---
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.