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https://github.com/jingyi0000/vlm_survey
Collection of AWESOME vision-language models for vision tasks
https://github.com/jingyi0000/vlm_survey
clip computer-vision deep-learning knowledge-distillation multi-modal-model survey transfer-learning vision-language-model
Last synced: 3 days ago
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Collection of AWESOME vision-language models for vision tasks
- Host: GitHub
- URL: https://github.com/jingyi0000/vlm_survey
- Owner: jingyi0000
- Created: 2023-03-30T06:06:59.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-29T09:55:33.000Z (about 1 month ago)
- Last Synced: 2024-09-30T10:41:43.217Z (3 days ago)
- Topics: clip, computer-vision, deep-learning, knowledge-distillation, multi-modal-model, survey, transfer-learning, vision-language-model
- Homepage:
- Size: 651 KB
- Stars: 2,276
- Watchers: 121
- Forks: 202
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Awesome Vision-Language Models [](https://awesome.re)
This is the repository of **Vision Language Models for Vision Tasks: a Survey**, a systematic survey of VLM studies in various visual recognition tasks including image classification, object detection, semantic segmentation, etc. For details, please refer to:
**Vision-Language Models for Vision Tasks: A Survey** [[Paper](https://arxiv.org/abs/2304.00685)]
*IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024*
[](https://arxiv.org/abs/2304.00685)
[](https://GitHub.com/Naereen/StrapDown.js/graphs/commit-activity)
[](http://makeapullrequest.com)
*Feel free to pull requests or contact us if you find any related papers that are not included here.*
The process to submit a pull request is as follows:
- a. Fork the project into your own repository.
- b. Add the Title, Paper link, Conference, Project/Code link in `README.md` using the following format:
```
|[Title](Paper Link)|Conference|[Code/Project](Code/Project link)|
```
- c. Submit the pull request to this branch.
## 🔥 News
Last update on 2024/07/24
#### VLM Pre-training Methods
* [CVPR 2024] Do Vision and Language Encoders Represent the World Similarly? [[Paper](https://openaccess.thecvf.com/content/CVPR2024/papers/Maniparambil_Do_Vision_and_Language_Encoders_Represent_the_World_Similarly_CVPR_2024_paper.pdf)][[Code](https://github.com/mayug/0-shot-llm-vision)]
* [CVPR 2024] Efficient Vision-Language Pre-training by Cluster Masking [[Paper](https://arxiv.org/pdf/2405.08815)][[Code](https://github.com/Zi-hao-Wei/Efficient-Vision-Language-Pre-training-by-Cluster-Masking)]
* [CVPR 2024] Towards Better Vision-Inspired Vision-Language Models [[Paper](https://www.lamda.nju.edu.cn/caoyh/files/VIVL.pdf)]
* [CVPR 2024] Non-autoregressive Sequence-to-Sequence Vision-Language Models [[Paper](https://arxiv.org/abs/2403.02249v1)]
* [CVPR 2024] ViTamin: Designing Scalable Vision Models in the Vision-Language Era [[Paper](https://arxiv.org/abs/2404.02132v1)][[Code](https://github.com/Beckschen/ViTamin)]
* [CVPR 2024] Iterated Learning Improves Compositionality in Large Vision-Language Models [[Paper](https://arxiv.org/abs/2404.02145v1)]
* [CVPR 2024] FairCLIP: Harnessing Fairness in Vision-Language Learning [[Paper](https://arxiv.org/abs/2403.19949v1)][[Code](https://ophai.hms.harvard.edu/datasets/fairvlmed10k)]
* [CVPR 2024] InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks [[Paper](https://arxiv.org/abs/2312.14238)][[Code](https://github.com/OpenGVLab/InternVL)]
* [CVPR 2024] VILA: On Pre-training for Visual Language Models [[Paper](https://arxiv.org/abs/2312.07533)]
* [CVPR 2024] Generative Region-Language Pretraining for Open-Ended Object Detection [[Paper](https://arxiv.org/pdf/2403.10191v1.pdf)][[Code](https://github.com/FoundationVision/GenerateU)]
* [CVPR 2024] Enhancing Vision-Language Pre-training with Rich Supervisions [[Paper](https://arxiv.org/pdf/2403.03346v1.pdf)]
* [ICLR 2024] Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization [[Paper](https://arxiv.org/abs/2309.04669)][[Code](https://github.com/jy0205/LaVIT)]
* [ICLR 2024] MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning [[Paper](https://arxiv.org/abs/2309.07915)][[Code](https://github.com/PKUnlp-icler/MIC)]
* [ICLR 2024] Retrieval-Enhanced Contrastive Vision-Text Models [[Paper](https://arxiv.org/abs/2306.07196)]
#### VLM Transfer Learning Methods
* [ECCV 2024] CLAP: Isolating Content from Style through Contrastive Learning with Augmented Prompts [[Paper](https://arxiv.org/abs/2311.16445)][[Code](https://github.com/YichaoCai1/CLAP)]
* [ECCV 2024] FALIP: Visual Prompt as Foveal Attention Boosts CLIP Zero-Shot Performance [[Paper](https://arxiv.org/abs/2407.05578v1)][[Code](https://pumpkin805.github.io/FALIP/)]
* [ECCV 2024] GalLoP: Learning Global and Local Prompts for Vision-Language Models [[Paper](https://arxiv.org/pdf/2407.01400)]
* [ECCV 2024] Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models [[Paper](https://arxiv.org/pdf/2407.05342v1)][[Code](https://github.com/lloongx/DIKI)]
* [CVPR 2024] One Prompt Word is Enough to Boost Adversarial Robustness for Pre-trained Vision-Language Models [[Paper](https://arxiv.org/abs/2403.01849v1)][[Code](https://github.com/TreeLLi/APT)]
* [CVPR 2024] Any-Shift Prompting for Generalization over Distributions [[Paper](https://arxiv.org/abs/2402.10099)]
* [CVPR 2024] A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models [[Paper](https://arxiv.org/abs/2312.12730)][[Code](https://github.com/jusiro/CLAP)]
* [CVPR 2024] Anchor-based Robust Finetuning of Vision-Language Models [[Paper](https://arxiv.org/abs/2404.06244)]
* [CVPR 2024] Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners [[Paper](https://arxiv.org/abs/2404.02117v1)][[Code](https://github.com/KHU-AGI/PriViLege)]
* [CVPR 2024] Visual In-Context Prompting [[Paper](https://arxiv.org/abs/2311.13601)][[Code](https://github.com/UX-Decoder/DINOv)]
