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Section: Hierarchical Concept Segmentation Benchmark Suite
We curate a hierarchical concept segmentation benchmark suite to evaluate the out-of-distribution generalization of ConceptSeg-R1 across three cognitive levels: Context-Independent (CI) , Context-Dependent (CD) , and Context-Reasoning (CR) concepts. This suite integrates existing datasets into a unified taxonomy, supporting both rule induction training and structured evaluation. The following table summarizes the full benchmark composition.
| Concept Level | Directory Name | Source | Paper/Dataset Name | Description |
|---|---|---|---|---|
| Context-Independent (CI) | coco2014_Living | COCO | One-shot learning for semantic segmentation. | Common living objects (e.g., people, animals). |
| Context-Independent (CI) | coco2014_Artifact | COCO | One-shot learning for semantic segmentation. | Man-made artifact categories (e.g., vehicles, tools). |
| Context-Independent (CI) | ultra_rare | iNaturalist | Sam 3: Segment anything with concepts. | Long-tail classes (bottom 1% of model confidence). |
| Context-Reasoning (CI) | rare | iNaturalist | Sam 3: Segment anything with concepts. | Evaluation of OOD / Rare classes. |
| Context-Reasoning (CI) | fewshot1000 | FSS-1000 | FSS-1000: A 1000-Class Dataset for Few-shot Segmentation | Consistency: Identifying shared patterns across support sets. |
| Context-Reasoning (CI) | CoSOD3k1024 | COSOD3K | Co-Salient Object Detection: A Benchmark and Algorithms | Co-saliency: Reasoning to find co-existing objects. |
| Context-Dependent (CD) | DUTS | DUTS | DUTS: A Large-scale Dataset for Salient Object Detection | Saliency: Targets that stand out from the background. |
| Context-Dependent (CD) | COD10K1024 | COD10K | COD10K: A Large-scale Camouflaged Object Detection Dataset | Camouflage: Targets blending into surroundings. |
| Context-Dependent (CD) | transparent1024 | Trans10K | Trans10K: A Large-scale Dataset for Transparent Object Segmentation | Transparency: Optical refraction through materials. |
| Context-Dependent (CD) | Shadow_detection | SBU | Large-scale training of shadow detectors with noisily-annotated shadow examples. | Shadows: Alterations by contextual interactions. |
| Context-Dependent (CD) | ESDIDefects | ESDIs-SOD | Autocorrelation aware aggregation network for salient object detection of strip steel surface defects | Industrial Anomaly: Manufacturing surface defects. |
| Context-Dependent (CD) | Polyp | Kvasir/CVC | Pranet: Parallel reverse attention network for polyp segmentation. | Medical: Colon Polyp lesions identification. |
| Context-Dependent (CD) | Breast_Tumor | Dataset-B | . Dataset of breast ultrasound images | Medical: Breast Ultrasound tumors identification. |
| Context-Dependent (CD) | isic2018 | ISIC2018 | Skin lesion analysis toward melanoma detection 2018: A challenge hosted by the international skin imaging collaboration (isic) | Medical: Skin lesion analysis relative to healthy tissue. |
| Context-Reasoning (CR) | MGrounding-630k | MGrounding | Multi-Image Grounding for Visual Reasoning | Logic: Reasoning to identify distinct or shared objects. |
| Context-Reasoning (CR) | MIG-Bench | MIG | Migician: Revealing the magic of free-form multi-image grounding in multimodal large language models | Complex CR: Includes Spatio-temporality and View Difference. |
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