--- viewer: false task_categories: - image-to-image - text-to-image - image-segmentation - image-text-to-text - image-text-to-image - visual-question-answering - image-to-text tags: - unified-multimodal-model - benchmark - synergistic-learning size_categories: - 10K Dataset statistics Benchmark comparison ## Layout ``` ├── Internal_Consistency/ # IC │ ├── prompts.txt # 556 generation prompts │ ├── questions.json # yes/no VQA keyed by prompt_ │ └── images/ # 556 reference images ├── Und_Guided_Gen/ # UGG │ ├── UGG.json # 543 grounded generation items │ └── images/ # 539 source images ├── Gen_Guided_Und/ # GGU (three spatial sub-categories) │ ├── 2D_Spatial/ # 182 2D spatial items │ ├── 3D_Spatial/ # 180 3D spatial items │ └── Complex_Relation/ # 180 relation items └── Mutual_Enhancement/ # ME ├── ME.json # 264 rows, 528 samples └── images/ # 264 source images ``` ## Field notes - **IC** — `questions.json` is keyed `prompt_` matching the 0-indexed line `N` in `prompts.txt`; each value maps question numbers to yes/no questions about the generated image. - **UGG** — `image_path` is relative to `Und_Guided_Gen/` (e.g. `images/1.jpg`); `bbox`/`mask` give the grounded edit region, `operation` the edit type. - **GGU** — `image_path` is relative to each sub-category dir: `2D_Spatial/matrices/...`, `3D_Spatial/cubes/...`. `Complex_Relation` items carry no source image and are reconstructed from their `description` / validation questions. - **ME** — `image_path` is a bare filename resolving under `Mutual_Enhancement/images/`.