Search Results for author: Yongyi Tang

Found 6 papers, 1 papers with code

Real-time Universal Style Transfer on High-resolution Images via Zero-channel Pruning

no code implementations16 Jun 2020 Jie An, Tao Li, Hao-Zhi Huang, Li Shen, Xuan Wang, Yongyi Tang, Jinwen Ma, Wei Liu, Jiebo Luo

Extracting effective deep features to represent content and style information is the key to universal style transfer.

Style Transfer

Hallucinating Optical Flow Features for Video Classification

1 code implementation28 May 2019 Yongyi Tang, Lin Ma, Lianqiang Zhou

However, extracting motion information, specifically in the form of optical flow features, is extremely computationally expensive, especially for large-scale video classification.

Classification General Classification +3

Non-local NetVLAD Encoding for Video Classification

no code implementations29 Sep 2018 Yongyi Tang, Xing Zhang, Jingwen Wang, Shaoxiang Chen, Lin Ma, Yu-Gang Jiang

This paper describes our solution for the 2$^\text{nd}$ YouTube-8M video understanding challenge organized by Google AI.

Classification General Classification +3

Long-Term Human Motion Prediction by Modeling Motion Context and Enhancing Motion Dynamic

no code implementations7 May 2018 Yongyi Tang, Lin Ma, Wei Liu, Wei-Shi Zheng

Human motion prediction aims at generating future frames of human motion based on an observed sequence of skeletons.

Human motion prediction motion prediction

Latent Embeddings for Collective Activity Recognition

no code implementations20 Sep 2017 Yongyi Tang, Peizhen Zhang, Jian-Fang Hu, Wei-Shi Zheng

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.

Activity Recognition

Aggregating Frame-level Features for Large-Scale Video Classification

no code implementations4 Jul 2017 Shaoxiang Chen, Xi Wang, Yongyi Tang, Xinpeng Chen, Zuxuan Wu, Yu-Gang Jiang

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 defined on top of the large scale YouTube-8M Dataset.

Classification General Classification +3

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