Moments in Time Dataset

A large-scale dataset for recognizing and understanding action in videos
Explore Moments

Moments is a research project in development by the MIT-IBM Watson AI Lab. The project is dedicated to building a very large-scale dataset to help AI systems recognize and understand actions and events in videos.

Today, the dataset includes a collection of one million labeled 3 second videos, involving people, animals, objects or natural phenomena, that capture the gist of a dynamic scene.

MOMENTS

Three seconds events capture an ecosystem of changes in the world: 3 seconds convey meaningful information to understand how agents (human, animal, artificial or natural) transform from one state to another.

Diversity

Designed to have large inter-class and intra-class variation that represent dynamical events at different levels of abstraction (i.e. "opening" doors, drawers, curtains, presents, eyes, mouths, and even flower petals).

Generalization

A large-scale, human-annotated video dataset capturing visual and/or audible actions, produced by humans, animals, objects or nature that together allow for the creation of compound activities occurring at longer time scales.

Transferability

Supervised tasks on a large coverage of the visual and auditory ecosystem help construct powerful but flexible feature detectors, allowing models to quickly transfer learned representations to novel domains.

Team

{{member.name}}

{{member.affiliation}}

{{member.name}}

{{member.affiliation}}

Video Understanding Demo

Can we understand what models attend to during a prediction?

Here, we show the areas of the video frames that our neural network is focusing on in order to recognize the event in the video. These methods show the networks ability to locate the most important areas to focus on for each video clip so that it can identify each moment.

Download our Paper

Moments in Time Dataset: one million videos for event understanding

Mathew Monfort, Bolei Zhou, Sarah Adel Bargal,
Tom Yan, Alex Andonian, Kandan Ramakrishnan, Lisa Brown,
Quanfu Fan, Dan Gutfreund, Carl Vondrick, Aude Oliva

To obtain the dataset, please contact mmonfort@mit.edu

Download Paper

Acknowledgements

This work was supported by: