# ProtoGAN: Towards Few Shot Learning for Action Recognition

arXiv: https://arxiv.org/abs/1909.07945 · HTML: https://mlsanity.com/paper/1909.07945/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

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

- [Prompting Visual-Language Models for Efficient Video Understanding](https://mlsanity.com/paper/2112.04478/) (2021)
- [Temporal-Relational CrossTransformers for Few-Shot Action Recognition](https://mlsanity.com/paper/2101.06184/) (2021)
- [StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2302.09309/) (2023)
- [Compound Prototype Matching for Few-shot Action Recognition](https://mlsanity.com/paper/2207.05515/) (2022)
- [Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting](https://mlsanity.com/paper/2108.08165/) (2021)
- [Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition](https://mlsanity.com/paper/2109.01305/) (2021)
- [Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments](https://mlsanity.com/paper/2207.10785/) (2022)
- [Temporal-Viewpoint Transportation Plan for Skeletal Few-shot Action Recognition](https://mlsanity.com/paper/2210.16820/) (2022)
- [Use Your Head: Improving Long-Tail Video Recognition](https://mlsanity.com/paper/2304.01143/) (2023)
- [Few-Shot Learning with Class Imbalance](https://mlsanity.com/paper/2101.02523/) (2021)
- [Multimodal Prototype-Enhanced Network for Few-Shot Action Recognition](https://mlsanity.com/paper/2212.04873/) (2022)
- [Few-Shot Action Recognition with Compromised Metric via Optimal Transport](https://mlsanity.com/paper/2104.03737/) (2021)
- [HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action Recognition](https://mlsanity.com/paper/2301.03330/) (2023)
- [Unsupervised Few-Shot Action Recognition via Action-Appearance Aligned Meta-Adaptation](https://mlsanity.com/paper/2109.15317/) (2021)
- [On the Importance of Spatial Relations for Few-shot Action Recognition](https://mlsanity.com/paper/2308.07119/) (2023)
- [A Comprehensive Review of Few-shot Action Recognition](https://mlsanity.com/paper/2407.14744/) (2024)
- [3D Skeleton-based Few-shot Action Recognition with JEANIE is not so Na"ive](https://mlsanity.com/paper/2112.12668/) (2021)
- [Hierarchical Compositional Representations for Few-shot Action Recognition](https://mlsanity.com/paper/2208.09424/) (2022)
- [Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment](https://mlsanity.com/paper/2402.04599/) (2024)
- [Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition](https://mlsanity.com/paper/2209.10073/) (2022)
- [ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification](https://mlsanity.com/paper/2101.11753/) (2021)
- [MASTAF: A Model-Agnostic Spatio-Temporal Attention Fusion Network for Few-shot Video Classification](https://mlsanity.com/paper/2112.04585/) (2021)
- [Few-shot Action Recognition via Intra- and Inter-Video Information Maximization](https://mlsanity.com/paper/2305.06114/) (2023)
- [Few Shot Activity Recognition Using Variational Inference](https://mlsanity.com/paper/2108.08990/) (2021)
- [Instance-level Few-shot Learning with Class Hierarchy Mining](https://mlsanity.com/paper/2304.07459/) (2023)
- [Federated Action Recognition on Heterogeneous Embedded Devices](https://mlsanity.com/paper/2107.12147/) (2021)
- [Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Study](https://mlsanity.com/paper/2202.09545/) (2022)
- [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)
- [Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning](https://mlsanity.com/paper/2112.06320/) (2021)

## Abstract (arXiv)

Few-shot learning (FSL) for action recognition is a challenging task of recognizing novel action categories which are represented by few instances in the training data. In a more generalized FSL setting (G-FSL), both seen as well as novel action categories need to be recognized. Conventional classifiers suffer due to inadequate data in FSL setting and inherent bias towards seen action categories in G-FSL setting. In this paper, we address this problem by proposing a novel ProtoGAN framework which synthesizes additional examples for novel categories by conditioning a conditional generative adversarial network with class prototype vectors. These class prototype vectors are learnt using a Class Prototype Transfer Network (CPTN) from examples of seen categories. Our synthesized examples for a novel class are semantically similar to real examples belonging to that class and is used to train a model exhibiting better generalization towards novel classes. We support our claim by performing extensive experiments on three datasets: UCF101, HMDB51 and Olympic-Sports. To the best of our knowledge, we are the first to report the results for G-FSL and provide a strong benchmark for future research. We also outperform the state-of-the-art method in FSL for all the aforementioned datasets.