Spatiotemporal fusion ConvNet
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Graz University of Technology,University of Oxford
- Organisation type
- Academia,Academia
- Country
- Austria, United Kingdom of Great Britain and Northern Ireland
- Published
- 1 June 2016
- Authors
- Christoph Feichtenhofer, Axel Pinz, Andrew Zisserman
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Video
- Task
- Video, Action recognition
- Numerical format
- FP32
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- 13,000 tokens
[SECONDS OF VIDEO] They use UCF101, whose paper says "We introduce UCF101 which is currently the largest dataset of human actions. It consists of 101 action classes, over 13k clips and 27 hours of video data"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 2,538
Sources
Where this record came from and when it was last checked.
- Reference
- Convolutional Two-Stream Network Fusion for Video Action Recognition
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Spatiotemporal fusion ConvNet was published by Graz University of Technology,University of Oxford, in the country recorded as Austria, during June 2016. It comes out of an organisation categorised as academia,Academia.
It works in the domain of Video, and is recorded as performing the task of video, Action recognition.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Training consumed a corpus of around 13,000 tokens of text.
Answers
Spatiotemporal fusion ConvNet — common questions
Spatiotemporal fusion ConvNet— what is it used for?
It works in the domain of Video, and is recorded as handling the task of video, Action recognition. These are the areas it was designed around; they describe intent rather than a hard boundary.
Spatiotemporal fusion ConvNet— what GPU do I need to run it?
None. This is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Spatiotemporal fusion ConvNet— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
Spatiotemporal fusion ConvNet— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Spatiotemporal fusion ConvNet— who created it?
It was published by Graz University of Technology,University of Oxford, based in Austria, an organisation categorised as academia,Academia.
Spatiotemporal fusion ConvNet— when was it released?
It was published in June 2016. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.