Add M³Diff training dataset card

README.md

@@ -53,8 +53,7 @@ covering real-world, edited, surveillance, bird, and 3D-rendered scenes.
 | **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.
+pairs.
 
 OmniDiff itself is a fine-grained IDC benchmark with 15,598 human-captioned
 image pairs collected from 324 diverse indoor and outdoor scenarios. It
@@ -145,17 +144,6 @@ 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,
@@ -170,13 +158,12 @@ redistribution. Uploading this JSON file does not relicense any image.
 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}
+@inproceedings{liu2025omnidiff,
+  title={OmniDiff: A Comprehe,nsive Benchmark for Fine-grained Image Difference Captioning},
+  author={Liu, Yuan and Hou, Saihui and Hou, Saijie and Du, Jiabao and Meng, Shibei and Huang Yongzhen},
+  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
+  pages={21440--21449},
+  year={2025}
 }
 ```