# Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition

arXiv: https://arxiv.org/abs/2010.09982 · HTML: https://mlsanity.com/paper/2010.09982/

## Key facts

- Cited by 50 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 30 of 50)

- [StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2302.09309/) (2023)
- [Hybrid Relation Guided Set Matching for Few-shot Action Recognition](https://mlsanity.com/paper/2204.13423/) (2022)
- [Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data](https://mlsanity.com/paper/2107.11978/) (2021)
- [Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector](https://mlsanity.com/paper/2402.03094/) (2024)
- [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)
- [Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment](https://mlsanity.com/paper/2210.01439/) (2022)
- [ObjectRelator: Enabling Cross-View Object Relation Understanding Across Ego-Centric and Exo-Centric Perspectives](https://mlsanity.com/paper/2411.19083/) (2024)
- [MVP-Shot: Multi-Velocity Progressive-Alignment Framework for Few-Shot Action Recognition](https://mlsanity.com/paper/2405.02077/) (2024)
- [Boosting Few-shot Action Recognition with Graph-guided Hybrid Matching](https://mlsanity.com/paper/2308.09346/) (2023)
- [Inductive and Transductive Few-Shot Video Classification via Appearance and Temporal Alignments](https://mlsanity.com/paper/2207.10785/) (2022)
- [EgoNight: Towards Egocentric Vision Understanding at Night with a Challenging Benchmark](https://mlsanity.com/paper/2510.06218/) (2025)
- [TGDM: Target Guided Dynamic Mixup for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2210.05392/) (2022)
- [V2-SAM: Marrying SAM2 with Multi-Prompt Experts for Cross-View Object Correspondence](https://mlsanity.com/paper/2511.20886/) (2025)
- [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)
- [Trajectory-aligned Space-time Tokens for Few-shot Action Recognition](https://mlsanity.com/paper/2407.18249/) (2024)
- [Frame Order Matters: A Temporal Sequence-Aware Model for Few-Shot Action Recognition](https://mlsanity.com/paper/2408.12475/) (2024)
- [ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning](https://mlsanity.com/paper/2210.05280/) (2022)
- [Task-Adapter: Task-specific Adaptation of Image Models for Few-shot Action Recognition](https://mlsanity.com/paper/2408.00249/) (2024)
- [Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning](https://mlsanity.com/paper/2108.06647/) (2021)
- [Few-shot Action Recognition via Intra- and Inter-Video Information Maximization](https://mlsanity.com/paper/2305.06114/) (2023)
- [Semantic-aware Video Representation for Few-shot Action Recognition](https://mlsanity.com/paper/2311.06218/) (2023)
- [Trokens: Semantic-Aware Relational Trajectory Tokens for Few-Shot Action Recognition](https://mlsanity.com/paper/2508.03695/) (2025)
- [Consistency Prototype Module and Motion Compensation for Few-Shot Action Recognition (CLIP-CPM2C)](https://mlsanity.com/paper/2312.01083/) (2023)
- [Learning Causal Domain-Invariant Temporal Dynamics for Few-Shot Action Recognition](https://mlsanity.com/paper/2402.12706/) (2024)

## Abstract (arXiv)

Humans can easily recognize actions with only a few examples given, while the existing video recognition models still heavily rely on the large-scale labeled data inputs. This observation has motivated an increasing interest in few-shot video action recognition, which aims at learning new actions with only very few labeled samples. In this paper, we propose a depth guided Adaptive Meta-Fusion Network for few-shot video recognition which is termed as AMeFu-Net. Concretely, we tackle the few-shot recognition problem from three aspects: firstly, we alleviate this extremely data-scarce problem by introducing depth information as a carrier of the scene, which will bring extra visual information to our model; secondly, we fuse the representation of original RGB clips with multiple non-strictly corresponding depth clips sampled by our temporal asynchronization augmentation mechanism, which synthesizes new instances at feature-level; thirdly, a novel Depth Guided Adaptive Instance Normalization (DGAdaIN) fusion module is proposed to fuse the two-stream modalities efficiently. Additionally, to better mimic the few-shot recognition process, our model is trained in the meta-learning way. Extensive experiments on several action recognition benchmarks demonstrate the effectiveness of our model.