Spatio-temporal Relation Modeling for Few-shot Action Recognition

Anirudh Thatipelli, Sanath Narayan, Salman Khan, Rao Muhammad Anwer, Fahad Shahbaz Khan, Bernard Ghanem

Research output: Chapter in Book/Report/Conference proceedingConference contribution

46 Scopus citations


We propose a novel few-shot action recognition framework, STRM, which enhances class-specific feature discrim-inability while simultaneously learning higher-order temporal representations. The focus of our approach is a novel spatio-temporal enrichment module that aggregates spatial and temporal contexts with dedicated local patch-level and global frame-level feature enrichment sub-modules. Local patch-level enrichment captures the appearance-based characteristics of actions. On the other hand, global framelevel enrichment explicitly encodes the broad temporal context, thereby capturing the relevant object features over time. The resulting spatio-temporally enriched representations are then utilized to learn the relational matching between query and support action sub-sequences. We further introduce a query-class similarity classifier on the patch-level enriched features to enhance class-specific feature dis-criminability by reinforcing the feature learning at different stages in the proposed framework. Experiments are performed on four few-shot action recognition benchmarks: Kinetics, SSv2, HMDB51 and UCF101. Our extensive ablation study reveals the benefits of the proposed contributions. Furthermore, our approach sets a new state-of-the-art on all four benchmarks. On the challenging SSv2 benchmark, our approach achieves an absolute gain of 3.5% in classification accuracy, as compared to the best existing method in the literature. Our code and models are available at
Original languageEnglish (US)
Title of host publication2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Number of pages10
ISBN (Print)9781665469463
StatePublished - Sep 27 2022

Bibliographical note

KAUST Repository Item: Exported on 2023-01-09
Acknowledgements: This work was partially supported by VR starting grant (2016-05543), in addition to the compute support provided at the Swedish National Infrastructure for Computing (SNIC), partially funded by the Swedish Research Council through grant agreement 2018-05973.


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