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lingcco/EventMemAgent

EventMemAgent Prepared training and evaluation inputs for EventMemAgent: Hierarchical Event-Centric Memory for Online Video Understanding with Adaptive Tool Use (ECCV 2026). Paper · Code · Model 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… See the full description on the dataset page: https://huggingface.co/datasets/lingcco/EventMemAgent.

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EventMemAgent

Prepared training and evaluation inputs for EventMemAgent: Hierarchical Event-Centric Memory for Online Video Understanding with Adaptive Tool Use (ECCV 2026).

Paper · Code · Model

Contents

ConfigurationSplitRowsDescription
trainingtrain10,000MovieChat samples with VideoMarathon annotations
ovobenchtest3,035OVO-Bench inputs with prepared event memory
streamingbenchtest2,500StreamingBench 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.