# Boosting Few-shot Action Recognition with Graph-guided Hybrid Matching

arXiv: https://arxiv.org/abs/2308.09346 · HTML: https://mlsanity.com/paper/2308.09346/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 47 references that are in the map.
- First posted on arXiv 18 Aug 2023.

## Builds on (most cited first, 30 of 34)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [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)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [Is Space-Time Attention All You Need for Video Understanding?](https://mlsanity.com/paper/2102.05095/) (2021)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [The "something something" video database for learning and evaluating visual common sense](https://mlsanity.com/paper/1706.04261/) (2017)
- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [Multiscale Vision Transformers](https://mlsanity.com/paper/2104.11227/) (2021)
- [Meta-SGD: Learning to Learn Quickly for Few-Shot Learning](https://mlsanity.com/paper/1707.09835/) (2017)
- [Few-Shot Learning with Graph Neural Networks](https://mlsanity.com/paper/1711.04043/) (2017)
- [Temporal Relational Reasoning in Videos](https://mlsanity.com/paper/1711.08496/) (2017)
- [Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions](https://mlsanity.com/paper/1812.03664/) (2018)
- [CrossTransformers: spatially-aware few-shot transfer](https://mlsanity.com/paper/2007.11498/) (2020)
- [Few-Shot Video Classification via Temporal Alignment](https://mlsanity.com/paper/1906.11415/) (2019)
- [Edge-labeling Graph Neural Network for Few-shot Learning](https://mlsanity.com/paper/1905.01436/) (2019)
- [VidTr: Video Transformer Without Convolutions](https://mlsanity.com/paper/2104.11746/) (2021)
- [Finding Task-Relevant Features for Few-Shot Learning by Category Traversal](https://mlsanity.com/paper/1905.11116/) (2019)
- [TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning](https://mlsanity.com/paper/1905.06549/) (2019)
- [Temporal-Relational CrossTransformers for Few-Shot Action Recognition](https://mlsanity.com/paper/2101.06184/) (2021)
- [TARN: Temporal Attentive Relation Network for Few-Shot and Zero-Shot Action Recognition](https://mlsanity.com/paper/1907.09021/) (2019)
- [Spatio-temporal Relation Modeling for Few-shot Action Recognition](https://mlsanity.com/paper/2112.05132/) (2021)
- [Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition](https://mlsanity.com/paper/2010.09982/) (2020)
- [Hybrid Relation Guided Set Matching for Few-shot Action Recognition](https://mlsanity.com/paper/2204.13423/) (2022)
- [TA2N: Two-Stage Action Alignment Network for Few-shot Action Recognition](https://mlsanity.com/paper/2107.04782/) (2021)
- [Compound Prototype Matching for Few-shot Action Recognition](https://mlsanity.com/paper/2207.05515/) (2022)
- [Learning Implicit Temporal Alignment for Few-shot Video Classification](https://mlsanity.com/paper/2105.04823/) (2021)
- [Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments](https://mlsanity.com/paper/2207.10785/) (2022)
- [Spatiotemporal Self-attention Modeling with Temporal Patch Shift for Action Recognition](https://mlsanity.com/paper/2207.13259/) (2022)
- [Revisiting the Spatial and Temporal Modeling for Few-shot Action Recognition](https://mlsanity.com/paper/2301.07944/) (2023)

## Built on by (most cited first, 24 of 24)

- [MVP-Shot: Multi-Velocity Progressive-Alignment Framework for Few-Shot Action Recognition](https://mlsanity.com/paper/2405.02077/) (2024)
- [HIG: Hierarchical Interlacement Graph Approach to Scene Graph Generation in Video Understanding](https://mlsanity.com/paper/2312.03050/) (2023)
- [USDRL: Unified Skeleton-Based Dense Representation Learning with Multi-Grained Feature Decorrelation](https://mlsanity.com/paper/2412.09220/) (2024)
- [A Comprehensive Review of Few-shot Action Recognition](https://mlsanity.com/paper/2407.14744/) (2024)
- [Trajectory-aligned Space-time Tokens for Few-shot Action Recognition](https://mlsanity.com/paper/2407.18249/) (2024)
- [Temporal Alignment-Free Video Matching for Few-shot Action Recognition](https://mlsanity.com/paper/2504.05956/) (2025)
- [Few-shot Action Recognition with Captioning Foundation Models](https://mlsanity.com/paper/2310.10125/) (2023)
- [Trokens: Semantic-Aware Relational Trajectory Tokens for Few-Shot Action Recognition](https://mlsanity.com/paper/2508.03695/) (2025)
- [TAMT: Temporal-Aware Model Tuning for Cross-Domain Few-Shot Action Recognition](https://mlsanity.com/paper/2411.19041/) (2024)
- [Spatio-temporal Decoupled Knowledge Compensator for Few-Shot Action Recognition](https://mlsanity.com/paper/2602.18043/) (2026)
- [FedSC: Federated Learning with Semantic-Aware Collaboration](https://mlsanity.com/paper/2506.21012/) (2025)
- [Manta: Enhancing Mamba for Few-Shot Action Recognition of Long Sub-Sequence](https://mlsanity.com/paper/2412.07481/) (2024)
- [Joint Image-Instance Spatial-Temporal Attention for Few-shot Action Recognition](https://mlsanity.com/paper/2503.14430/) (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)
- [Hierarchical Relation-augmented Representation Generalization for Few-shot Action Recognition](https://mlsanity.com/paper/2504.10079/) (2025)
- [A2M2-Net: Adaptively Aligned Multi-Scale Moment for Few-Shot Action Recognition](https://mlsanity.com/paper/2509.17638/) (2025)
- [Otter: Mitigating Background Distractions of Wide-Angle Few-Shot Action Recognition with Enhanced RWKV](https://mlsanity.com/paper/2511.06741/) (2025)
- [Task-Specific Distance Correlation Matching for Few-Shot Action Recognition](https://mlsanity.com/paper/2512.11340/) (2025)
- [A Baseline Study and Benchmark for Few-Shot Open-Set Action Recognition with Feature Residual Discrimination](https://mlsanity.com/paper/2603.04125/) (2026)
- [Knowledge is Power: Advancing Few-shot Action Recognition with Multimodal Semantics from MLLMs](https://mlsanity.com/paper/2603.26033/) (2026)
- [Comparison Drives Preference: Reference-Aware Modeling for AI-Generated Video Quality Assessment](https://mlsanity.com/paper/2604.17074/) (2026)
- [Few-Shot Video Recognition via Hierarchical Metric Learning](https://mlsanity.com/paper/2609.05242/) (2026)

## Abstract (arXiv)

Class prototype construction and matching are core aspects of few-shot action recognition. Previous methods mainly focus on designing spatiotemporal relation modeling modules or complex temporal alignment algorithms. Despite the promising results, they ignored the value of class prototype construction and matching, leading to unsatisfactory performance in recognizing similar categories in every task. In this paper, we propose GgHM, a new framework with Graph-guided Hybrid Matching. Concretely, we learn task-oriented features by the guidance of a graph neural network during class prototype construction, optimizing the intra- and inter-class feature correlation explicitly. Next, we design a hybrid matching strategy, combining frame-level and tuple-level matching to classify videos with multivariate styles. We additionally propose a learnable dense temporal modeling module to enhance the video feature temporal representation to build a more solid foundation for the matching process. GgHM shows consistent improvements over other challenging baselines on several few-shot datasets, demonstrating the effectiveness of our method. The code will be publicly available at https://github.com/jiazheng-xing/GgHM.