Skip-Convolutions for Efficient Video Processing with Amir Habibian - #496
Published:Jun 28, 2021 19:59
•1 min read
•Practical AI
Analysis
This article summarizes a podcast episode from Practical AI, focusing on video processing research presented at CVPR. The primary focus is on Amir Habibian's work, a senior staff engineer manager at Qualcomm Technologies. The discussion centers around two papers: "Skip-Convolutions for Efficient Video Processing," which explores training discrete variables within visual neural networks, and "FrameExit," a framework for conditional early exiting in video recognition. The article provides a brief overview of the topics discussed, hinting at the potential for improved efficiency in video processing through these novel approaches. The show notes are available at twimlai.com/go/496.
Key Takeaways
- •The article discusses research on efficient video processing.
- •It highlights Amir Habibian's work on Skip-Convolutions and FrameExit.
- •The research aims to improve efficiency in video recognition and processing.
Reference
“We explore the paper Skip-Convolutions for Efficient Video Processing, which looks at training discrete variables to end to end into visual neural networks.”