40 citations · 79 across the 6 of their papers we have counts for
6 papers
Real-time Universal Style Transfer on High-resolution Images via Zero-channel Pruning
Jie An, Tao Li, Haozhi Huang +6
Extracting effective deep features to represent content and style information is the key to universal style transfer. Most existing algorithms use VGG19 as the feature extractor, w…
Hallucinating Optical Flow Features for Video Classification
Yongyi Tang, Lin Ma, Lianqiang Zhou
Appearance and motion are two key components to depict and characterize the video content. Currently, the two-stream models have achieved state-of-the-art performances on video cla…
Non-local NetVLAD Encoding for Video Classification
Yongyi Tang, Xing Zhang, Jingwen Wang +3
This paper describes our solution for the 2 YouTube-8M video understanding challenge organized by Google AI. Unlike the video recognition benchmarks, such as Kinetics a…
Long-Term Human Motion Prediction by Modeling Motion Context and Enhancing Motion Dynamic
Yongyi Tang, Lin Ma, Wei Liu +1
Human motion prediction aims at generating future frames of human motion based on an observed sequence of skeletons. Recent methods employ the latest hidden states of a recurrent n…
Latent Embeddings for Collective Activity Recognition
Yongyi Tang, Peizhen Zhang, Jian-Fang Hu +1
Rather than simply recognizing the action of a person individually, collective activity recognition aims to find out what a group of people is acting in a collective scene. Previ-…
Aggregating Frame-level Features for Large-Scale Video Classification
Shaoxiang Chen, Xi Wang, Yongyi Tang +3
This paper introduces the system we developed for the Google Cloud & YouTube-8M Video Understanding Challenge, which can be considered as a multi-label classification problem defin…