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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 2128 of 28 papers

TitleStatusHype
Deep High-Resolution Representation Learning for Visual RecognitionCode1
BASNet: Boundary-Aware Salient Object DetectionCode1
Searching for MobileNetV3Code1
BiSeNet: Bilateral Segmentation Network for Real-time Semantic SegmentationCode1
Rethinking Atrous Convolution for Semantic Image SegmentationCode1
ICNet for Real-Time Semantic Segmentation on High-Resolution ImagesCode0
Pyramid Scene Parsing NetworkCode1
U-Net: Convolutional Networks for Biomedical Image SegmentationCode3
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