Exploring Temporal Information for Improved Video Understanding
arXiv:1905.10654
Abstract
In this dissertation, I present my work towards exploring temporal information for better video understanding. Specifically, I have worked on two problems: action recognition and semantic segmentation. For action recognition, I have proposed a framework, termed hidden two-stream networks, to learn an optimal motion representation that does not require the computation of optical flow. My framework alleviates several challenges faced in video classification, such as learning motion representations, real-time inference, multi-framerate handling, generalizability to unseen actions, etc. For semantic segmentation, I have introduced a general framework that uses video prediction models to synthesize new training samples. By scaling up the training dataset, my trained models are more accurate and robust than previous models even without modifications to the network architectures or objective functions. I believe videos have much more potential to be mined, and temporal information is one of the most important cues for machines to perceive the visual world better.
PhD dissertation. Posting here for easier access and update. Chapter 4, 6 and 7 are collaborative work
References in corpus (8)
- Two-Stream Convolutional Networks for Action Recognition in Videos
- UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
- Caffe: Convolutional Architecture for Fast Feature Embedding
- The Kinetics Human Action Video Dataset
- YouTube-8M: A Large-Scale Video Classification Benchmark
- Towards Good Practices for Very Deep Two-Stream ConvNets
- Multi-Task Zero-Shot Action Recognition with Prioritised Data Augmentation
- Evolution-Preserving Dense Trajectory Descriptors