Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification

Last update: Dec 6, 2022

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Machine Learning TAP

Overview

Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification

Introduction.

This package includes the python codes for implementing Temporal Alignment Prediction (TAP) in supervised learning and few-shot action recognition, described in

Bing Su and Ji-Rong Wen. "Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification" ICLR, 2022.

License & disclaimer.

The codes can be used for research purposes only. This package is strictly for non-commercial academic use only.
Usage

The "Supervised_Learning" folder contains codes for using TAP in supervised distance learning for sequence data.

The "Few_Shot_Learning" folder contains codes for using TAP in few-shot action recognition.

Please refer to the ReadMe files in the folds.
Citations

Please cite the following paper if you use the codes:

Bing Su and Ji-Rong Wen, "Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification", ICLR, 2022.

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Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification

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Overview

Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence Classification

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