video understanding
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2021 ◽  
Author(s):  
Beibei Zhang ◽  
Fan Yu ◽  
Yaqun Fang ◽  
Tongwei Ren ◽  
Gangshan Wu

2021 ◽  
Author(s):  
mohit sharma ◽  
Raj Aaryaman Patra ◽  
Harshal Desai ◽  
Shruti Vyas ◽  
Yogesh Rawat ◽  
...  

2021 ◽  
Author(s):  
Liwei Jin ◽  
Haoyue Cheng ◽  
Su Xu ◽  
Wayne Wu ◽  
Limin Wang
Keyword(s):  

2021 ◽  
Author(s):  
Vishal Anand ◽  
Raksha Ramesh ◽  
Boshen Jin ◽  
Ziyin Wang ◽  
Xiaoxiao Lei ◽  
...  

2021 ◽  
Author(s):  
Sihan Chen ◽  
Xinxin Zhu ◽  
Dongze Hao ◽  
Wei Liu ◽  
Jiawei Liu ◽  
...  
Keyword(s):  

2021 ◽  
Author(s):  
Zhenzhi Wang ◽  
Zhimin Li ◽  
Liyu Wu ◽  
Jiangfeng Xiong ◽  
Qinglin Lu
Keyword(s):  

Author(s):  
AJ Piergiovanni ◽  
Anelia Angelova ◽  
Michael Ryoo

Automatic video understanding is becoming more important for applications where real-time performance is crucial and compute is limited. Yet, accurate solutions so far have been computationally intensive. We propose efficient models for videos - Tiny Video Networks - which are video architectures, automatically designed to comply with fast runtimes and, at the same time are effective at video recognition tasks. The Tiny Video Networks run at faster-than-real-time speeds and demonstrate strong performance across several video benchmarks. These models not only provide new tools for real-time video applications, but also enable fast research and development in video understanding. Code and models are available.


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