| --- |
| license: mit |
| language: |
| - en |
| task_categories: |
| - visual-question-answering |
| - video-classification |
| tags: |
| - video |
| - vlm |
| - temporal-reasoning |
| - temporal-conflict |
| - benchmark |
| - mvbench |
| - clevrer |
| - charades |
| size_categories: |
| - n<1K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/metadata.parquet |
| --- |
| |
| # MVBench Temporal-Conflict Subset |
|
|
| A curated 75-sample subset of [OpenGVLab/MVBench](https://huggingface.co/datasets/OpenGVLab/MVBench) selected for a controlled **temporal-conflict** benchmark on vision-language models. |
|
|
| The full design and motivation are described in the parent project proposal (Temporal Conflict Resolution in Vision-Language Models). In short: each video here was chosen because its question + wrong-option distractors map directly onto a *visually-realizable mid-clip edit* — recolor, resize, swap, multiplication, or existence-flip — so that the wrong candidate becomes a valid edit target. The benchmark then probes how a VLM arbitrates between the original and the edited state when answering the original MVBench question. |
|
|
| ## Composition |
|
|
| | Split | Source | n | Conflict mechanism | |
| |---|---|---:|---| |
| | `moving_attribute` | CLEVRER (synthetic) | 15 | Recolor / reshape / re-material the target object mid-clip toward a wrong-option distractor | |
| | `moving_count` | CLEVRER (synthetic) | 15 | Clone or remove objects mid-clip to shift count toward a wrong-option distractor | |
| | `object_existence` | CLEVRER (synthetic) | 15 | Add or remove the queried object class mid-clip to flip yes ↔ no | |
| | `moving_direction` | CLEVRER (synthetic) | 15 | Recolor / reshape the named referent mid-clip; original direction question now has two candidate referents | |
| | `object_interaction` | Charades / STAR (real) | 15 | Real-world object swap inside the action window — replace the held object with a wrong-option distractor | |
|
|
| **Total: 75 samples, 75 unique videos, ~98 MB.** |
|
|
| ## Selection criteria |
|
|
| Sampling was uniform random (seed `42`) from each MVBench JSON, oversampling to 30 candidates per split. Each candidate was then **manually verified** by a per-split agent against the following editability test: |
|
|
| 1. **Atomic distractors.** The wrong-option candidates must be visually-realizable as a single attribute edit, not narrative descriptions. |
| 2. **Referent specificity.** The question must name a single, isolatable object (or class) so the edit lands cleanly. |
| 3. **QA invariance.** The edit must not break the question's logical pre-conditions (e.g., for `moving_count`, exclude collision-counting questions where cloning would create unscheduled collisions). |
| 4. **Editability of the swap target.** For `object_interaction`, *every* candidate must be a hand-sized, portable object — answers that are furniture (`table`, `bed`, `closet`, `refrigerator`) were dropped because such swaps are not coherent for current real-world video edit models. |
|
|
| Candidates that failed were replaced from the reserve until 15 valid samples were collected per split. The `editability_notes` field on every row records the recommended edit type for that sample. |
|
|
| ## Files |
|
|
| - `data/metadata.parquet` — the canonical 75-row table (loadable with `datasets.load_dataset`). |
| - `metadata.json` — same data as JSON for non-parquet workflows. |
| - `videos/*.mp4` — 75 video files, named with the **original MVBench filename** so they can be cross-referenced against OpenGVLab/MVBench. |
|
|
| ## Schema |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | `id` | string | Unique key, e.g. `moving_attribute_00` | |
| | `split` | string | Original MVBench split name | |
| | `source_dataset` | string | `CLEVRER` or `Charades/STAR` | |
| | `video` | string | Original MVBench video filename (also the basename in `videos/`) | |
| | `video_path` | string | Path relative to repo root | |
| | `question` | string | Original MVBench question (unchanged) | |
| | `candidates` | list[string] | Original 3-5 multiple-choice options | |
| | `answer` | string | Ground-truth answer in the un-edited video | |
| | `conflict_mechanism` | string | Recommended conflict-edit type for this split | |
| | `editability_notes` | string | Per-sample edit recipe written by the verification agents | |
| | `orig_mvbench_index` | int | Index into the original MVBench split JSON | |
| | `start`, `end`, `accurate_start`, `accurate_end` | float | (object_interaction only) Action-window timestamps in seconds for clipping | |
| |
| ## Usage |
| |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("shivank21/mvbench-temporal-conflict-subset", split="train") |
| print(ds[0]) |
| # Video is at: hf://datasets/shivank21/mvbench-temporal-conflict-subset/videos/<filename> |
| ``` |
| |
| To download a single video: |
| |
| ```python |
| from huggingface_hub import hf_hub_download |
| local = hf_hub_download( |
| repo_id="shivank21/mvbench-temporal-conflict-subset", |
| filename=f"videos/{row['video']}", |
| repo_type="dataset", |
| ) |
| ``` |
| |
| ## Provenance and licensing |
|
|
| - **Questions, candidates, answers, and video selections** come from [OpenGVLab/MVBench](https://huggingface.co/datasets/OpenGVLab/MVBench) (MIT-licensed). Cite their paper [arXiv:2311.17005](https://arxiv.org/abs/2311.17005). |
| - **CLEVRER videos** are synthetic clips from Yi et al., 2019 ([clevrer.csail.mit.edu](http://clevrer.csail.mit.edu/)). Academic-research use only. |
| - **STAR / Charades videos** are real-world clips from Sigurdsson et al., 2016 ([prior.allenai.org/projects/charades](https://prior.allenai.org/projects/charades)). Subject to the original Charades license. |
|
|
| This subset is for academic research use only. |
|
|