* [CVPR 2024] TCP:Textual-based Class-aware Prompt tuning for Visual-Language Model [[Paper](https://arxiv.org/abs/2311.18231)][[Code](https://github.com/htyao89/Textual-based_Class-aware_prompt_tuning/)]
* [CVPR 2024] Efficient Test-Time Adaptation of Vision-Language Models [[Paper](https://arxiv.org/abs/2403.18293v1)][[Code](https://kdiaaa.github.io/tda/)]
* [CVPR 2024] Dual Memory Networks: A Versatile Adaptation Approach for Vision-Language Models [[Paper](https://arxiv.org/abs/2403.17589v1)][[Code](https://github.com/YBZh/DMN)]
* [ICLR 2024] DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning [[Paper](https://arxiv.org/abs/2309.05173)][[Code](https://github.com/ZhengxiangShi/DePT)]
* [ICLR 2024] Nemesis: Normalizing the soft-prompt vectors of vision-language models [[Paper](https://openreview.net/pdf?id=zmJDzPh1Dm)]
* [ICLR 2024] Prompt Gradient Projection for Continual Learning [[Paper](https://openreview.net/pdf?id=EH2O3h7sBI)]
* [ICLR 2024] An Image Is Worth 1000 Lies: Transferability of Adversarial Images across Prompts on Vision-Language Models [[Paper](https://openreview.net/pdf?id=nc5GgFAvtk)]
* [ICLR 2024] Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching [[Paper](https://arxiv.org/abs/2305.13310)][[Code](https://github.com/aim-uofa/Matcher)]
* [ICLR 2024] Text-driven Prompt Generation for Vision-Language Models in Federated Learning [[Paper](https://arxiv.org/abs/2310.06123)]
* [ICLR 2024] Consistency-guided Prompt Learning for Vision-Language Models [[Paper](https://arxiv.org/abs/2306.01195)]
* [ICLR 2024] C-TPT: Calibrated Test-Time Prompt Tuning for Vision-Language Models via Text Feature Dispersion [[Paper](https://openreview.net/pdf?id=jzzEHTBFOT)]
* [arXiv 2024] Learning to Prompt Segment Anything Models [[Paper](https://arxiv.org/pdf/2401.04651.pdf)]
#### VLM Knowledge Distillation for Detection
* [CVPR 2024] RegionGPT: Towards Region Understanding Vision Language Model [[Paper](https://arxiv.org/pdf/2403.02330v1.pdf)][[Code](https://guoqiushan.github.io/regiongpt.github.io/)]
* [ICLR 2024] LLMs Meet VLMs: Boost Open Vocabulary Object Detection with Fine-grained Descriptors [[Paper](https://arxiv.org/pdf/2402.04630.pdf)]
* [ICLR 2024] Ins-DetCLIP: Aligning Detection Model to Follow Human-Language Instruction [[Paper](https://openreview.net/pdf?id=M0MF4t3hE9)]
#### VLM Knowledge Distillation for Segmentation
* [ICLR 2024] CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction [[Paper](https://arxiv.org/abs/2310.01403)]
#### VLM Knowledge Distillation for Other Vision Tasks
* [ICLR 2024] FROSTER: Frozen CLIP Is A Strong Teacher for Open-Vocabulary Action Recognition [[Paper](https://arxiv.org/pdf/2402.03241.pdf)][[Project](https://visual-ai.github.io/froster)]
* [ICLR 2024] AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection [[Paper](https://arxiv.org/pdf/2310.18961.pdf)][[Code](https://github.com/zqhang/AnomalyCLIP)]
## Abstract
Most visual recognition studies rely heavily on crowd-labelled data in deep neural networks (DNNs) training, and they usually train a DNN for each single visual recognition task, leading to a laborious and time-consuming visual recognition paradigm. To address the two challenges, Vision Language Models (VLMs) have been intensively investigated recently, which learns rich vision-language correlation from web-scale image-text pairs that are almost infinitely available on the Internet and enables zero-shot predictions on various visual recognition tasks with a single VLM. This paper provides a systematic review of visual language models for various visual recognition tasks, including: (1) the background that introduces the development of visual recognition paradigms; (2) the foundations of VLM that summarize the widely-adopted network architectures, pre-training objectives, and downstream tasks; (3) the widely adopted datasets in VLM pre-training and evaluations; (4) the review and categorization of existing VLM pre-training methods, VLM transfer learning methods, and VLM knowledge distillation methods; (5) the benchmarking, analysis and discussion of the reviewed methods; (6) several research challenges and potential research directions that could be pursued in the future VLM studies for visual recognition.
## Citation
If you find our work useful in your research, please consider citing:
```
@article{zhang2024vision,
title={Vision-language models for vision tasks: A survey},
author={Zhang, Jingyi and Huang, Jiaxing and Jin, Sheng and Lu, Shijian},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
year={2024},
publisher={IEEE}
}
```
## Menu
- [Datasets](#datasets)
- [Datasets for VLM Pre-training](#datasets-for-vlm-pre-training)
- [Datasets for VLM Evaluation](#datasets-for-vlm-evaluation)
- [Vision-Language Pre-training Methods](#vision-language-pre-training-methods)
- [Pre-training with Contrastive Objective](#pre-training-with-contrastive-objective)
- [Pre-training with Generative Objective](#pre-training-with-generative-objective)
- [Pre-training with Alignment Objective](#pre-training-with-alignment-objective)
- [Vision-Language Model Transfer Learning Methods](#vision-language-model-transfer-learning-methods)
- [Transfer with Prompt Tuning](#transfer-with-prompt-tuning)
- [Transfer with Text Prompt Tuning](#transfer-with-text-prompt-tuning)
- [Transfer with Visual Prompt Tuning](#transfer-with-visual-prompt-tuning)
- [Transfer with Text and Visual Prompt Tuning](#transfer-with-text-and-visual-prompt-tuning)
- [Transfer with Feature Adapter](#transfer-with-feature-adapter)
- [Transfer with Other Methods](#transfer-with-other-methods)
- [Vision-Language Model Knowledge Distillation Methods](#vision-language-model-knowledge-distillation-methods)
- [Knowledge Distillation for Object Detection](#knowledge-distillation-for-object-detection)
