Developing　real-time　cloud　segmentation　technology　is　urgent　for　many　remote　sensing　based　applications　such　as　weather　forecasting.　Existing　deep　learning　based　cloud　segmentation　methods　involve　two　shortcomings.　(a):　They　tend　to　produce　discontinuous　boundaries　and　fail　to　capture　less　salient　feature,　which　corresponds　to　thin　cloud　pixels;　(b):　they　are　unrobust　towards　different　scenarios.　Those　issues　are　circumvented　by　integrating　U-Net　and　the　swin　transformer　together,　with　an　efficiently　designed　dual　attention　mechanism　based　skip　connection.　Typically,　a　swin　transformer　based　encoder-decoder　network,　by　incorporating　a　dual　attentional　skip　connection　with　Swin-UNet　(DASUNet)　is　proposed.　DASUNet　captures　the　global　relationship　of　image　patches　based　on　its　window　attention　mechanism,　which　fits　the　real-time　requirement.　Moreover,　DASUNet　characterizes　the　less　salient　features　by　equipping　with　token　dual　attention　modules　among　the　skip　connection,　which　compensates　the　ignorance　of　less　salient　features　incurred　from　traditional　attention　mechanism　during　the　stacking　of　transformer　layers.　Experiments　on　ground-based　images　(SWINySeg)　and　remote　sensing　images　(HRC-WHU,　38-Cloud)　show　that,　DASUNet　achieves　the　state-of-the-art　or　competitive　results　for　cloud　segmentation　(six　top-1　positions　of　six　metrics　among　11　methods　on　SWINySeg,　two　top-1　positions　of　five　metrics　among　10　methods　on　HRC-WHU,　two　top-1　positions　of　four　metrics　among　12　methods　with　ParaNum　＜=　30M$\le　30{\rm　M}$　on　38-Cloud),　with　100FPS　implementation　speed　averagely　for　each　224x224$224\times　224$　image.　We　propose　a　swin　transformer　based　encoder-decoder　network,　by　incorporating　a　dual　attentional　skip　connection　with　Swin-UNet　(DASUNet)　for　cloud　segmentation.　DASUNet　characterizes　the　less　salient　features　by　equipping　with　token　dual　attention　modules　among　the　skip　connection.　The　token　dual　attention　module　consists　of　token　similarity　attention　and　token　importance　attention,　which　compensates　the　ignorance　of　less　salient　features　incurred　from　traditional　attention　mechanism　during　the　stacking　of　transformer　layers.　image