Relaxed Spatio-Temporal Deep Feature Aggregation for Real-Fake Expression Prediction

TL;DR

This paper introduces a learnable spatio-temporal feature aggregation method that improves real-fake expression prediction by capturing short-term temporal and spatial dependencies, outperforming existing techniques.

Contribution

The proposed method uniquely retains short-time temporal structure and spatial interdependencies in video features, and is adaptable to scarce training data scenarios.

Findings

Achieved 65% MAP score on real-fake expression dataset

Outperformed previous methods with only one misclassification

Set a new state-of-the-art result in Chalearn Challenge

Abstract

Frame-level visual features are generally aggregated in time with the techniques such as LSTM, Fisher Vectors, NetVLAD etc. to produce a robust video-level representation. We here introduce a learnable aggregation technique whose primary objective is to retain short-time temporal structure between frame-level features and their spatial interdependencies in the representation. Also, it can be easily adapted to the cases where there have very scarce training samples. We evaluate the method on a real-fake expression prediction dataset to demonstrate its superiority. Our method obtains 65% score on the test dataset in the official MAP evaluation and there is only one misclassified decision with the best reported result in the Chalearn Challenge (i.e. 66:7%) . Lastly, we believe that this method can be extended to different problems such as action/event recognition in future.