Few-Shot action recognition
23 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Few-Shot action recognition
Most implemented papers
Spatio-temporal Relation Modeling for Few-shot Action Recognition
Experiments are performed on four few-shot action recognition benchmarks: Kinetics, SSv2, HMDB51 and UCF101.
Multi-level Second-order Few-shot Learning
The goal of multi-level feature design is to extract feature representations at different layer-wise levels of CNN, realizing several levels of visual abstraction to achieve robust few-shot learning.
Task-adaptive Spatial-Temporal Video Sampler for Few-shot Action Recognition
In this paper, we propose a novel video frame sampler for few-shot action recognition to address this issue, where task-specific spatial-temporal frame sampling is achieved via a temporal selector (TS) and a spatial amplifier (SA).
Uncertainty-DTW for Time Series and Sequences
leiwangr/udtw
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Dynamic Time Warping (DTW) is used for matching pairs of sequences and celebrated in applications such as forecasting the evolution of time series, clustering time series or even matching sequence pairs in few-shot action recognition.
TempCLR: Temporal Alignment Representation with Contrastive Learning
yyuncong/tempclr
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For long videos, given a paragraph of description where the sentences describe different segments of the video, by matching all sentence-clip pairs, the paragraph and the full video are aligned implicitly.
HyRSM++: Hybrid Relation Guided Temporal Set Matching for Few-shot Action Recognition
alibaba-mmai-research/hyrsmplusplus
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To be specific, HyRSM++ consists of two key components, a hybrid relation module and a temporal set matching metric.
CLIP-guided Prototype Modulating for Few-shot Action Recognition
alibaba-mmai-research/clip-fsar
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Learning from large-scale contrastive language-image pre-training like CLIP has shown remarkable success in a wide range of downstream tasks recently, but it is still under-explored on the challenging few-shot action recognition (FSAR) task.
MAtch, eXpand and Improve: Unsupervised Finetuning for Zero-Shot Action Recognition with Language Knowledge
We adapt a VL model for zero-shot and few-shot action recognition using a collection of unlabeled videos and an unpaired action dictionary.
MoLo: Motion-augmented Long-short Contrastive Learning for Few-shot Action Recognition
alibaba-mmai-research/molo
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CVPR 2023
To address these issues, we develop a Motion-augmented Long-short Contrastive Learning (MoLo) method that contains two crucial components, including a long-short contrastive objective and a motion autodecoder.
Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action Recognition
The second module (MLT) focuses on the Multiple-level feature of the support prototype and query sample to mine more information for the alignment, which operates on different level features.
