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SotA/Computer Vision/Camouflaged Object Segmentation

Camouflaged Object Segmentation

34 benchmarks47 papers

Camouflaged object segmentation (COS) or Camouflaged object detection (COD), which was originally promoted by T.-N. Le et al. (2017), aims to identify objects that conceal their texture into the surrounding environment. The high intrinsic similarities between the target object and the background make COS/COD far more challenging than the traditional object segmentation task. Also, refer to the online benchmarks on CAMO dataset, COD dataset, and online demo.

<span style="color:grey; opacity: 0.6">( Image source: Anabranch Network for Camouflaged Object Segmentation )</span>

Benchmarks

Camouflaged Object Segmentation on CAMO

MAEWeighted F-MeasureS-MeasureE_{\phi}S_{\alpha}F_{\beta}

Camouflaged Object Segmentation on PCOD_1200

S-Measure

Camouflaged Object Segmentation on COD

S-MeasureWeighted F-MeasureMAE

Camouflaged Object Segmentation on CHAMELEON

S-measureweighted F-measureMAE

Camouflaged Object Segmentation on NC4K

S-measureweighted F-measureMAE

Camouflaged Object Segmentation on MoCA-Mask

S-measureweighted F-measureMAEmDicemIoU

Camouflaged Object Segmentation on COD10K

E_{\phi}MAES_{\alpha}F_{\beta}

Camouflaged Object Segmentation on Camouflaged Animal Dataset

S-measureweighted F-measureMAEmDicemIoU

Camouflaged Object Segmentation on Chameleon

E_{\phi}F_{\beta}MAES_{\alpha}