--- license: mit language: - en pretty_name: LongTVQA+ --- # LongTVQA+ Dataset This repository contains the **LongTVQA+** dataset in JSON format. LongTVQA+ is built upon the original **TVQA+** dataset, with the key difference that it **extends the question grounding scope from short clip-level segments (≈1 minute) to long episode-level videos (up to ~20 minutes)**. This enables research on long-form video understanding, long-range temporal reasoning, and fine-grained spatio-temporal grounding in realistic TV show episodes. In addition to the extended temporal scope, LongTVQA+ preserves and leverages the rich annotations provided in TVQA+, including: 1. Frame-level bounding box annotations for visual concept words appearing in questions and correct answers. 2. Refined timestamp annotations aligned with long episode-level context. Please refer to the original **TVQA+ paper** for details on the annotation protocol and baseline evaluations. --- ## Files - `LongTVQA_plus_train.json` — training split (23,545 QA samples) - `LongTVQA_plus_val.json` — validation split (3,017 QA samples) - `LongTVQA_plus_subtitle_clip_level.json` — clip-level subtitles indexed by video clip (4,198 clips) - `LongTVQA_plus_subtitle_episode_level.json` — episode-level subtitles indexed by episode (220 episodes) --- ## QA JSON Format Each entry in `LongTVQA_plus_train.json` and `LongTVQA_plus_val.json` is a dictionary with the following fields: | Key | Type | Description | | --- | --- | --- | | `qid` | int | Question ID (same as in TVQA+). | | `q` | str | Question text. | | `a0` ... `a4` | str | Five multiple-choice answers. | | `answer` | str | Correct answer key (`"a0"`–`"a4"`). | | `ts` | list | Refined timestamp annotation. For example, `[0, 5.4]` indicates the localized temporal span starts at 0s and ends at 5.4s. | | `episode_name` | str | Episode ID (e.g. `s01e02`). | | `occur_clip` | str | Video clip name. Format: `{show_name_abbr}_s{season}e{episode}_seg{segment}_clip_{clip}`. Episodes are typically divided into two segments separated by the opening theme. For **The Big Bang Theory**, `{show_name_abbr}` is omitted (e.g. `s05e02_seg02_clip_00`). | | `bbox` | dict | Frame-level bounding box annotations sampled at 3 FPS. Keys are frame indices. Values are lists of bounding boxes with `img_id`, `top`, `left`, `width`, `height`, and `label`. | --- ### QA Sample ```json { "answer": "a1", "qid": 134094, "ts": [5.99, 11.98], "a1": "Howard is talking to Raj and Leonard", "a0": "Howard is talking to Bernadette", "a3": "Howard is talking to Leonard and Penny", "a2": "Howard is talking to Sheldon , and Raj", "q": "Who is Howard talking to when he is in the lab room ?", "episode_name": "s05e02", "occur_clip": "s05e02_seg02_clip_00", "a4": "Howard is talking to Penny and Bernadette", "bbox": { "14": [ { "img_id": 14, "top": 153, "label": "Howard", "width": 180, "height": 207, "left": 339 }, { "img_id": 14, "top": 6, "label": "lab", "width": 637, "height": 354, "left": 3 } ], "20": [], "26": [], "32": [], "38": [] } } ``` --- ## Subtitles JSON Format Two subtitle files are provided to support different temporal granularities: | File | Key | Type | Description | | ------------------------------------------- | -------------- | ---- | ------------------------------------------------------------------------------------------------------------------- | | `LongTVQA_plus_subtitle_clip_level.json` | `vid_name` | str | Clip-level subtitle text, with utterances separated by ``. | | `LongTVQA_plus_subtitle_episode_level.json` | `episode_name` | str | Episode-level subtitle text, including clip markers such as ``, and utterances separated by ``. | --- ### Subtitles Sample ```json { "s09e14_seg02_clip_04": "Sheldon : That 's a risk I'm willing to take ! Amy : Well , this is so nice . ..." } ``` --- ## License This dataset is released under the **MIT License**. ## 📝 Citation If you find our work helpful, please cite: ```bibtex @misc{liu2025longvideoagentmultiagentreasoninglong, title={LongVideoAgent: Multi-Agent Reasoning with Long Videos}, author={Runtao Liu and Ziyi Liu and Jiaqi Tang and Yue Ma and Renjie Pi and Jipeng Zhang and Qifeng Chen}, year={2025}, eprint={2512.20618}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={[https://arxiv.org/abs/2512.20618](https://arxiv.org/abs/2512.20618)}, }