# Few-shot Action Recognition with Implicit Temporal Alignment and Pair Similarity Optimization

arXiv: https://arxiv.org/abs/2010.06215 · HTML: https://mlsanity.com/paper/2010.06215/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 18 of 18)

- [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)
- [M3Net: Multi-view Encoding, Matching, and Fusion for Few-shot Fine-grained Action Recognition](https://mlsanity.com/paper/2308.03063/) (2023)
- [Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments](https://mlsanity.com/paper/2207.10785/) (2022)
- [A Closer Look at Few-Shot Video Classification: A New Baseline and Benchmark](https://mlsanity.com/paper/2110.12358/) (2021)
- [Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse Occlusions](https://mlsanity.com/paper/2202.11423/) (2022)
- [Few-Shot Action Recognition with Compromised Metric via Optimal Transport](https://mlsanity.com/paper/2104.03737/) (2021)
- [Unsupervised Few-Shot Action Recognition via Action-Appearance Aligned Meta-Adaptation](https://mlsanity.com/paper/2109.15317/) (2021)
- [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)
- [Enhancing Trust in LLMs: Algorithms for Comparing and Interpreting LLMs](https://mlsanity.com/paper/2406.01943/) (2024)
- [Task-Adapter: Task-specific Adaptation of Image Models for Few-shot Action Recognition](https://mlsanity.com/paper/2408.00249/) (2024)
- [An Efficient Framework for Few-shot Skeleton-based Temporal Action Segmentation](https://mlsanity.com/paper/2207.09925/) (2022)
- [TAMT: Temporal-Aware Model Tuning for Cross-Domain Few-Shot Action Recognition](https://mlsanity.com/paper/2411.19041/) (2024)
- [Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning](https://mlsanity.com/paper/2112.06320/) (2021)
- [Joint Image-Instance Spatial-Temporal Attention for Few-shot Action Recognition](https://mlsanity.com/paper/2503.14430/) (2025)
- [Localization-Aware Multi-Scale Representation Learning for Repetitive Action Counting](https://mlsanity.com/paper/2501.07312/) (2025)
- [A2M2-Net: Adaptively Aligned Multi-Scale Moment for Few-Shot Action Recognition](https://mlsanity.com/paper/2509.17638/) (2025)

## Abstract (arXiv)

Few-shot learning aims to recognize instances from novel classes with few labeled samples, which has great value in research and application. Although there has been a lot of work in this area recently, most of the existing work is based on image classification tasks. Video-based few-shot action recognition has not been explored well and remains challenging: 1) the differences of implementation details among different papers make a fair comparison difficult; 2) the wide variations and misalignment of temporal sequences make the video-level similarity comparison difficult; 3) the scarcity of labeled data makes the optimization difficult. To solve these problems, this paper presents 1) a specific setting to evaluate the performance of few-shot action recognition algorithms; 2) an implicit sequence-alignment algorithm for better video-level similarity comparison; 3) an advanced loss for few-shot learning to optimize pair similarity with limited data. Specifically, we propose a novel few-shot action recognition framework that uses long short-term memory following 3D convolutional layers for sequence modeling and alignment. Circle loss is introduced to maximize the within-class similarity and minimize the between-class similarity flexibly towards a more definite convergence target. Instead of using random or ambiguous experimental settings, we set a concrete criterion analogous to the standard image-based few-shot learning setting for few-shot action recognition evaluation. Extensive experiments on two datasets demonstrate the effectiveness of our proposed method.