Release training and evaluation inputs

README.md

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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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eval/streamingbench/eventmemagent_streamingbench.parquet

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train/eventmemagent_train.parquet

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