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Dichotomous Image Segmentation

Currently, existing image segmentation tasks mainly focus on segmenting objects with specific characteristics, e.g., salient, camouflaged, meticulous, or specific categories. Most of them have the same input/output formats, and barely use exclusive mechanisms designed for segmenting targets in their models, which means almost all tasks are dataset-dependent. Thus, it is very promising to formulate a category-agnostic DIS task for accurately segmenting objects with different structure complexities, regardless of their characteristics. Compared with semantic segmentation, the proposed DIS task usually focuses on images with single or a few targets, from which getting richer accurate details of each target is more feasible.

Papers

Showing 110 of 28 papers

TitleStatusHype
Patch-Depth Fusion: Dichotomous Image Segmentation via Fine-Grained Patch Strategy and Depth Integrity-PriorCode1
BEN: Using Confidence-Guided Matting for Dichotomous Image SegmentationCode0
Mask Factory: Towards High-quality Synthetic Data Generation for Dichotomous Image Segmentation0
High-Precision Dichotomous Image Segmentation via Probing Diffusion CapacityCode2
Evaluation Study on SAM 2 for Class-agnostic Instance-level SegmentationCode1
Multi-view Aggregation Network for Dichotomous Image SegmentationCode2
Bilateral Reference for High-Resolution Dichotomous Image SegmentationCode7
Promoting Segment Anything Model towards Highly Accurate Dichotomous Image Segmentation0
SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion ProcessCode2
Unite-Divide-Unite: Joint Boosting Trunk and Structure for High-accuracy Dichotomous Image SegmentationCode1
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