- [Knowledge Distillation for Semantic Segmentation](#knowledge-distillation-for-semantic-segmentation)
## Datasets
### Datasets for VLM Pre-training
| Dataset | Year | Num of Image-Text Paris | Language | Project |
|-----------------------------------------------------|:------:|:-------------------------------:|:----------------:|:------------:|
|[SBU Caption](https://proceedings.neurips.cc/paper_files/paper/2011/file/5dd9db5e033da9c6fb5ba83c7a7ebea9-Paper.pdf)|2011|1M|English|[Project](https://www.cs.rice.edu/~vo9/sbucaptions/)|
|[COCO Caption](https://arxiv.org/pdf/1504.00325v2.pdf)|2016|1.5M|English|[Project](https://github.com/tylin/coco-caption)|
|[Yahoo Flickr Creative Commons 100 Million](https://arxiv.org/pdf/1503.01817v2.pdf)|2016|100M|English|[Project](http://projects.dfki.uni-kl.de/yfcc100m/)|
|[Visual Genome](https://arxiv.org/pdf/1602.07332v1.pdf)|2017|5.4M|English|[Project](http://visualgenome.org/)|
|[Conceptual Captions 3M](https://aclanthology.org/P18-1238.pdf)|2018|3.3M|English|[Project](https://ai.google.com/research/ConceptualCaptions/)|
|[Localized Narratives](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123500630.pdf)|2020|0.87M|English|[Project](https://google.github.io/localized-narratives/)|
|[Conceptual 12M](https://openaccess.thecvf.com/content/CVPR2021/papers/Changpinyo_Conceptual_12M_Pushing_Web-Scale_Image-Text_Pre-Training_To_Recognize_Long-Tail_Visual_CVPR_2021_paper.pdf)|2021|12M|English|[Project](https://github.com/google-research-datasets/conceptual-12m)|
|[Wikipedia-based Image Text](https://arxiv.org/pdf/2103.01913v2.pdf)|2021|37.6M|108 Languages|[Project](https://github.com/google-research-datasets/wit)|
|[Red Caps](https://arxiv.org/pdf/2111.11431v1.pdf)|2021|12M|English|[Project](https://redcaps.xyz/)|
|[LAION400M](https://arxiv.org/pdf/2111.02114v1.pdf)|2021|400M|English|[Project](https://laion.ai/blog/laion-400-open-dataset/)|
|[LAION5B](https://arxiv.org/pdf/2210.08402.pdf)|2022|5B|Over 100 Languages|[Project](https://laion.ai/blog/laion-5b/)|
|[WuKong](https://arxiv.org/pdf/2202.06767.pdf)|2022|100M|Chinese|[Project](https://wukong-dataset.github.io/wukong-dataset/)|
|[CLIP](https://arxiv.org/pdf/2103.00020.pdf)|2021|400M|English|-|
|[ALIGN](https://arxiv.org/pdf/2102.05918.pdf)|2021|1.8B|English|-|
|[FILIP](https://arxiv.org/pdf/2111.07783.pdf)|2021|300M|English|-|
|[WebLI](https://arxiv.org/pdf/2209.06794.pdf)|2022|12B|English|-|
### Datasets for VLM Evaluation
#### Image Classification
| Dataset | Year | Classes | Training | Testing |Evaluation Metric| Project|
|-----------------------------------------------------|:------:|:-------:|:--------:|:-------:|:------:|:-----------:|
|MNIST|1998|10|60,000|10,000|Accuracy|[Project](http://yann.lecun.com/exdb/mnist/)|
|Caltech-101|2004|102|3,060|6,085|Mean Per Class|[Project](https://data.caltech.edu/records/mzrjq-6wc02)|
|PASCAL VOC 2007|2007|20|5,011|4,952|11-point mAP|[Project](http://host.robots.ox.ac.uk/pascal/VOC/voc2007/)|
|Oxford 102 Flowers|2008|102|2,040|6,149|Mean Per Class|[Project](https://www.robots.ox.ac.uk/~vgg/data/flowers/102/)|
|CIFAR-10|2009|10|50,000|10,000|Accuracy|[Project](https://www.cs.toronto.edu/~kriz/cifar.html)|
|CIFAR-100|2009|100|50,000|10,000|Accuracy|[Project](https://www.cs.toronto.edu/~kriz/cifar.html)|
|ImageNet-1k|2009|1000|1,281,167|50,000|Accuracy|[Project](https://www.image-net.org/)|
|SUN397|2010|397|19,850|19,850|Accuracy|[Project](https://vision.princeton.edu/projects/2010/SUN/)|
|SVHN|2011|10|73,257|26,032|Accuracy|[Project](http://ufldl.stanford.edu/housenumbers/)|
|STL-10|2011|10|1,000|8,000|Accuracy|[Project](https://cs.stanford.edu/~acoates/stl10/)|
|GTSRB|2011|43|26,640|12,630|Accuracy|[Project](https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign)|
|KITTI Distance|2012|4|6,770|711|Accuracy|[Project](https://github.com/harshilpatel312/KITTI-distance-estimation)|
|IIIT5k|2012|36|2,000|3,000|Accuracy|[Project](https://cvit.iiit.ac.in/research/projects/cvit-projects/the-iiit-5k-word-dataset)|
|Oxford-IIIT PETS|2012|37|3,680|3,669|Mean Per Class|[Project](https://www.robots.ox.ac.uk/~vgg/data/pets/)|
|Stanford Cars|2013|196|8,144|8,041|Accuracy|[Project](http://ai.stanford.edu/~jkrause/cars/car_dataset.html)|
|FGVC Aircraft|2013|100|6,667|3,333|Mean Per Class|[Project](https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/)|
|Facial Emotion|2013|8|32,140|3,574|Accuracy|[Project](https://www.kaggle.com/competitions/challenges-in-representation-learning-facial-expression-recognition-challenge/data)|
|Rendered SST2|2013|2|7,792|1,821|Accuracy|[Project](https://github.com/openai/CLIP/blob/main/data/rendered-sst2.md)|
|Describable Textures|2014|47|3,760|1,880|Accuracy|[Project](https://www.robots.ox.ac.uk/~vgg/data/dtd/)|
|Food-101|2014|101|75,750|25,250|Accuracy|[Project](https://www.kaggle.com/datasets/dansbecker/food-101)|
|Birdsnap|2014|500|42,283|2,149|Accuracy|[Project](https://thomasberg.org/)|
|RESISC45|2017|45|3,150|25,200|Accuracy|[Project](https://pan.baidu.com/s/1mifR6tU?_at_=1679281159364#list/path=%2F)|
|CLEVR Counts|2017|8|2,000|500|Accuracy|[Project](https://cs.stanford.edu/people/jcjohns/clevr/)|
|PatchCamelyon|2018|2|294,912|32,768|Accuracy|[Project](https://github.com/basveeling/pcam)|
|EuroSAT|2019|10|10,000|5,000|Accuracy|[Project](https://github.com/phelber/eurosat)|
|Hateful Memes|2020|2|8,500|500|ROC AUC|[Project](https://ai.facebook.com/blog/hateful-memes-challenge-and-data-set/)|
|Country211|2021|211|43,200|21,100|Accuracy|[Project](https://github.com/openai/CLIP/blob/main/data/country211.md)|
#### Image-Text Retrieval
| Dataset | Year | Classes | Training | Testing |Evaluation Metric| Project|
|-----------------------------------------------------|:------:|:-------:|:--------:|:-------:|:------:|:-----------:|
|Flickr30k|2014|-|31,783|-|Recall|[Project](https://shannon.cs.illinois.edu/DenotationGraph/)
