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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 1120 of 28 papers

TitleStatusHype
Unite-Divide-Unite: Joint Boosting Trunk and Structure for High-accuracy Dichotomous Image SegmentationCode1
Rethinking BiSeNet For Real-time Semantic SegmentationCode1
Camouflaged Object Segmentation with Distraction MiningCode1
Concealed Object DetectionCode1
HyperSeg: Patch-wise Hypernetwork for Real-time Semantic SegmentationCode1
Suppress and Balance: A Simple Gated Network for Salient Object DetectionCode1
Global Context-Aware Progressive Aggregation Network for Salient Object DetectionCode1
F3Net: Fusion, Feedback and Focus for Salient Object DetectionCode1
Deep High-Resolution Representation Learning for Visual RecognitionCode1
BASNet: Boundary-Aware Salient Object DetectionCode1
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