| | --- |
| | pretty_name: MMNeedle |
| | language: |
| | - en |
| | license: cc-by-4.0 |
| | homepage: https://mmneedle.github.io/ |
| | paper: https://arxiv.org/abs/2406.11230 |
| | tagline: Benchmarking long-context multimodal retrieval under extreme visual context lengths. |
| | tags: |
| | - multimodal |
| | - visual-question-answering |
| | - long-context |
| | - evaluation |
| | size_categories: |
| | - 100K<n<1M |
| | --- |
| | |
| | # MMNeedle |
| |
|
| | MMNeedle is a stress test for long-context multimodal reasoning. Each example |
| | contains a sequence of haystack images created by stitching MS COCO sub-images |
| | into 1×1, 2×2, 4×4, or 8×8 grids. Given textual needle descriptions (derived |
| | from MS COCO captions), models must predict which haystack image and which |
| | sub-image cell matches the caption—or report that the needle is absent. |
| |
|
| | This dataset card accompanies the official Hugging Face release so researchers no |
| | longer need to download from Google Drive or regenerate the benchmark from MS |
| | COCO. |
| |
|
| | ## Dataset structure |
| |
|
| | - **Sequences (`sequence_length`)**: either a single stitched image or a set of 10 stitched images. |
| | - **Grid sizes (`grid_rows`, `grid_cols`)**: {1, 2, 4, 8} with square layouts. |
| | - **Needles per query (`needles_per_query`)**: {1, 2, 5}. Each query provides that many captions. |
| | - **Examples per configuration**: 10,000. Half contain the needle(s); half are negatives. |
| | - **Total examples**: 210,000 (21 configurations × 10k samples). |
| | |
| | Every example stores the full list of haystack image paths, the ground-truth |
| | needle locations (`image_index`, `row`, `col`), the MS COCO image IDs for the |
| | needles, the natural-language captions, and a `has_needle` boolean. |
| | |
| | ## Usage |
| | |
| | ```python |
| | from datasets import load_dataset |
| | |
| | ds = load_dataset("Wang-ML-Lab/MMNeedle", split="test") |
| | example = ds[0] |
| | print(example.keys()) |
| | # dict_keys(['id', 'sequence_length', 'grid_rows', 'grid_cols', 'needles_per_query', |
| | # 'haystack_images', 'needle_locations', 'needle_image_ids', |
| | # 'needle_captions', 'has_needle']) |
| | ``` |
| | |
| | Each entry in `haystack_images` is a PIL-compatible image object. `needle_captions` |
| | contains one string per requested needle (even for negative examples, where the |
| | corresponding location is `(-1, -1, -1)`). |
| | |
| | ## Data fields |
| | |
| | | Field | Type | Description | |
| | | --- | --- | --- | |
| | | `id` | string | Unique identifier combining configuration and sample id. | |
| | | `sequence_length` | int | Number of stitched haystack images shown to the model. | |
| | | `grid_rows`, `grid_cols` | int | Dimensions of the stitched grid (each cell is 256×256 px). | |
| | | `needles_per_query` | int | Number of captions provided for the sample (1, 2, or 5). | |
| | | `haystack_images` | list of `Image` | Ordered haystack images for the sequence. | |
| | | `needle_locations` | list of dict | One dict per caption with `image_index`, `row`, and `col` (−1 when absent). | |
| | | `needle_image_ids` | list of string | MS COCO filenames that generated each caption. | |
| | | `needle_captions` | list of string | MS COCO captions used as the needle descriptions. | |
| | | `has_needle` | bool | True if at least one caption corresponds to a haystack cell. | |
| | |
| | ## Recommended evaluation protocol |
| | |
| | 1. Feed the ordered haystack images (preserving grid layout) plus the instruction |
| | template from the MMNeedle paper to your multimodal model. |
| | 2. Parse the model output into `(image_index, row, col)` triples. |
| | 3. Compare against `needle_locations` to compute accuracy for positives and the |
| | false-positive rate for negatives. |
| | |
| | See the repository’s `needle.py` for a reference implementation. |
| | |
| | ## Source data |
| | |
| | - **Images & Captions**: MS COCO 2014 validation split (CC BY 4.0). |
| | - **Needle Metadata**: Automatically generated by the MMNeedle authors; included |
| | here as JSON files. |
| | |
| | ## Licensing |
| | |
| | All stitched haystack images inherit the [Creative Commons Attribution 4.0 |
| | License](https://creativecommons.org/licenses/by/4.0/) from MS COCO. Attribution |
| | at minimum should cite both MMNeedle and MS COCO. |
| | |
| | ## Citations |
| | |
| | ``` |
| | @article{wang2024mmneedle, |
| | title={Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models}, |
| | author={Wang, Hengyi and Shi, Haizhou and Tan, Shiwei and Qin, Weiyi and Wang, Wenyuan and Zhang, Tunyu and Nambi, Akshay and Ganu, Tanuja and Wang, Hao}, |
| | journal={arXiv preprint arXiv:2406.11230}, |
| | year={2024} |
| | } |
| | |
| | @article{lin2014microsoft, |
| | title={Microsoft COCO: Common Objects in Context}, |
| | author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C. Lawrence}, |
| | journal={ECCV}, |
| | year={2014} |
| | } |
| | ``` |
| | |
