# CLIP-guided Prototype Modulating for Few-shot Action Recognition

arXiv: https://arxiv.org/abs/2303.02982 · HTML: https://mlsanity.com/paper/2303.02982/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 72 of its 87 references that are in the map.
- First posted on arXiv 6 Mar 2023.

## Builds on (most cited first, 30 of 72)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](https://mlsanity.com/paper/1901.02860/) (2019)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Align before Fuse: Vision and Language Representation Learning with Momentum Distillation](https://mlsanity.com/paper/2107.07651/) (2021)
- [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://mlsanity.com/paper/2004.06165/) (2020)
- [Learning to Compare: Relation Network for Few-Shot Learning](https://mlsanity.com/paper/1711.06025/) (2017)
- [The "something something" video database for learning and evaluating visual common sense](https://mlsanity.com/paper/1706.04261/) (2017)
- [CLIP-Adapter: Better Vision-Language Models with Feature Adapters](https://mlsanity.com/paper/2110.04544/) (2021)
- [Open-vocabulary Object Detection via Vision and Language Knowledge Distillation](https://mlsanity.com/paper/2104.13921/) (2021)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [Language-driven Semantic Segmentation](https://mlsanity.com/paper/2201.03546/) (2022)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [RegionCLIP: Region-based Language-Image Pretraining](https://mlsanity.com/paper/2112.09106/) (2021)
- [LiT: Zero-Shot Transfer with Locked-image text Tuning](https://mlsanity.com/paper/2111.07991/) (2021)
- [DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting](https://mlsanity.com/paper/2112.01518/) (2021)

## Built on by (most cited first, 30 of 41)

- [UniAnimate: Taming Unified Video Diffusion Models for Consistent Human Image Animation](https://mlsanity.com/paper/2406.01188/) (2024)
- [GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graph](https://mlsanity.com/paper/2309.13625/) (2023)
- [Holmes-VAD: Towards Unbiased and Explainable Video Anomaly Detection via Multi-modal LLM](https://mlsanity.com/paper/2406.12235/) (2024)
- [MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition](https://mlsanity.com/paper/2304.00946/) (2023)
- [MVP-Shot: Multi-Velocity Progressive-Alignment Framework for Few-Shot Action Recognition](https://mlsanity.com/paper/2405.02077/) (2024)
- [BoostAdapter: Improving Vision-Language Test-Time Adaptation via Regional Bootstrapping](https://mlsanity.com/paper/2410.15430/) (2024)
- [A Comprehensive Review of Few-shot Action Recognition](https://mlsanity.com/paper/2407.14744/) (2024)
- [Few-shot Action Recognition with Captioning Foundation Models](https://mlsanity.com/paper/2310.10125/) (2023)
- [Task-Adapter: Task-specific Adaptation of Image Models for Few-shot Action Recognition](https://mlsanity.com/paper/2408.00249/) (2024)
- [Trokens: Semantic-Aware Relational Trajectory Tokens for Few-Shot Action Recognition](https://mlsanity.com/paper/2508.03695/) (2025)
- [Using Left and Right Brains Together: Towards Vision and Language Planning](https://mlsanity.com/paper/2402.10534/) (2024)
- [MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition](https://mlsanity.com/paper/2308.01532/) (2023)
- [Consistency Prototype Module and Motion Compensation for Few-Shot Action Recognition (CLIP-CPM2C)](https://mlsanity.com/paper/2312.01083/) (2023)
- [DMSD-CDFSAR: Distillation from Mixed-Source Domain for Cross-Domain Few-shot Action Recognition](https://mlsanity.com/paper/2407.05657/) (2024)
- [Storyboard guided Alignment for Fine-grained Video Action Recognition](https://mlsanity.com/paper/2410.14238/) (2024)
- [TAMT: Temporal-Aware Model Tuning for Cross-Domain Few-Shot Action Recognition](https://mlsanity.com/paper/2411.19041/) (2024)
- [DMPT: Decoupled Modality-aware Prompt Tuning for Multi-modal Object Re-identification](https://mlsanity.com/paper/2504.10985/) (2025)
- [Spatio-temporal Decoupled Knowledge Compensator for Few-Shot Action Recognition](https://mlsanity.com/paper/2602.18043/) (2026)
- [SWAP: Towards Copyright Auditing of Soft Prompts via Sequential Watermarking](https://mlsanity.com/paper/2511.04711/) (2025)
- [Multi-view Distillation based on Multi-modal Fusion for Few-shot Action Recognition(CLIP-M2DF)](https://mlsanity.com/paper/2401.08345/) (2024)
- [Continual Test-Time Adaptation for Single Image Defocus Deblurring via Causal Siamese Networks](https://mlsanity.com/paper/2501.09052/) (2025)
- [Adaptive Prototype Model for Attribute-based Multi-label Few-shot Action Recognition](https://mlsanity.com/paper/2502.12582/) (2025)
- [Joint Image-Instance Spatial-Temporal Attention for Few-shot Action Recognition](https://mlsanity.com/paper/2503.14430/) (2025)
- [VTD-CLIP: Video-to-Text Discretization via Prompting CLIP](https://mlsanity.com/paper/2503.18407/) (2025)
- [Beyond Label Semantics: Language-Guided Action Anatomy for Few-shot Action Recognition](https://mlsanity.com/paper/2507.16287/) (2025)
- [D2ST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action Recognition](https://mlsanity.com/paper/2312.01431/) (2023)
- [SOAP: Enhancing Spatio-Temporal Relation and Motion Information Capturing for Few-Shot Action Recognition](https://mlsanity.com/paper/2407.16344/) (2024)
- [Few-shot Class-Incremental Learning via Generative Co-Memory Regularization](https://mlsanity.com/paper/2601.07117/) (2026)
- [Big-model Driven Few-shot Continual Learning](https://mlsanity.com/paper/2309.00862/) (2023)
- [Video-to-Task Learning via Motion-Guided Attention for Few-Shot Action Recognition](https://mlsanity.com/paper/2411.11335/) (2024)

## Abstract (arXiv)

Learning from large-scale contrastive language-image pre-training like CLIP has shown remarkable success in a wide range of downstream tasks recently, but it is still under-explored on the challenging few-shot action recognition (FSAR) task. In this work, we aim to transfer the powerful multimodal knowledge of CLIP to alleviate the inaccurate prototype estimation issue due to data scarcity, which is a critical problem in low-shot regimes. To this end, we present a CLIP-guided prototype modulating framework called CLIP-FSAR, which consists of two key components: a video-text contrastive objective and a prototype modulation. Specifically, the former bridges the task discrepancy between CLIP and the few-shot video task by contrasting videos and corresponding class text descriptions. The latter leverages the transferable textual concepts from CLIP to adaptively refine visual prototypes with a temporal Transformer. By this means, CLIP-FSAR can take full advantage of the rich semantic priors in CLIP to obtain reliable prototypes and achieve accurate few-shot classification. Extensive experiments on five commonly used benchmarks demonstrate the effectiveness of our proposed method, and CLIP-FSAR significantly outperforms existing state-of-the-art methods under various settings. The source code and models will be publicly available at https://github.com/alibaba-mmai-research/CLIP-FSAR.