|COCO Caption|2015|-|82,783|5,000|Recall|[Project](https://github.com/tylin/coco-caption)
#### Action Recognition
| Dataset | Year | Classes | Training | Testing |Evaluation Metric| Project|
|-----------------------------------------------------|:------:|:-------:|:--------:|:-------:|:------:|:-----------:|
|UCF101|2012|101|9,537|1,794|Accuracy|[Project](https://www.crcv.ucf.edu/data/UCF101.php)|
|Kinetics700|2019|700|494,801|31,669|Mean (top1, top5)|[Project](https://www.deepmind.com/open-source/kinetics)|
|RareAct|2020|122|7,607|-|mWAP, mSAP|[Project](https://github.com/antoine77340/RareAct)|
#### Object Detection
| Dataset | Year | Classes | Training | Testing |Evaluation Metric| Project|
|-----------------------------------------------------|:------:|:-------:|:--------:|:-------:|:------:|:-----------:|
|COCO 2014 Detection|2014|80|83,000|41,000|Box mAP|[Project](https://www.kaggle.com/datasets/jeffaudi/coco-2014-dataset-for-yolov3)|
|COCO 2017 Detection|2017|80|118,000|5,000|Box mAP|[Project](https://www.kaggle.com/datasets/awsaf49/coco-2017-dataset)|
|LVIS|2019|1203|118,000|5,000|Box mAP|[Project](https://www.lvisdataset.org/)|
|ODinW|2022|314|132,413|20,070|Box mAP|[Project](https://eval.ai/web/challenges/challenge-page/1839/overview)|
#### Semantic Segmentation
| Dataset | Year | Classes | Training | Testing |Evaluation Metric| Project|
|-----------------------------------------------------|:------:|:-------:|:--------:|:-------:|:------:|:-----------:|
|PASCAL VOC 2012|2012|20|1,464|1,449|mIoU|[Project](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/)|
|PASCAL Content|2014|459|4,998|5,105|mIoU|[Project](https://www.cs.stanford.edu/~roozbeh/pascal-context/)|
|Cityscapes|2016|19|2,975|500|mIoU|[Project](https://www.cityscapes-dataset.com/)|
|ADE20k|2017|150|25,574|2,000|mIoU|[Project](https://groups.csail.mit.edu/vision/datasets/ADE20K/)|
## Vision-Language Pre-training Methods
### Pre-training with Contrastive Objective
| Paper | Published in | Code/Project |
|---------------------------------------------------|:-------------:|:------------:|
|[CLIP: Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/pdf/2103.00020.pdf)|ICML 2021|[Code](https://github.com/openai/CLIP)|
|[ALIGN: Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://arxiv.org/pdf/2102.05918.pdf)|ICML 2021|-|
|[OTTER: Data Efficient Language-Supervised Zero-Shot Recognition with Optimal Transport Distillation](https://github.com/facebookresearch/OTTER)|arXiv 2021|[Code](https://github.com/facebookresearch/OTTER)|
|[Florence: A New Foundation Model for Computer Vision](https://arxiv.org/abs/2111.11432)|arXiv 2021|-|
|[RegionClip: Region-based Language-Image Pretraining](https://arxiv.org/abs/2112.09106)|arXiv 2021|[Code](https://github.com/microsoft/RegionCLIP)|
|[DeCLIP: Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm](https://arxiv.org/abs/2110.05208)|ICLR 2022|[Code](https://github.com/Sense-GVT/DeCLIP)|
|[FILIP: Fine-grained Interactive Language-Image Pre-Training](https://arxiv.org/abs/2111.07783)|ICLR 2022|-|
|[KELIP: Large-scale Bilingual Language-Image Contrastive Learning](https://arxiv.org/abs/2203.14463)|ICLRW 2022|[Code](https://github.com/navervision/KELIP)|
|[ZeroVL: Contrastive Vision-Language Pre-training with Limited Resources](https://arxiv.org/abs/2112.09331)|ECCV 2022|[Code](https://github.com/zerovl/ZeroVL)|
|[SLIP: Self-supervision meets Language-Image Pre-training](https://arxiv.org/abs/2112.12750)|ECCV 2022|[Code](https://github.com/facebookresearch/SLIP)|
|[UniCL: Unified Contrastive Learning in Image-Text-Label Space](https://arxiv.org/abs/2204.03610)|CVPR 2022|[Code](https://github.com/microsoft/UniCL)|
|[LiT: Zero-Shot Transfer with Locked-image text Tuning](https://arxiv.org/abs/2111.07991)|CVPR 2022|[Code](https://google-research.github.io/vision_transformer/lit/)|
|[GroupViT: Semantic Segmentation Emerges from Text Supervision](https://arxiv.org/abs/2202.11094)|CVPR 2022|[Code](https://github.com/NVlabs/GroupViT)|
|[PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining](https://arxiv.org/abs/2204.14095)|NeurIPS 2022|-|
|[UniCLIP: Unified Framework for Contrastive Language-Image Pre-training](https://arxiv.org/abs/2209.13430)|NeurIPS 2022|-|
|[K-LITE: Learning Transferable Visual Models with External Knowledge](https://arxiv.org/abs/2204.09222)|NeurIPS 2022|[Code](https://github.com/microsoft/klite)|
|[FIBER: Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone](https://arxiv.org/abs/2206.07643)|NeurIPS 2022|[Code](https://github.com/microsoft/FIBER)|
|[Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese](https://arxiv.org/abs/2211.01335)|arXiv 2022|[Code](https://github.com/OFA-Sys/Chinese-CLIP)|
|[AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities](https://arxiv.org/abs/2211.06679)|arXiv 2022|[Code](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltCLIP)|
|[SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation](https://arxiv.org/abs/2211.14813)|arXiv 2022|[Code](https://github.com/ArrowLuo/SegCLIP)|
|[NLIP: Noise-robust Language-Image Pre-training](https://arxiv.org/abs/2212.07086)|AAAI 2023|-|
|[PaLI: A Jointly-Scaled Multilingual Language-Image Model](https://arxiv.org/abs/2209.06794)|ICLR 2023|[Project](https://ai.googleblog.com/2022/09/pali-scaling-language-image-learning-in.html)|
|[HiCLIP: Contrastive Language-Image Pretraining with Hierarchy-aware Attention](https://arxiv.org/abs/2303.02995)|ICLR 2023|[Code](https://github.com/jeykigung/hiclip)|
|[CLIPPO: Image-and-Language Understanding from Pixels Only](https://arxiv.org/abs/2212.08045)|CVPR 2023|[Code](https://github.com/google-research/big_vision)|
|[RA-CLIP: Retrieval Augmented Contrastive Language-Image Pre-training](https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_RA-CLIP_Retrieval_Augmented_Contrastive_Language-Image_Pre-Training_CVPR_2023_paper.pdf)|CVPR 2023|-|
