Datasets:
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).
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
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.