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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
Pyramid Scene Parsing NetworkCode1
Rethinking Atrous Convolution for Semantic Image SegmentationCode1
Rethinking BiSeNet For Real-time Semantic SegmentationCode1
Deep High-Resolution Representation Learning for Visual RecognitionCode1
BASNet: Boundary-Aware Salient Object DetectionCode1
BiSeNet: Bilateral Segmentation Network for Real-time Semantic SegmentationCode1
Camouflaged Object Segmentation with Distraction MiningCode1
Concealed Object DetectionCode1
Evaluation Study on SAM 2 for Class-agnostic Instance-level SegmentationCode1
F3Net: Fusion, Feedback and Focus for Salient Object DetectionCode1
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