|[DeAR: Debiasing Vision-Language Models with Additive Residuals](https://arxiv.org/abs/2303.10431)|CVPR 2023|-|
|[Filtering, Distillation, and Hard Negatives for Vision-Language Pre-Training](https://arxiv.org/abs/2301.02280)|CVPR 2023|[Code](https://github.com/facebookresearch/diht)|
|[LaCLIP: Improving CLIP Training with Language Rewrites](https://arxiv.org/abs/2305.20088)|NeurIPS 2023|[Code](https://github.com/LijieFan/LaCLIP)|
|[ALIP: Adaptive Language-Image Pre-training with Synthetic Caption](https://arxiv.org/pdf/2308.08428.pdf)|ICCV 2023|[Code](https://github.com/deepglint/ALIP)|
|[GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training](https://arxiv.org/pdf/2308.11331v1.pdf)|ICCV 2023|-|
|[CLIPpy: Perceptual Grouping in Contrastive Vision-Language Models](https://arxiv.org/abs/2210.09996)|ICCV 2023|-|
### Pre-training with Generative Objective
| Paper | Published in | Code/Project |
|---------------------------------------------------|:-------------:|:------------:|
|[FLAVA: A Foundational Language And Vision Alignment Model](https://arxiv.org/abs/2112.04482)|CVPR 2022|[Code](https://github.com/facebookresearch/multimodal/tree/main/examples/flava)|
|[CoCa: Contrastive Captioners are Image-Text Foundation Models](https://arxiv.org/abs/2205.01917)|arXiv 2022|[Code](https://github.com/lucidrains/CoCa-pytorch)|
|[Too Large; Data Reduction for Vision-Language Pre-Training](https://arxiv.org/abs/2305.20087)|arXiv 2023|[Code](https://github.com/showlab/data-centric.vlp)|
|[SAM: Segment Anything](https://arxiv.org/abs/2304.02643)|arXiv 2023|[Code](https://github.com/facebookresearch/segment-anything)|
|[SEEM: Segment Everything Everywhere All at Once](https://arxiv.org/pdf/2304.06718.pdf)|arXiv 2023|[Code](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once)|
|[Semantic-SAM: Segment and Recognize Anything at Any Granularity](https://arxiv.org/pdf/2307.04767.pdf)|arXiv 2023|[Code](https://github.com/UX-Decoder/Semantic-SAM)|
### Pre-training with Alignment Objective
| Paper | Published in | Code/Project |
|---------------------------------------------------|:-------------:|:------------:|
|[GLIP: Grounded Language-Image Pre-training](https://arxiv.org/abs/2112.03857)|CVPR 2022|[Code](https://github.com/microsoft/GLIP)|
|[DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection](https://arxiv.org/abs/2209.09407)|NeurIPS 2022|-|
|[nCLIP: Non-Contrastive Learning Meets Language-Image Pre-Training](https://arxiv.org/abs/2210.09304)|CVPR 2023|[Code](https://github.com/shallowtoil/xclip)|
## Vision-Language Model Transfer Learning Methods
### Transfer with Prompt Tuning
#### Transfer with Text Prompt Tuning
| Paper | Published in | Code/Project |
|---------------------------------------------------|:-------------:|:------------:|
|[CoOp: Learning to Prompt for Vision-Language Models](https://arxiv.org/abs/2109.01134)|IJCV 2022|[Code](https://github.com/KaiyangZhou/CoOp)|
|[CoCoOp: Conditional Prompt Learning for Vision-Language Models](https://arxiv.org/abs/2203.05557)|CVPR 2022|[Code](https://github.com/KaiyangZhou/CoOp)|
|[ProDA: Prompt Distribution Learning](https://arxiv.org/abs/2205.03340)|CVPR 2022|-|
|[DenseClip: Language-Guided Dense Prediction with Context-Aware Prompting](https://arxiv.org/abs/2112.01518)|CVPR 2022|[Code](https://github.com/raoyongming/DenseCLIP)|
|[TPT: Test-time prompt tuning for zero-shot generalization in vision-language models](https://arxiv.org/abs/2209.07511)|NeurIPS 2022|[Code](https://github.com/azshue/TPT)|
|[DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations](https://arxiv.org/abs/2206.09541)|NeurIPS 2022|[Code](https://github.com/sunxm2357/DualCoOp)|
|[CPL: Counterfactual Prompt Learning for Vision and Language Models](https://arxiv.org/abs/2210.10362)|EMNLP 2022|[Code](https://github.com/eric-ai-lab/CPL)|
|[Bayesian Prompt Learning for Image-Language Model Generalization](https://arxiv.org/abs/2210.02390v2)|arXiv 2022|-|
|[UPL: Unsupervised Prompt Learning for Vision-Language Models](https://arxiv.org/abs/2204.03649)|arXiv 2022|[Code](https://github.com/tonyhuang2022/UPL)|
|[ProGrad: Prompt-aligned Gradient for Prompt Tuning](https://arxiv.org/abs/2205.14865)|arXiv 2022|[Code](https://github.com/BeierZhu/Prompt-align)|
|[SoftCPT: Prompt Tuning with Soft Context Sharing for Vision-Language Models](https://arxiv.org/abs/2208.13474)|arXiv 2022|[Code](https://github.com/kding1225/softcpt)|
|[SubPT: Understanding and Mitigating Overfitting in Prompt Tuning for Vision-Language Models](https://arxiv.org/abs/2211.02219)|TCSVT 2023|[Code](https://github.com/machengcheng2016/Subspace-Prompt-Learning)|
|[LASP: Text-to-Text Optimization for Language-Aware Soft Prompting of Vision & Language Models](https://arxiv.org/abs/2210.01115)|CVPR 2023|[Code](https://www.adrianbulat.com/lasp)|
|[LMPT: Prompt Tuning with Class-Specific Embedding Loss for Long-tailed Multi-Label Visual Recognition](https://arxiv.org/abs/2305.04536)|arXiv 2023|[Code](https://github.com/richard-peng-xia/LMPT)|
|[Texts as Images in Prompt Tuning for Multi-Label Image Recognition](https://arxiv.org/abs/2211.12739)|CVPR 2023|[code](https://github.com/guozix/TaI-DPT)
|[Visual-Language Prompt Tuning with Knowledge-guided Context Optimization](https://arxiv.org/abs/2303.13283)|CVPR 2023|[Code](https://github.com/htyao89/KgCoOp)|
|[Learning to Name Classes for Vision and Language Models](https://arxiv.org/abs/2304.01830v1)|CVPR 2023|-|
|[PLOT: Prompt Learning with Optimal Transport for Vision-Language Models](https://arxiv.org/abs/2210.01253)|ICLR 2023|[Code](https://github.com/CHENGY12/PLOT)|
|[CuPL: What does a platypus look like? Generating customized prompts for zero-shot image classification](https://arxiv.org/abs/2209.03320)|ICCV 2023|[Code](https://github.com/sarahpratt/CuPL)|
