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[CVPR 2023] Official repository of paper titled "Fine-tuned CLIP models are efficient video learners".
| Date | Stars |
|---|---|
| 2026-07-31 | 309 |
| 2026-08-02 | 309 |
| 2026-08-06 | 309 |
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# Fine-tuned CLIP models are efficient video learners [CVPR 2023] > [**Fine-tuned CLIP models are efficient video learners**](https://arxiv.org/abs/2212.03640)<br> > [Hanoona Rasheed*](https://scholar.google.com/citations?user=yhDdEuEAAAAJ&hl=en&authuser=1&oi=sra), [Muhammad Uzair Khattak*](https://scholar.google.com/citations?user=M6fFL4gAAAAJ&hl=en&authuser=1), [Muhammad Maaz](https://scholar.google.com/citations?user=vTy9Te8AAAAJ&hl=en&authuser=1&oi=sra), [Salman Khan](https://salman-h-khan.github.io/), [Fahad Shahbaz Khan](https://scholar.google.es/citations?user=zvaeYnUAAAAJ&hl=en) *Equally contributing first authors [](https://muzairkhattak.github.io/ViFi-CLIP/) [](https://arxiv.org/abs/2212.03640) [](https://www.youtube.com/watch?v=uqPLPIyWBb0) [](https://drive.google.com/file/d/1_CITKY9u_Fh77iqQDP2TrcbVD5_61ArT/view?usp=sharing) [](https://github.com/muzairkhattak/ViFi-CLIP/blob/main/ViFi-CLIP_Inference_custom_video.ipynb) Official implementation of the paper "[Fine-tuned CLIP models are efficient video learners](https://arxiv.org/abs/2212.03640)". <hr /> [//]: # ([](https://paperswithcode.com/sota/prompt-engineering-on-imagenet?p=maple-multi-modal-prompt-learning)) [//]: # ([](https://paperswithcode.com/sota/prompt-engineering-on-sun397?p=maple-multi-modal-prompt-learning)) [//]: # ([](https://paperswithcode.com/sota/prompt-engineering-on-eurosat?p=maple-multi-modal-prompt-learning)) [//]: # ([](https://paperswithcode.com/sota/prompt-engineering-on-ucf101?p=maple-multi-modal-prompt-learning)) [//]: # ([](https://paperswithcode.com/sota/prompt-engineering-on-fgvc-aircraft?p=maple-multi-modal-prompt-learning)) [//]: # () [//]: # () [//]: # (<hr />) # :rocket: News * **(Nov 24, 2023)** * Interactive notebook released. Inference with ViFi-CLIP on custom videos without significant installation dependencies! * **(Feb 28, 2023)** * Paper accepted at CVPR 2023 :tada: * **(Dec 6, 2022)** * Training and evaluation codes for [ViFi-CLIP](https://arxiv.org/abs/2212.03640), along with pretrained models are released. <hr /> ## Highlights  <p align="justify"> This work explores the capability of a simple baseline called ViFi-CLIP (Video Fine-tuned CLIP) for adapting image pretrained CLIP to video domain. The figure compares the zero-shot performance of vanilla CLIP and several of its variants adapted for videos (trained on Kinetics-400, evaluated on UCF-101 and HMDB-51). The t-SNE visualizations of video-embeddings obtained from ViFi-CLIP (4th col.) are compared with embeddings from vanilla CLIP (1st col.), individually tuned CLIP text (2nd col.) and image encoder (3rd col.) on videos, and recent state-of-the-art work, XCLIP (last col.) (∆ represents difference over XCLIP). The embeddings of ViFi-CLIP are better se
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matched fp:333f574ded63b52a, llm:Repository description: 'Fine-tuned CLIP models are efficient video learners' (CVPR 2023) — official code.
matched fp:333f574ded63b52a, llm:Repository description: 'Fine-tuned CLIP models are efficient video learners' (CVPR 2023) — official code.
matched fp:333f574ded63b52a, llm:Repository description: 'Fine-tuned CLIP models are efficient video learners' (CVPR 2023) — official code.