Collection of AWESOME vision-language models for vision tasks
3,126
107 commits
updated Sep 16, 2026
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]
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024
🤩 Our paper is selected into TPAMI Top 50 Popular Paper List !!
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:
README.md using the following format: |[Title](Paper Link)|Conference|[Code/Project](Code/Project link)|
We plan to update the arXiv version of our survey paper soon. If your paper is missing from this repository, feel free to contact us or open an issue!
📅 Last update on 2025/10/14
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.
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}
}
| Dataset | Year | Num of Image-Text Paris | Language | Project |
|---|---|---|---|---|
| SBU Caption | 2011 | 1M | English | Project |
| COCO Caption | 2016 | 1.5M | English | Project |
| Yahoo Flickr Creative Commons 100 Million | 2016 | 100M | English | Project |
| Visual Genome | 2017 | 5.4M | English | Project |
| Conceptual Captions 3M | 2018 | 3.3M | English | Project |
| Localized Narratives | 2020 | 0.87M | English | Project |
| Conceptual 12M | 2021 | 12M | English | Project |
| Wikipedia-based Image Text | 2021 | 37.6M | 108 Languages | Project |
| Red Caps | 2021 | 12M | English | Project |
| LAION400M | 2021 | 400M | English | Project |
| LAION5B | 2022 | 5B | Over 100 Languages | Project |
| WuKong | 2022 | 100M | Chinese | Project |
| CLIP | 2021 | 400M | English | - |
| ALIGN | 2021 | 1.8B | English | - |
| FILIP | 2021 | 300M | English | - |
| WebLI | 2022 | 12B | English | - |
| Dataset | Year | Classes | Training | Testing | Evaluation Metric | Project |
|---|---|---|---|---|---|---|
| MNIST | 1998 | 10 | 60,000 | 10,000 | Accuracy | Project |
| Caltech-101 | 2004 | 102 | 3,060 | 6,085 | Mean Per Class | Project |
| PASCAL VOC 2007 | 2007 | 20 | 5,011 | 4,952 | 11-point mAP | Project |
| Oxford 102 Flowers | 2008 | 102 | 2,040 | 6,149 | Mean Per Class | Project |
| CIFAR-10 | 2009 | 10 | 50,000 | 10,000 | Accuracy | Project |
| CIFAR-100 | 2009 | 100 | 50,000 | 10,000 | Accuracy | Project |
| ImageNet-1k | 2009 | 1000 | 1,281,167 | 50,000 | Accuracy | Project |
| SUN397 | 2010 | 397 | 19,850 | 19,850 | Accuracy | Project |
| SVHN | 2011 | 10 | 73,257 | 26,032 | Accuracy | Project |
| STL-10 | 2011 | 10 | 1,000 | 8,000 | Accuracy | Project |
| GTSRB | 2011 | 43 | 26,640 | 12,630 | Accuracy | Project |
| KITTI Distance | 2012 | 4 | 6,770 | 711 | Accuracy | Project |
| IIIT5k | 2012 | 36 | 2,000 | 3,000 | Accuracy | Project |
| Oxford-IIIT PETS | 2012 | 37 | 3,680 | 3,669 | Mean Per Class | Project |
| Stanford Cars | 2013 | 196 | 8,144 | 8,041 | Accuracy | Project |
| FGVC Aircraft | 2013 | 100 | 6,667 | 3,333 | Mean Per Class | Project |
| Facial Emotion | 2013 | 8 | 32,140 | 3,574 | Accuracy | Project |
| Rendered SST2 | 2013 | 2 | 7,792 | 1,821 | Accuracy | Project |
| Describable Textures | 2014 | 47 | 3,760 | 1,880 | Accuracy | Project |
| Food-101 | 2014 | 101 | 75,750 | 25,250 | Accuracy | Project |
| Birdsnap | 2014 | 500 | 42,283 | 2,149 | Accuracy | Project |
| RESISC45 | 2017 | 45 | 3,150 | 25,200 | Accuracy | Project |