|[ProTeCt: Prompt Tuning for Hierarchical Consistency](https://arxiv.org/abs/2306.02240)|arXiv 2023|-|
|[Enhancing CLIP with CLIP: Exploring Pseudolabeling for Limited-Label Prompt Tuning](https://arxiv.org/abs/2306.01669)|arXiv 2023|[Code](http://github.com/BatsResearch/menghini-enhanceCLIPwithCLIP-code)|
|[Why Is Prompt Tuning for Vision-Language Models Robust to Noisy Labels?](https://arxiv.org/pdf/2307.11978v1.pdf)|ICCV 2023|[Code](https://github.com/CEWu/PTNL)|
|[Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models](https://arxiv.org/pdf/2303.06571.pdf)|ICCV 2023|-|
|[Knowledge-Aware Prompt Tuning for Generalizable Vision-Language Models](https://arxiv.org/pdf/2308.11186v1.pdf)|ICCV 2023|-|
|[Read-only Prompt Optimization for Vision-Language Few-shot Learning](https://arxiv.org/pdf/2308.14960.pdf)|ICCV 2023|[Code](https://github.com/mlvlab/RPO)|
|[Bayesian Prompt Learning for Image-Language Model Generalization](https://arxiv.org/pdf/2210.02390.pdf)|ICCV 2023|[Code](https://github.com/saic-fi/Bayesian-Prompt-Learning)|
|[Distribution-Aware Prompt Tuning for Vision-Language Models](https://arxiv.org/pdf/2309.03406.pdf)|ICCV 2023|[Code](https://github.com/mlvlab/DAPT)|
|[LPT: Long-Tailed Prompt Tuning For Image Classification](https://arxiv.org/pdf/2210.01033.pdf)|ICCV 2023|[Code](https://github.com/DongSky/LPT)|
|[Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning](https://openaccess.thecvf.com/content/ICCV2023/papers/Feng_Diverse_Data_Augmentation_with_Diffusions_for_Effective_Test-time_Prompt_Tuning_ICCV_2023_paper.pdf)|ICCV 2023|[Code](https://github.com/chunmeifeng/DiffTPT)|
|[CLAP: Isolating Content from Style through Contrastive Learning with Augmented Prompts](https://arxiv.org/abs/2311.16445)|ECCV 2024|[Code](https://github.com/YichaoCai1/CLAP)|
#### Transfer with Visual Prompt Tuning
| Paper | Published in | Code/Project |
|---------------------------------------------------|:-------------:|:------------:|
|[Exploring Visual Prompts for Adapting Large-Scale Models](https://arxiv.org/abs/2203.17274)|arXiv 2022|[Code](https://github.com/hjbahng/visual_prompting)|
|[Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification](https://arxiv.org/abs/2306.02243)|arXiv 2023|-|
|[Fine-Grained Visual Prompting](https://arxiv.org/abs/2306.04356)|arXiv 2023|-|
|[LoGoPrompt: Synthetic Text Images Can Be Good Visual Prompts for Vision-Language Models](https://arxiv.org/pdf/2309.01155v1.pdf)|ICCV 2023|[Code](https://chengshiest.github.io/logo/)|
#### Transfer with Text and Visual Prompt Tuning
| Paper | Published in | Code/Project |
|---------------------------------------------------|:-------------:|:------------:|
|[UPT: Unified Vision and Language Prompt Learning](https://arxiv.org/abs/2210.07225)|arXiv 2022|[Code](https://github.com/yuhangzang/upt)|
|[MVLPT: Multitask Vision-Language Prompt Tuning](https://arxiv.org/abs/2211.11720)|arXiv 2022|[Code](https://github.com/facebookresearch/vilbert-multi-task)|
|[CAVPT: Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model](https://arxiv.org/abs/2208.08340)|arXiv 2022|[Code](https://github.com/fanrena/DPT)|
|[MaPLe: Multi-modal Prompt Learning](https://arxiv.org/abs/2210.03117)|CVPR 2023|[Code](https://github.com/muzairkhattak/multimodal-prompt-learning)|
### Transfer with Feature Adapter
| Paper | Published in | Code/Project |
|---------------------------------------------------|:-------------:|:------------:|
|[Clip-Adapter: Better Vision-Language Models with Feature Adapters](https://arxiv.org/abs/2110.04544)|arXiv 2021|[Code](https://github.com/gaopengcuhk/CLIP-Adapter)|
|[Tip-Adapte: Training-free Adaption of CLIP for Few-shot Classification](https://arxiv.org/abs/2207.09519)|ECCV 2022|[Code](https://github.com/gaopengcuhk/Tip-Adapter)|
|[SVL-Adapter: Self-Supervised Adapter for Vision-Language Pretrained Models](https://arxiv.org/abs/2210.03794)|BMVC 2022|[Code](https://github.com/omipan/svl_adapter)|
|[CLIPPR: Improving Zero-Shot Models with Label Distribution Priors](https://arxiv.org/abs/2212.00784)|arXiv 2022|[Code](https://github.com/jonkahana/CLIPPR)|
|[SgVA-CLIP: Semantic-guided Visual Adapting of Vision-Language Models for Few-shot Image Classification](https://arxiv.org/abs/2211.16191)|arXiv 2022|-|
|[SuS-X: Training-Free Name-Only Transfer of Vision-Language Models](https://arxiv.org/abs/2211.16198)|ICCV 2023|[Code](https://github.com/vishaal27/SuS-X)|
|[VL-PET: Vision-and-Language Parameter-Efficient Tuning via Granularity Control](https://arxiv.org/abs/2308.09804)|ICCV 2023|[Code](https://github.com/HenryHZY/VL-PET)|
|[SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More](https://arxiv.org/abs/2304.09148)|arXiv 2023|[Code](http://tianrun-chen.github.io/SAM-Adaptor/)|
|[Segment Anything in High Quality](https://arxiv.org/abs/2306.01567)|arXiv 2023|[Code](https://github.com/SysCV/SAM-HQ)|
|[HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical Understanding](https://arxiv.org/abs/2311.14064)|arXiv 2023|[Code](https://github.com/richard-peng-xia/HGCLIP)|
|[CLAP: Contrastive Learning with Augmented Prompts for Robustness on Pretrained Vision-Language Models](https://arxiv.org/abs/2311.16445)|arXiv 2023|-|
### Transfer with Other Methods
| Paper | Published in | Code/Project |
|---------------------------------------------------|:-------------:|:------------:|
|[VT-Clip: Enhancing Vision-Language Models with Visual-guided Texts](https://arxiv.org/abs/2112.02399)|arXiv 2021|-|
|[Wise-FT: Robust fine-tuning of zero-shot models](https://arxiv.org/abs/2109.01903)|CVPR 2022|[Code](https://github.com/mlfoundations/wise-ft)|
|[MaskCLIP: Extract Free Dense Labels from CLIP](https://arxiv.org/abs/2112.01071)|ECCV 2022|[Code](https://github.com/chongzhou96/MaskCLIP)|
|[MUST: Masked Unsupervised Self-training for Label-free Image Classification](https://arxiv.org/abs/2206.02967)|ICLR 2023| [Code](https://github.com/salesforce/MUST)|
