Datasets:
Dataset details
Schema
- video_uid: Ego4D video UUID.
- conversation: ordered list of turns, each with role, content, time and task.
- role: user or assistant.
- content: natural-language query or response.
- time: seconds from the start of the video.
- task: modality/reasoning/task identifier, for example object_spatial_appear.
A video can have multiple dialogue examples. Preserve example order and all test examples when evaluating. Do not deduplicate test rows by video_uid.
Video access and preprocessing
This repository contains annotations and video UID lists; it does not redistribute videos or precomputed visual features. Obtain Ego4D access using the official procedure. The UID files under uids/ contain the 4,426 training IDs, 40 test IDs and their 4,466-ID union. Use the Memento code repository to download the video_540ss subset, sample at 2 FPS, pad frames to 384×384 and encode SigLIP features.
Construction
Memento-54K is constructed from Ego4D daily-life videos selected for long-duration interaction. The paper describes nine task types across spatial and temporal reasoning and the action, object and text modalities. Action queries use timestamped narrations with GPT-4o-assisted generation; object and text tasks use timestamp-level detections with rule-based QA generation. Dialogue samples combine one to five user queries with timestamped responses. See the paper for the full construction method.
Data quality and limitations
Generated queries and detector-derived labels may contain errors. First-person videos reflect the activities, environments and participants represented in Ego4D and are not a representative sample of every application setting.
License and attribution
Memento annotation text and the authored dataset organization are licensed under CC BY 4.0, permitting research and commercial reuse subject to its terms and attribution. This grant covers rights held by the Memento annotation authors. Ego4D source videos remain under their data-provider agreements and must be obtained independently; this repository grants no rights to redistribute those videos or to third-party content, likenesses or trademarks.
Please attribute Memento: Toward an All-Day Proactive Assistant for Ultra-Long Streaming Video and the Ego4D dataset when using this resource.
Citation
@inproceedings{memento2026iclr,
title = {Memento: Toward an All-Day Proactive Assistant for Ultra-Long Streaming Video},
booktitle = {ICLR},
year = {2026},
url = {https://openreview.net/forum?id=FtdbdoGbk3}
}