Datasets:
Tasks:
Visual Question Answering
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
File size: 3,041 Bytes
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task_categories:
- visual-question-answering
language:
- en
tags:
- video-understanding
- eventmemagent
configs:
- config_name: training
data_files:
- split: train
path: train/eventmemagent_train.parquet
- config_name: ovobench
data_files:
- split: test
path: eval/ovobench/eventmemagent_ovobench.parquet
- config_name: streamingbench
data_files:
- split: test
path: eval/streamingbench/eventmemagent_streamingbench.parquet
---
# EventMemAgent
Prepared training and evaluation inputs for **EventMemAgent: Hierarchical
Event-Centric Memory for Online Video Understanding with Adaptive Tool Use**
(ECCV 2026).
[Paper](https://arxiv.org/abs/2602.15329) ·
[Code](https://github.com/lingcco/EventMemAgent) ·
[Model](https://huggingface.co/lingcco/EventMemAgent-8B)
## Contents
| Configuration | Split | Rows | Description |
| --- | --- | ---: | --- |
| training | train | 10,000 | MovieChat samples with VideoMarathon annotations |
| ovobench | test | 3,035 | OVO-Bench inputs with prepared event memory |
| streamingbench | test | 2,500 | StreamingBench inputs with prepared event memory |
The evaluation folders also contain `ovo_bench_new.json` and `questions_real.json`,
the reference annotations needed by the scoring scripts. Generated predictions,
scoring outputs and experiment logs are not included. No separate training
validation split is included.
## Memory construction
Videos are sampled at 1 FPS. Short-term memory contains at most 32 frames;
event segmentation uses minimum event length 8 and content threshold 0.2.
Frozen Qwen3-VL-4B-Instruct generates event captions and
Qwen3-Embedding-0.6B generates event embeddings.
Parquet records retain the original nested fields, including `prompt`,
`question`, `ground_truth`, `agent_name`, `reward_model`, `data_source`,
`short_term_memory`, `long_term_memory` and `extra_info.tools_kwargs`. Images
are embedded in memory records. Original video paths identify samples and do
not need to exist locally for precomputed-memory evaluation.
## Loading
```python
from datasets import load_dataset
train = load_dataset("lingcco/EventMemAgent", "training", split="train", streaming=True)
ovo = load_dataset("lingcco/EventMemAgent", "ovobench", split="test", streaming=True)
streamingbench = load_dataset("lingcco/EventMemAgent", "streamingbench", split="test", streaming=True)
```
Alternatively, download the parquet files and reference JSON files and use the
accompanying code repository's evaluation and scoring scripts. Keep evaluation
splits out of policy optimization and checkpoint selection.
## Sources and usage terms
These processed inputs are derived from MovieChat, VideoMarathon, OVO-Bench
and StreamingBench. Original video frames and annotations remain subject to
their respective upstream licenses and usage terms; this repository does not
grant additional rights to third-party content. The accompanying code's Apache
2.0 license does not apply automatically to the underlying datasets.
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