|[CALIP: Zero-Shot Enhancement of CLIP with Parameter-free Attention](https://arxiv.org/abs/2209.14169)|AAAI 2023|[Code](https://github.com/ziyuguo99/calip)|
|[Semantic Prompt for Few-Shot Image Recognition](https://arxiv.org/abs/2303.14123v1)|CVPR 2023|-|
|[Prompt, Generate, then Cache: Cascade of Foundation Models makes Strong Few-shot Learners](https://arxiv.org/abs/2303.02151)|CVPR 2023|[Code](https://github.com/ZrrSkywalker/CaFo)|
|[Task Residual for Tuning Vision-Language Models](https://arxiv.org/abs/2211.10277)|CVPR 2023|[Code](https://github.com/geekyutao/TaskRes)|
|[Deeply Coupled Cross-Modal Prompt Learning](https://arxiv.org/abs/2305.17903)|ACL 2023|[Code](https://github.com/GingL/CMPA)|
|[Prompt Ensemble Self-training for Open-Vocabulary Domain Adaptation](https://arxiv.org/abs/2306.16658)|arXiv 2023|-|
|[Personalize Segment Anything Model with One Shot](https://arxiv.org/abs/2305.03048)|arXiv 2023|[Code](https://github.com/ZrrSkywalker/Personalize-SAM)|
|[Chils: Zero-shot image classification with hierarchical label sets](https://proceedings.mlr.press/v202/novack23a/novack23a.pdf)|ICML 2023|[Code](https://github.com/acmi-lab/CHILS)|
|[Improving Zero-shot Generalization and Robustness of Multi-modal Models](https://openaccess.thecvf.com/content/CVPR2023/papers/Ge_Improving_Zero-Shot_Generalization_and_Robustness_of_Multi-Modal_Models_CVPR_2023_paper.pdf)|CVPR 2023|[Code](https://github.com/gyhandy/Hierarchy-CLIP)|
|[Exploiting Category Names for Few-Shot Classification with Vision-Language Models](https://openreview.net/pdf?id=w25Q9Ttjrs)|ICLR W 2023|-|
|[Beyond Sole Strength: Customized Ensembles for Generalized Vision-Language Models](https://arxiv.org/abs/2311.17091)|arXiv 2023|[Code](https://github.com/zhiheLu/Ensemble_VLM)|
|[Regularized Mask Tuning: Uncovering Hidden Knowledge in Pre-trained Vision-Language Models](https://arxiv.org/pdf/2307.15049v1.pdf)|ICCV 2023|[Code](https://wuw2019.github.io/RMT/)|
|[PromptStyler: Prompt-driven Style Generation for Source-free Domain Generalization](https://arxiv.org/pdf/2307.15199v1.pdf)|ICCV 2023|[Code](https://promptstyler.github.io/)|
|[PADCLIP: Pseudo-labeling with Adaptive Debiasing in CLIP for Unsupervised Domain Adaptation](https://assets.amazon.science/ff/08/64f27eb54b82a0c59c95dc138af4/padclip-pseudo-labeling-with-adaptive-debiasing-in-clip.pdf)|ICCV 2023|-|
|[Black Box Few-Shot Adaptation for Vision-Language models](https://arxiv.org/pdf/2304.01752.pdf)|ICCV 2023|[Code](https://github.com/saic-fi/LFA)|
|[AD-CLIP: Adapting Domains in Prompt Space Using CLIP](https://arxiv.org/pdf/2308.05659.pdf)|ICCVW 2023|-|
|[Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning](https://arxiv.org/pdf/2306.14565.pdf)|arXiv 2023|[Code](https://fuxiaoliu.github.io/LRV/)|
|[Language Models as Black-Box Optimizers for Vision-Language Models](https://arxiv.org/abs/2309.05950)|arXiv 2023|-|
## Vision-Language Model Knowledge Distillation Methods
### Knowledge Distillation for Object Detection
| Paper | Published in | Code/Project |
|---------------------------------------------------|:-------------:|:------------:|
|[ViLD: Open-vocabulary Object Detection via Vision and Language Knowledge Distillation](https://arxiv.org/abs/2104.13921)|ICLR 2022|[Code](https://github.com/tensorflow/tpu/tree/master/models/official/detection/projects/vild)|
|[DetPro: Learning to Prompt for Open-Vocabulary Object Detection with Vision-Language Model](https://arxiv.org/abs/2203.14940)|CVPR 2022|[Code](https://github.com/dyabel/detpro)|
|[XPM: Open-Vocabulary Instance Segmentation via Robust Cross-Modal Pseudo-Labeling](https://arxiv.org/abs/2111.12698)|CVPR 2022|[Code](https://github.com/hbdat/cvpr22_cross_modal_pseudo_labeling)|
|[Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection](https://arxiv.org/abs/2207.03482)|NeurIPS 2022|[Code](https://github.com/hanoonaR/object-centric-ovd)|
|[PromptDet: Towards Open-vocabulary Detection using Uncurated Images](https://arxiv.org/abs/2203.16513)|ECCV 2022|[Code](https://github.com/fcjian/PromptDet)|
|[PB-OVD: Open Vocabulary Object Detection with Pseudo Bounding-Box Labels](https://arxiv.org/abs/2111.09452)|ECCV 2022|[Code](https://github.com/salesforce/PB-OVD)|
|[OV-DETR: Open-Vocabulary DETR with Conditional Matching](https://arxiv.org/abs/2203.11876)|ECCV 2022|[Code](https://github.com/yuhangzang/OV-DETR)|
|[Detic: Detecting Twenty-thousand Classes using Image-level Supervision](https://arxiv.org/abs/2201.02605)|ECCV 2022|[Code](https://github.com/facebookresearch/Detic)|
|[OWL-ViT: Simple Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2205.06230)|ECCV 2022|[Code](https://github.com/google-research/scenic/tree/main/scenic/projects/owl_vit)|
|[VL-PLM: Exploiting Unlabeled Data with Vision and Language Models for Object Detection](https://arxiv.org/abs/2207.08954)|ECCV 2022|[Code](https://github.com/xiaofeng94/VL-PLM)|
|[ZSD-YOLO: Zero-shot Object Detection Through Vision-Language Embedding Alignment](https://arxiv.org/abs/2109.12066)|arXiv 2022|[Code](https://github.com/Johnathan-Xie/ZSD-YOLO)|
|[HierKD: Open-Vocabulary One-Stage Detection with Hierarchical Visual-Language Knowledge Distillation](https://arxiv.org/abs/2203.10593)|arXiv 2022|[Code](https://github.com/mengqiDyangge/HierKD)|
|[VLDet: Learning Object-Language Alignments for Open-Vocabulary Object Detection](https://arxiv.org/abs/2211.14843)|ICLR 2023|[Code](https://github.com/clin1223/VLDet)|
|[F-VLM: Open-Vocabulary Object Detection upon Frozen Vision and Language Models](https://arxiv.org/abs/2209.15639)|ICLR 2023|[Code](https://github.com/google-research/google-research/tree/master/fvlm)|
|[CondHead: Learning to Detect and Segment for Open Vocabulary Object Detection](https://arxiv.org/abs/2212.12130)|CVPR 2023|-|
