TransNet: A deep network for fast detection of common shot transitions

06/08/2019
by   Tomáš Souček, et al.
Charles University in Prague
0

Shot boundary detection (SBD) is an important first step in many video processing applications. This paper presents a simple modular convolutional neural network architecture that achieves state-of-the-art results on the RAI dataset with well above real-time inference speed even on a single mediocre GPU. The network employs dilated convolutions and operates just on small resized frames. The training process employed randomly generated transitions using selected shots from the TRECVID IACC.3 dataset. The code and a selected trained network will be available at https://github.com/soCzech/TransNet.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/23/2017

Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural Networks

Shot boundary detection (SBD) is an important component of many video an...
08/11/2020

TransNet V2: An effective deep network architecture for fast shot transition detection

Although automatic shot transition detection approaches are already inve...
01/13/2023

YOLOv6 v3.0: A Full-Scale Reloading

The YOLO community has been in high spirits since our first two releases...
05/09/2017

Large-scale, Fast and Accurate Shot Boundary Detection through Spatio-temporal Convolutional Neural Networks

Shot boundary detection (SBD) is an important pre-processing step for vi...
08/13/2018

Fast Video Shot Transition Localization with Deep Structured Models

Detection of video shot transition is a crucial pre-processing step in v...
04/02/2018

NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter

This paper describes our NIHRIO system for SemEval-2018 Task 3 "Irony de...
04/09/2020

X3D: Expanding Architectures for Efficient Video Recognition

This paper presents X3D, a family of efficient video networks that progr...

Please sign up or login with your details

Forgot password? Click here to reset