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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
Bilateral Reference for High-Resolution Dichotomous Image SegmentationCode7
Highly Accurate Dichotomous Image SegmentationCode4
Revisiting Image Pyramid Structure for High Resolution Salient Object DetectionCode3
U-Net: Convolutional Networks for Biomedical Image SegmentationCode3
U^2-Net: Going Deeper with Nested U-Structure for Salient Object DetectionCode3
Multi-view Aggregation Network for Dichotomous Image SegmentationCode2
High-Precision Dichotomous Image Segmentation via Probing Diffusion CapacityCode2
SegRefiner: Towards Model-Agnostic Segmentation Refinement with Discrete Diffusion ProcessCode2
Concealed Object DetectionCode1
Camouflaged Object Segmentation with Distraction MiningCode1
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