|[Aligning Bag of Regions for Open-Vocabulary Object Detection](https://arxiv.org/abs/2302.13996)|CVPR 2023|[Code](https://github.com/wusize/ovdet)|
|[Region-Aware Pretraining for Open-Vocabulary Object Detection with Vision Transformers](https://arxiv.org/abs/2305.07011v1)|CVPR 2023|[Code](https://github.com/mcahny/rovit)|
|[Object-Aware Distillation Pyramid for Open-Vocabulary Object Detection](https://arxiv.org/abs/2303.05892)|CVPR 2023|[Code](https://github.com/LutingWang/OADP)|
|[CORA: Adapting CLIP for Open-Vocabulary Detection with Region Prompting and Anchor Pre-Matching](https://arxiv.org/abs/2303.13076v1)|CVPR 2023|[Code](https://github.com/tgxs002/CORA)|
|[DetCLIPv2: Scalable Open-Vocabulary Object Detection Pre-training via Word-Region Alignment](https://arxiv.org/abs/2304.04514v1)|CVPR 2023|-|
|[Detecting Everything in the Open World: Towards Universal Object Detection](https://arxiv.org/abs/2303.11749)|CVPR 2023|[Code](https://github.com/zhenyuw16/UniDetector)|
|[CapDet: Unifying Dense Captioning and Open-World Detection Pretraining](https://arxiv.org/abs/2303.02489)|CVPR 2023|-|
|[Contextual Object Detection with Multimodal Large Language Models](https://arxiv.org/abs/2305.18279)|arXiv 2023|[Code](https://github.com/yuhangzang/ContextDET)|
|[Building One-class Detector for Anything: Open-vocabulary Zero-shot OOD Detection Using Text-image Models](https://arxiv.org/abs/2305.17207)|arXiv 2023|[Code](https://github.com/gyhandy/One-Class-Anything)|
|[EdaDet: Open-Vocabulary Object Detection Using Early Dense Alignment](https://arxiv.org/pdf/2309.01151v1.pdf)|ICCV 2023|[Code](https://chengshiest.github.io/edadet)|
|[Improving Pseudo Labels for Open-Vocabulary Object Detection](https://arxiv.org/pdf/2308.06412.pdf)|arXiv 2023|-|
### Knowledge Distillation for Semantic Segmentation
| Paper | Published in | Code/Project |
|---------------------------------------------------|:-------------:|:------------:|
|[SSIW: Semantic Segmentation In-the-Wild Without Seeing Any Segmentation Examples](https://arxiv.org/abs/2112.03185)|arXiv 2021|-|
|[ReCo: Retrieve and Co-segment for Zero-shot Transfer](https://arxiv.org/abs/2206.07045)|NeurIPS 2022|[Code](https://github.com/NoelShin/reco)|
|[CLIMS: Cross Language Image Matching for Weakly Supervised Semantic Segmentation](https://arxiv.org/abs/2203.02668)|CVPR 2022|[Code](https://github.com/CVI-SZU/CLIMS)|
|[CLIPSeg: Image Segmentation Using Text and Image Prompts](https://arxiv.org/abs/2112.10003)|CVPR 2022|[Code](https://github.com/timojl/clipseg)|
|[ZegFormer: Decoupling Zero-Shot Semantic Segmentation](https://arxiv.org/abs/2112.07910)|CVPR 2022|[Code](https://github.com/dingjiansw101/ZegFormer)|
|[LSeg: Language-driven Semantic Segmentation](https://arxiv.org/abs/2201.03546)|ICLR 2022|[Code](https://github.com/isl-org/lang-seg)|
|[ZSSeg: A Simple Baseline for Open-Vocabulary Semantic Segmentation with Pre-trained Vision-language Model](https://arxiv.org/abs/2112.14757)|ECCV 2022|[Code](https://github.com/MendelXu/zsseg.baseline)|
|[OpenSeg: Scaling Open-Vocabulary Image Segmentation with Image-Level Labels](https://arxiv.org/abs/2112.12143)|ECCV 2022|[Code](https://github.com/tensorflow/tpu/tree/641c1ac6e26ed788327b973582cbfa297d7d31e7/models/official/detection/projects/openseg)|
|[Fusioner: Open-vocabulary Semantic Segmentation with Frozen Vision-Language Models](https://arxiv.org/abs/2210.15138)|BMVC 2022|[Code](https://github.com/chaofanma/Fusioner)|
|[OVSeg: Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP](https://arxiv.org/abs/2210.04150)|CVPR 2023|[Code](https://github.com/facebookresearch/ov-seg)|
|[ZegCLIP: Towards Adapting CLIP for Zero-shot Semantic Segmentation](https://arxiv.org/abs/2212.03588)|CVPR 2023|[Code](https://github.com/ZiqinZhou66/ZegCLIP)|
|[CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation](https://arxiv.org/abs/2212.09506)|CVPR 2023|[Code](https://github.com/linyq2117/CLIP-ES)|
|[FreeSeg: Unified, Universal and Open-Vocabulary Image Segmentation](https://arxiv.org/abs/2303.17225v1)|CVPR 2023|[Code](https://freeseg.github.io/)|
|[Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations](https://arxiv.org/abs/2303.16891v1)|CVPR 2023|[Code](https://vibashan.github.io/ovis-web/)|
|[Exploring Open-Vocabulary Semantic Segmentation without Human Labels](https://arxiv.org/abs/2306.00450)|arXiv 2023|-|
|[OpenVIS: Open-vocabulary Video Instance Segmentation](https://arxiv.org/abs/2305.16835)|arXiv 2023|-|
|[Segment Anything is A Good Pseudo-label Generator for Weakly Supervised Semantic Segmentation](https://arxiv.org/abs/2305.01275)|arXiv 2023|-|
|[Segment Anything Model (SAM) Enhanced Pseudo Labels for Weakly Supervised Semantic Segmentation](https://arxiv.org/abs/2305.05803)|arXiv 2023|[Code](https://github.com/cskyl/SAM_WSSS)|
|[Plug-and-Play, Dense-Label-Free Extraction of Open-Vocabulary Semantic Segmentation from Vision-Language Models](https://arxiv.org/abs/2311.17095)|arXiv 2023|-|
|[SegPrompt: Boosting Open-World Segmentation via Category-level Prompt Learning](https://arxiv.org/pdf/2308.06531v1.pdf)|ICCV 2023|[Code](https://github.com/aim-uofa/SegPrompt)|
|[ICPC: Instance-Conditioned Prompting with Contrastive Learning for Semantic Segmentation](https://arxiv.org/pdf/2308.07078.pdf)|arXiv 2023|-|
|[Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP](https://arxiv.org/pdf/2308.02487.pdf)|arXiv 2023|[Code](https://github.com/bytedance/fc-clip)|
|[Plug-and-Play, Dense-Label-Free Extraction of Open-Vocabulary Semantic Segmentation from Vision-Language Models](https://arxiv.org/abs/2311.17095)|arXiv 2023|-|
### Knowledge Distillation for Other Tasks
| Paper | Published in | Code/Project |
|---------------------------------------------------|:-------------:|:------------:|
|[Controlling Vision-Language Models for Universal Image Restoration](https://arxiv.org/abs/2310.01018)|arXiv 2023|[Code](https://github.com/Algolzw/daclip-uir)|