Deep RNN Framework for Visual Sequential Applications

TL;DR

This paper introduces a deep RNN framework with novel modules and training schemes that significantly improve performance on visual sequence tasks, demonstrating ease of training and substantial accuracy gains.

Contribution

The paper proposes a new deep RNN architecture with the Context Bridge Module and Overlap Coherence Training Scheme, enabling effective training of very deep RNNs for visual sequences.

Findings

Achieves over 11% relative improvement in video classification accuracy.

Improves auxiliary annotation performance by 14.7%.

Enhances video prediction metrics by 2.4% on PSNR and SSIM.

Abstract

Extracting temporal and representation features efficiently plays a pivotal role in understanding visual sequence information. To deal with this, we propose a new recurrent neural framework that can be stacked deep effectively. There are mainly two novel designs in our deep RNN framework: one is a new RNN module called Context Bridge Module (CBM) which splits the information flowing along the sequence (temporal direction) and along depth (spatial representation direction), making it easier to train when building deep by balancing these two directions; the other is the Overlap Coherence Training Scheme that reduces the training complexity for long visual sequential tasks on account of the limitation of computing resources. We provide empirical evidence to show that our deep RNN framework is easy to optimize and can gain accuracy from the increased depth on several visual sequence problems. On these tasks, we evaluate our deep RNN framework with 15 layers, 7* than conventional RNN networks, but it is still easy to train. Our deep framework achieves more than 11% relative improvements over shallow RNN models on Kinetics, UCF-101, and HMDB-51 for video classification. For auxiliary annotation, after replacing the shallow RNN part of Polygon-RNN with our 15-layer deep CBM, the performance improves by 14.7%. For video future prediction, our deep RNN improves the state-of-the-art shallow model's performance by 2.4% on PSNR and SSIM. The code and trained models are published accompanied by this paper: https://github.com/BoPang1996/Deep-RNN-Framework.