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Few Shot Action Recognition
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Few-shot (FS) action recognition is a challenging com- puter vision problem, where the task is to classify an unlabelled query video into one of the action categories in the support set having limited samples per action class.
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Task-Adapter++: Task-specific Adaptation with Order-aware Alignment for Few-shot Action Recognition
Temporal Alignment-Free Video Matching for Few-shot Action Recognition
Joint Image-Instance Spatial-Temporal Attention for Few-shot Action Recognition
Manta: Enhancing Mamba for Few-Shot Action Recognition of Long Sub-Sequence
TAMT: Temporal-Aware Model Tuning for Cross-Domain Few-Shot Action Recognition
Video-to-Task Learning via Motion-Guided Attention for Few-Shot Action Recognition
Frame Order Matters: A Temporal Sequence-Aware Model for Few-Shot Action Recognition
Task-Adapter: Task-specific Adaptation of Image Models for Few-shot Action Recognition
Trajectory-aligned Space-time Tokens for Few-shot Action Recognition
SOAP: Enhancing Spatio-Temporal Relation and Motion Information Capturing for Few-Shot Action Recognition
A Comprehensive Review of Few-shot Action Recognition
DMSD-CDFSAR: Distillation from Mixed-Source Domain for Cross-Domain Few-shot Action Recognition
Understanding the Cross-Domain Capabilities of Video-Based Few-Shot Action Recognition Models
Few-Shot Classification of Interactive Activities of Daily Living (InteractADL)
MVP-Shot: Multi-Velocity Progressive-Alignment Framework for Few-Shot Action Recognition
Simultaneous Detection and Interaction Reasoning for Object-Centric Action Recognition
Learning Causal Domain-Invariant Temporal Dynamics for Few-Shot Action Recognition
Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment
Multi-view Distillation based on Multi-modal Fusion for Few-shot Action Recognition(CLIP-$\mathrm{M^2}$DF)
Two-stream joint matching method based on contrastive learning for few-shot action recognition
D$^2$ST-Adapter: Disentangled-and-Deformable Spatio-Temporal Adapter for Few-shot Action Recognition
Consistency Prototype Module and Motion Compensation for Few-Shot Action Recognition (CLIP-CP$\mathbf{M^2}$C)
Semantic-aware Video Representation for Few-shot Action Recognition
Few-shot Action Recognition with Captioning Foundation Models
CDFSL-V: Cross-Domain Few-Shot Learning for Videos
Boosting Few-shot Action Recognition with Graph-guided Hybrid Matching
On the Importance of Spatial Relations for Few-shot Action Recognition
MA-FSAR: Multimodal Adaptation of CLIP for Few-Shot Action Recognition
Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action Recognition
Few-shot Action Recognition via Intra- and Inter-Video Information Maximization
MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition
Rethinking matching-based few-shot action recognition
MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language Knowledge
CLIP-guided Prototype Modulating for Few-shot Action Recognition
Revisiting the Spatial and Temporal Modeling for Few-shot Action Recognition
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