| CLEVR Counts | 2017 | 8 | 2,000 | 500 | Accuracy | Project |
| PatchCamelyon | 2018 | 2 | 294,912 | 32,768 | Accuracy | Project |
| EuroSAT | 2019 | 10 | 10,000 | 5,000 | Accuracy | Project |
| Hateful Memes | 2020 | 2 | 8,500 | 500 | ROC AUC | Project |
| Country211 | 2021 | 211 | 43,200 | 21,100 | Accuracy | Project |
| Dataset | Year | Classes | Training | Testing | Evaluation Metric | Project |
|---|---|---|---|---|---|---|
| Flickr30k | 2014 | - | 31,783 | - | Recall | Project |
| COCO Caption | 2015 | - | 82,783 | 5,000 | Recall | Project |
| Dataset | Year | Classes | Training | Testing | Evaluation Metric | Project |
|---|---|---|---|---|---|---|
| UCF101 | 2012 | 101 | 9,537 | 1,794 | Accuracy | Project |
| Kinetics700 | 2019 | 700 | 494,801 | 31,669 | Mean (top1, top5) | Project |
| RareAct | 2020 | 122 | 7,607 | - | mWAP, mSAP | Project |
| Dataset | Year | Classes | Training | Testing | Evaluation Metric | Project |
|---|---|---|---|---|---|---|
| COCO 2014 Detection | 2014 | 80 | 83,000 | 41,000 | Box mAP | Project |
| COCO 2017 Detection | 2017 | 80 | 118,000 | 5,000 | Box mAP | Project |
| LVIS | 2019 | 1203 | 118,000 | 5,000 | Box mAP | Project |
| ODinW | 2022 | 314 | 132,413 | 20,070 | Box mAP | Project |
| Dataset | Year | Classes | Training | Testing | Evaluation Metric | Project |
|---|---|---|---|---|---|---|
| PASCAL VOC 2012 | 2012 | 20 | 1,464 | 1,449 | mIoU | Project |
| PASCAL Content | 2014 | 459 | 4,998 | 5,105 | mIoU | Project |
| Cityscapes | 2016 | 19 | 2,975 | 500 | mIoU | Project |
| ADE20k | 2017 | 150 | 25,574 | 2,000 | mIoU | Project |
Collection of AWESOME vision-language models for vision tasks
3,126
107 commits
updated Sep 16, 2026
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]
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024
🤩 Our paper is selected into TPAMI Top 50 Popular Paper List !!
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:
README.md using the following format: |[Title](Paper Link)|Conference|[Code/Project](Code/Project link)|
We plan to update the arXiv version of our survey paper soon. If your paper is missing from this repository, feel free to contact us or open an issue!
📅 Last update on 2025/10/14
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.
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}
}
| Dataset | Year | Num of Image-Text Paris | Language | Project |
|---|---|---|---|---|
| SBU Caption | 2011 | 1M | English | Project |
| COCO Caption | 2016 | 1.5M | English | Project |
| Yahoo Flickr Creative Commons 100 Million | 2016 | 100M | English | Project |
| Visual Genome | 2017 | 5.4M | English | Project |
| Conceptual Captions 3M | 2018 | 3.3M | English | Project |
| Localized Narratives | 2020 | 0.87M | English | Project |
| Conceptual 12M | 2021 | 12M | English | Project |
| Wikipedia-based Image Text | 2021 | 37.6M | 108 Languages | Project |
| Red Caps | 2021 | 12M | English | Project |
| LAION400M | 2021 | 400M | English | Project |
| LAION5B | 2022 | 5B | Over 100 Languages | Project |
| WuKong | 2022 | 100M | Chinese | Project |
| CLIP | 2021 | 400M | English | - |
| ALIGN | 2021 | 1.8B | English | - |
| FILIP | 2021 | 300M | English | - |
