# TAEN: Temporal Aware Embedding Network for Few-Shot Action Recognition

arXiv: https://arxiv.org/abs/2004.10141 · HTML: https://mlsanity.com/paper/2004.10141/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2020.

## Built on by (most cited first, 12 of 12)

- [TA2N: Two-Stage Action Alignment Network for Few-shot Action Recognition](https://mlsanity.com/paper/2107.04782/) (2021)
- [Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition](https://mlsanity.com/paper/2109.01305/) (2021)
- [MVP-Shot: Multi-Velocity Progressive-Alignment Framework for Few-Shot Action Recognition](https://mlsanity.com/paper/2405.02077/) (2024)
- [Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments](https://mlsanity.com/paper/2207.10785/) (2022)
- [Uncertainty-DTW for Time Series and Sequences](https://mlsanity.com/paper/2211.00005/) (2022)
- [Few-Shot Action Recognition with Compromised Metric via Optimal Transport](https://mlsanity.com/paper/2104.03737/) (2021)
- [Hierarchical Compositional Representations for Few-shot Action Recognition](https://mlsanity.com/paper/2208.09424/) (2022)
- [Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition](https://mlsanity.com/paper/2209.10073/) (2022)
- [Knowledge Prompting for Few-shot Action Recognition](https://mlsanity.com/paper/2211.12030/) (2022)
- [Collaborative Weakly Supervised Video Correlation Learning for Procedure-Aware Instructional Video Analysis](https://mlsanity.com/paper/2312.11024/) (2023)
- [Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning](https://mlsanity.com/paper/2112.06320/) (2021)
- [Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action Recognition](https://mlsanity.com/paper/2307.01985/) (2023)

## Abstract (arXiv)

Classification of new class entities requires collecting and annotating hundreds or thousands of samples that is often prohibitively costly. Few-shot learning suggests learning to classify new classes using just a few examples. Only a small number of studies address the challenge of few-shot learning on spatio-temporal patterns such as videos. In this paper, we present the Temporal Aware Embedding Network (TAEN) for few-shot action recognition, that learns to represent actions, in a metric space as a trajectory, conveying both short term semantics and longer term connectivity between action parts. We demonstrate the effectiveness of TAEN on two few shot tasks, video classification and temporal action detection and evaluate our method on the Kinetics-400 and on ActivityNet 1.2 few-shot benchmarks. With training of just a few fully connected layers we reach comparable results to prior art on both few shot video classification and temporal detection tasks, while reaching state-of-the-art in certain scenarios.