt learning
core thinking
- Why CLIP-prior works well?
- CLIP learns relationships between vision and language from 400 million text-image pairs.
- How to transfer?
- Zero-shot transfer
- Prompt learning
| Title | Year | Venue | Code | Notes |
|---|---|---|---|---|
| Learning Transferable Visual Models From Natural Language Supervision | 2021 | ICML | Link | Link |
| Title | Year | Venue | Code |
|---|---|---|---|
| Learning to Prompt for Vision-Language Models | 2021 | Arxiv | Link |
| Neural Prompt Search | 2022 | Arxiv | None |
| Prompt Distribution Learning | 2022 | CVPR | None |
| Conditional Prompt Learning for Vision-Language Models | 2022 | CVPR | Link |
| CLIP-Adapter: Better Vision-Language Models with Feature Adapters | 2022 | Arxiv | Link |
| Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language Modeling | 2022 | Arxiv | Link |
| Unsupervised Prompt Learning for Vision-Language Models | 2022 | Arxiv | Link |
| Title | Year | Venue | Code | Notes |
|---|---|---|---|---|
| Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model | 2022 | CVPR | Link | Link |
| DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting | 2022 | CVPR | Link | Link |
| Title | Year | Venue | Code |
|---|---|---|---|
| HairCLIP: Design Your Hair by Text and Reference Image | 2022 | CVPR | Link |
| FlexIT: Towards Flexible Semantic Image Translation | 2022 | CVPR | None |
| VQGAN-CLIP: Open Domain Image Generation and Editing with Natural Language Guidance | 2022 | Arxiv | Link |
| Title | Year | Venue | Code |
|---|---|---|---|
| Bridge-Prompt: Towards Ordinal Action Understanding in Instructional Videos | 2022 | CVPR | Link |
| Title | Year | Venue | Code |
|---|---|---|---|
| ClipCap: CLIP Prefix for Image Captioning | 2021 | Arxiv | Link |
| CPT: Colorful prompt tuning for pre-trained vision-language models | 2021 | Arxiv | None |
| ReCLIP: A Strong Zero-Shot Baseline for Referring Expression Comprehension | 2022 | ACL | None |