Learning Video Representations from Correspondence Proposals

Abstract

Correspondences between frames encode rich information about dynamic content in videos. However, it is challenging to effectively capture and learn those due to their irregular structure and complex dynamics. In this paper, we propose a novel neural network that learns video representations by aggregating information from potential correspondences. This network, named $CPNet$, can learn evolving 2D fields with temporal consistency. In particular, it can effectively learn representations for videos by mixing appearance and long-range motion with an RGB-only input. We provide extensive ablation experiments to validate our model. CPNet shows stronger performance than existing methods on Kinetics and achieves the state-of-the-art performance on Something-Something and Jester. We provide analysis towards the behavior of our model and show its robustness to errors in proposals.

CVPR 2019 (Oral)

References in corpus (8)

• Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
• Two-Stream Convolutional Networks for Action Recognition in Videos
• The Kinetics Human Action Video Dataset
• Computing the Stereo Matching Cost with a Convolutional Neural Network
• ConvNet Architecture Search for Spatiotemporal Feature Learning
• Dynamic Graph CNN for Learning on Point Clouds
• Do Convnets Learn Correspondence?
• Revisiting the Effectiveness of Off-the-shelf Temporal Modeling Approaches for Large-scale Video Classification

Cited by in corpus (3)

• V4D:4D Convolutional Neural Networks for Video-level Representation Learning
• TAM: Temporal Adaptive Module for Video Recognition
• MotionSqueeze: Neural Motion Feature Learning for Video Understanding