Two-Stream Convolutional Networks for Action Recognition in Videos

TL;DR

This paper introduces a two-stream ConvNet architecture for video action recognition, combining spatial and temporal information, and demonstrates its effectiveness on standard benchmarks with improved performance.

Contribution

The paper presents a novel two-stream ConvNet architecture that integrates spatial and temporal data, and employs multi-task learning to enhance action recognition performance.

Findings

Achieved competitive results on UCF-101 and HMDB-51 benchmarks.

ConvNet trained on dense optical flow performs well with limited data.

Multi-task learning improves accuracy across datasets.

Abstract

We investigate architectures of discriminatively trained deep Convolutional Networks (ConvNets) for action recognition in video. The challenge is to capture the complementary information on appearance from still frames and motion between frames. We also aim to generalise the best performing hand-crafted features within a data-driven learning framework. Our contribution is three-fold. First, we propose a two-stream ConvNet architecture which incorporates spatial and temporal networks. Second, we demonstrate that a ConvNet trained on multi-frame dense optical flow is able to achieve very good performance in spite of limited training data. Finally, we show that multi-task learning, applied to two different action classification datasets, can be used to increase the amount of training data and improve the performance on both. Our architecture is trained and evaluated on the standard video actions benchmarks of UCF-101 and HMDB-51, where it is competitive with the state of the art. It also exceeds by a large margin previous attempts to use deep nets for video classification.