Tiny Video Networks
Citations Over TimeTop 11% of 2021 papers
Abstract
Abstract Automatic video understanding is becoming more important for applications where real‐time performance is crucial and compute is limited: for example, automated video tagging, robot perception, activity recognition for mobile devices. 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 TVNs 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.
Related Papers
- → Improved scale-invariant feature transform feature-matching technique-based object tracking in video sequences via a neural network and Kinect sensor(2013)8 cited
- → Research on machine vision technology based detection and tracking of objects on video image(2022)4 cited
- → AI Based Video Processing using OO(2023)1 cited
- → Video Skimming and Summarization Based on Principal Component Analysis(2001)5 cited
- Design of Real Time Video Image Acquisition and Process Program Based on Video for Windows(2004)