ConvTransformer: A Convolutional Transformer Network for Video Frame Synthesis

Abstract

Deep Convolutional Neural Networks (CNNs) are powerful models that have achieved excellent performance on difficult computer vision tasks. Although CNNs perform well whenever large labeled training samples are available, they work badly on video frame synthesis due to objects deforming and moving, scene lighting changes, and cameras moving in video sequence. In this paper, we present a novel and general end-to-end architecture, called convolutional Transformer or ConvTransformer, for video frame sequence learning and video frame synthesis. The core ingredient of ConvTransformer is the proposed attention layer, i.e., multi-head convolutional self-attention layer, that learns the sequential dependence of video sequence. ConvTransformer uses an encoder, built upon multi-head convolutional self-attention layer, to encode the sequential dependence between the input frames, and then a decoder decodes the long-term dependence between the target synthesized frames and the input frames. Experiments on video future frame extrapolation task show ConvTransformer to be superior in quality while being more parallelizable to recent approaches built upon convolutional LSTM (ConvLSTM). To the best of our knowledge, this is the first time that ConvTransformer architecture is proposed and applied to video frame synthesis.

References in corpus (10)

• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
• UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
• Longformer: The Long-Document Transformer
• Decomposing Motion and Content for Natural Video Sequence Prediction
• Learning to Generate Long-term Future via Hierarchical Prediction
• Lite Transformer with Long-Short Range Attention
• DeepStereo: Learning to Predict New Views from the World's Imagery
• BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation

Cited by in corpus (12)

• A Survey on Visual Transformer
• Facial Expression Recognition with Visual Transformers and Attentional Selective Fusion
• A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization
• ConTNet: Why not use convolution and transformer at the same time?
• Characterization of anomalous diffusion through convolutional transformers
• Less is More: Consistent Video Depth Estimation with Masked Frames Modeling
• NAST: Non-Autoregressive Spatial-Temporal Transformer for Time Series Forecasting
• Transformers predicting the future. Applying attention in next-frame and time series forecasting
• Deformation Driven Seq2Seq Longitudinal Tumor and Organs-at-Risk Prediction for Radiotherapy
• EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification
• OadTR: Online Action Detection with Transformers
• Dispatcher: A Message-Passing Approach To Language Modelling