| # 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](https://ego4d-data.org/docs/start-here/). 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](https://creativecommons.org/licenses/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 |
| |
| ```bibtex |
| @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} |
| } |
| ``` |
| |