| WebLI | 2022 | 12B | English | - |
| Dataset | Year | Classes | Training | Testing | Evaluation Metric | Project |
|---|---|---|---|---|---|---|
| MNIST | 1998 | 10 | 60,000 | 10,000 | Accuracy | Project |
| Caltech-101 | 2004 | 102 | 3,060 | 6,085 | Mean Per Class | Project |
| PASCAL VOC 2007 | 2007 | 20 | 5,011 | 4,952 | 11-point mAP | Project |
| Oxford 102 Flowers | 2008 | 102 | 2,040 | 6,149 | Mean Per Class | Project |
| CIFAR-10 | 2009 | 10 | 50,000 | 10,000 | Accuracy | Project |
| CIFAR-100 | 2009 | 100 | 50,000 | 10,000 | Accuracy | Project |
| ImageNet-1k | 2009 | 1000 | 1,281,167 | 50,000 | Accuracy | Project |
| SUN397 | 2010 | 397 | 19,850 | 19,850 | Accuracy | Project |
| SVHN | 2011 | 10 | 73,257 | 26,032 | Accuracy | Project |
| STL-10 | 2011 | 10 | 1,000 | 8,000 | Accuracy | Project |
| GTSRB | 2011 | 43 | 26,640 | 12,630 | Accuracy | Project |
| KITTI Distance | 2012 | 4 | 6,770 | 711 | Accuracy | Project |
| IIIT5k | 2012 | 36 | 2,000 | 3,000 | Accuracy | Project |
| Oxford-IIIT PETS | 2012 | 37 | 3,680 | 3,669 | Mean Per Class | Project |
| Stanford Cars | 2013 | 196 | 8,144 | 8,041 | Accuracy | Project |
| FGVC Aircraft | 2013 | 100 | 6,667 | 3,333 | Mean Per Class | Project |
| Facial Emotion | 2013 | 8 | 32,140 | 3,574 | Accuracy | Project |
| Rendered SST2 | 2013 | 2 | 7,792 | 1,821 | Accuracy | Project |
| Describable Textures | 2014 | 47 | 3,760 | 1,880 | Accuracy | Project |
| Food-101 | 2014 | 101 | 75,750 | 25,250 | Accuracy | Project |
| Birdsnap | 2014 | 500 | 42,283 | 2,149 | Accuracy | Project |
| RESISC45 | 2017 | 45 | 3,150 | 25,200 | Accuracy | Project |
| CLEVR Counts | 2017 | 8 | 2,000 | 500 | Accuracy | Project |
| PatchCamelyon | 2018 | 2 | 294,912 | 32,768 | Accuracy | Project |
| EuroSAT | 2019 | 10 | 10,000 | 5,000 | Accuracy | Project |
| Hateful Memes | 2020 | 2 | 8,500 | 500 | ROC AUC | Project |
| Country211 | 2021 | 211 | 43,200 | 21,100 | Accuracy | Project |
| Dataset | Year | Classes | Training | Testing | Evaluation Metric | Project |
|---|---|---|---|---|---|---|
| Flickr30k | 2014 | - | 31,783 | - | Recall | Project |
| COCO Caption | 2015 | - | 82,783 | 5,000 | Recall | Project |
| Dataset | Year | Classes | Training | Testing | Evaluation Metric | Project |
|---|---|---|---|---|---|---|
| UCF101 | 2012 | 101 | 9,537 | 1,794 | Accuracy | Project |
| Kinetics700 | 2019 | 700 | 494,801 | 31,669 | Mean (top1, top5) | Project |
| RareAct | 2020 | 122 | 7,607 | - | mWAP, mSAP | Project |
| Dataset | Year | Classes | Training | Testing | Evaluation Metric | Project |
|---|---|---|---|---|---|---|
| COCO 2014 Detection | 2014 | 80 | 83,000 | 41,000 | Box mAP | Project |
| COCO 2017 Detection | 2017 | 80 | 118,000 | 5,000 | Box mAP | Project |
| LVIS | 2019 | 1203 | 118,000 | 5,000 | Box mAP | Project |
| ODinW | 2022 | 314 | 132,413 | 20,070 | Box mAP | Project |
| Dataset | Year | Classes | Training | Testing | Evaluation Metric | Project |
|---|---|---|---|---|---|---|
| PASCAL VOC 2012 | 2012 | 20 | 1,464 | 1,449 | mIoU | Project |
| PASCAL Content | 2014 | 459 | 4,998 | 5,105 | mIoU | Project |
| Cityscapes | 2016 | 19 | 2,975 | 500 | mIoU | Project |
| ADE20k | 2017 | 150 | 25,574 | 2,000 | mIoU | Project |