Add M³Diff training dataset card

README.md

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+---
+pretty_name: M³Diff Instruction-Tuning Data
+language:
+- en
+license: other
+task_categories:
+- image-to-text
+tags:
+- image
+- text
+- image-difference-captioning
+- multimodal
+- multi-image
+- visual-instruction-tuning
+- m3diff
+size_categories:
+- 100K<n<1M
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: training_m3diff.json
+---
+
+# M³Diff Instruction-Tuning Data
+
+This repository contains the instruction-tuning annotations used to train
+**M³Diff**, the model introduced in [OmniDiff: A Comprehensive Benchmark for
+Fine-grained Image Difference Captioning](https://openaccess.thecvf.com/content/ICCV2025/html/Liu_OmniDiff_A_Comprehensive_Benchmark_for_Fine-grained_Image_Difference_Captioning_ICCV_2025_paper.html)
+(ICCV 2025).
+
+The dataset contains **896,015 question-answer records** for fine-grained
+Image Difference Captioning (IDC). Each record presents two related images and
+asks the model to describe the visual changes between them. This repository
+contains the JSON annotations and relative image paths only; **the image files
+are not included**.
+
+Hugging Face dataset ID: `IVC-liuyuan/training_m3diff`.
+
+## Dataset composition
+
+The training mixture combines OmniDiff with several established IDC datasets
+covering real-world, edited, surveillance, bird, and 3D-rendered scenes.
+
+| Source | Instruction records |
+| --- | ---: |
+| CLEVR-Change | 444,156 |
+| CLEVR-DC | 384,466 |
+| OmniDiff | 28,056 |
+| spot-the-diff | 20,862 |
+| birds-to-words | 14,265 |
+| IEdit | 4,210 |
+| **Total** | **896,015** |
+
+The counts above refer to instruction records, not necessarily unique image
+pairs. As described in the paper, captions from CLEVR-Change and CLEVR-DC are
+converted into independent training samples.
+
+OmniDiff itself is a fine-grained IDC benchmark with 15,598 human-captioned
+image pairs collected from 324 diverse indoor and outdoor scenarios. It
+contains real-world and 3D-rendered scenes, covers 12 visual change types, and
+has captions averaging 60 words. The change taxonomy includes viewpoint,
+illumination, addition, disappearance, removal, substitution, size, color,
+orientation, pose, OCR, and counting.
+
+## Data format
+
+`training_m3diff.json` is one JSON array. Every record has the following
+structure:
+
+```json
+{
+  "image_id": "005422.png",
+  "image": [
+    "CLEVR-Change/nsc_images/CLEVR_nonsemantic_005422.png",
+    "CLEVR-Change/sc_images/CLEVR_semantic_005422.png"
+  ],
+  "conversations": [
+    {
+      "from": "human",
+      "value": "What modifications can be observed between these two pictures? <image><image>Difference:"
+    },
+    {
+      "from": "gpt",
+      "value": "the small yellow cylinder changed to brown."
+    }
+  ]
+}
+```
+
+Fields:
+
+- `image_id` or `id`: source-specific sample identifier. Older converted
+  records use `image_id`, while some records use `id`.
+- `image`: two paths relative to the common image root, ordered as the first
+  and second images being compared.
+- `conversations`: a two-turn LLaVA-style conversation. The human turn contains
+  two `<image>` placeholders and the GPT turn contains the target difference
+  caption.
+
+All 896,015 records contain exactly two image paths and one human/GPT
+conversation pair. The data uses 30 paraphrased prompt templates to reduce
+dependence on a single question formulation.
+
+## Loading
+
+Load the annotations directly from the Hub:
+
+```python
+from datasets import load_dataset
+
+dataset = load_dataset("IVC-liuyuan/training_m3diff", split="train")
+print(dataset[0])
+```
+
+Alternatively, download the raw JSON:
+
+```python
+from huggingface_hub import hf_hub_download
+
+annotation_path = hf_hub_download(
+    repo_id="IVC-liuyuan/training_m3diff",
+    repo_type="dataset",
+    filename="training_m3diff.json",
+)
+```
+
+To train M³Diff, arrange the separately obtained images under a common root
+so the paths in each record resolve as follows:
+
+```text
+<IMAGE_ROOT>/
+├── OmniDiff/
+├── CLEVR-Change/
+├── CLEVR-DC/
+├── IEdit/
+├── spot-the-diff/
+└── birds-to-words/
+```
+
+## Intended use
+
+This dataset is intended for research on image difference captioning,
+fine-grained paired-image understanding, and multimodal instruction tuning. It
+was assembled for training M³Diff, which augments LLaVA-OneVision-7B with a
+Multi-scale Differential Perception (MDP) module.
+
+## Limitations and responsible use
+
+- The JSON file does not contain images and is not a self-contained training
+  package. Users must obtain each image dataset from its authorized source.
+- Source datasets differ in domain, annotation style, collection process, and
+  license. Their mixture may preserve source-specific biases and artifacts.
+- Captions may omit, misdescribe, or ambiguously localize changes. They should
+  not be treated as ground truth for safety-critical decisions.
+- Real-world imagery may carry privacy, copyright, or representation concerns;
+  users must review the terms and documentation of each source dataset.
+
+## License
+
+The repository aggregates annotation records derived from multiple datasets,
+so no single license is asserted for the complete mixture. The annotations,
+image paths, and separately downloaded images remain subject to the terms of
+OmniDiff and their respective upstream datasets: Spot-the-Diff, IEdit,
+Birds-to-Words, CLEVR-Change, and CLEVR-DC. Consult each source before use or
+redistribution. Uploading this JSON file does not relicense any image.
+
+## Citation
+
+If this training set or M³Diff is useful in your research, please cite:
+
+```bibtex
+@inproceedings{Liu_2025_ICCV,
+  author    = {Liu, Yuan and Hou, Saihui and Hou, Saijie and Du, Jiabao and Meng, Shibei and Huang, Yongzhen},
+  title     = {OmniDiff: A Comprehensive Benchmark for Fine-grained Image Difference Captioning},
+  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+  month     = {October},
+  year      = {2025},
+  pages     = {21440--21449}
+}
+```
+
+## Acknowledgements
+
+M³Diff builds on OmniDiff, LLaVA-OneVision, Qwen2, and SigLIP. We also
+acknowledge the creators of Spot-the-Diff, IEdit, Birds-to-Words,
+CLEVR-Change, and CLEVR-DC. Please cite the corresponding upstream works when
+using their portions of this training mixture.