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1\u3001\u7b80\u4ecb<\/h3>\n
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<\/p>\n
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\n
- \n
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- \n
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- \n
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2\u3001\u672c\u6587\u65b9\u6cd5<\/h3>\n
<\/p>\n
\u5982\u56fe2\u6240\u793a\uff0c\u5728\u7ed9\u5b9a\u57fa\u672c\u68c0\u6d4b\u5668\u7684\u60c5\u51b5\u4e0b\uff0c\u6574\u4e2aCEASC\u7f51\u7edc\u65e8\u5728\u901a\u8fc7\u5f00\u53d1\u4e0a\u4e0b\u6587\u589e\u5f3a\u7a00\u758f\u5377\u79ef\uff08CESC\uff09\u6765\u4f18\u5316FPN\u4e2d\u4e0d\u540c\u5c42\u7684\u68c0\u6d4b\u5934\uff0c\u8be5\u5377\u79ef\u901a\u8fc7\u8f7b\u91cf\u7ea7\u5377\u79ef\u6a21\u5757\u548c\u4e0a\u4e0b\u6587\u589e\u5f3a\u7ec4\u5f52\u4e00\u5316\uff08CEGN\uff09\u5c42\u5c06\u7126\u70b9\u4fe1\u606f\u4e0e\u5168\u5c40\u4e0a\u4e0b\u6587\u96c6\u6210\u3002\u8bbe\u8ba1\u4e86\u81ea\u9002\u5e94\u591a\u5c42\u63a9\u6a21\uff08AMM\uff09\u6a21\u5757\uff0c\u4f7f\u6a21\u578b\u80fd\u591f\u81ea\u9002\u5e94\u5730\u751f\u6210\u5177\u6709\u8db3\u591fmask ratio\u7684\u63a9\u6a21\uff0c\u4ece\u800c\u5728\u7cbe\u5ea6\u548c\u6548\u7387\u4e0a\u8fbe\u5230\u66f4\u597d\u7684\u5e73\u8861\u3002<\/p>\n
2.1\u3001\u4e0a\u4e0b\u6587\u589e\u5f3a\u7684\u7a00\u758f\u5377\u79ef<\/h4>\n
1\u3001\u7a00\u758f\u5377\u79ef<\/p>\n
\u65e0\u4eba\u673a\u56fe\u50cf\u4e0a\u7684\u5927\u591a\u6570\u73b0\u6709\u68c0\u6d4b\u5668\u90fd\u4f7f\u7528\u5bc6\u96c6\u7684\u68c0\u6d4b\u5934\uff0c\u5bf9\u6574\u4e2a\u7279\u5f81\u56fe\u8fdb\u884c\u5377\u79ef\u3002\u5c3d\u7ba1\u5145\u5206\u63a2\u7d22\u89c6\u89c9\u7ebf\u7d22\u6709\u52a9\u4e8e\u63a2\u6d4b\u5fae\u5c0f\u76ee\u6807\uff0c\u4f46\u5bc6\u96c6\u7684\u5934\u90e8\u9700\u8981\u66f4\u591a\u7684\u8ba1\u7b97\uff0c\u8fd9\u4e0d\u9002\u7528\u4e8e\u8d44\u6e90\u53d7\u9650\u7684\u65e0\u4eba\u673a\u5e73\u53f0\u3002<\/p>\n
\u4e0e\u6b64\u540c\u65f6\uff0c\u524d\u666f\u533a\u57df\u53ea\u5360\u636e\u65e0\u4eba\u673a\u83b7\u53d6\u7684\u5e27\u7684\u4e00\u5c0f\u90e8\u5206\uff0c\u5982\u56fe1\u6240\u793a\uff0c\u8fd9\u8868\u660e\u5bc6\u96c6\u7684\u5934\u90e8\u5bf9\u80cc\u666f\u8fdb\u884c\u4e86\u5927\u91cf\u7684\u8ba1\u7b97\u64cd\u4f5c\uff0c\u5176\u4e2d\u5305\u542b\u7684\u5bf9\u76ee\u6807\u68c0\u6d4b\u6709\u7528\u7684\u4fe1\u606f\u8981\u5c11\u5f97\u591a\u3002\u8fd9\u4e00\u89c2\u5bdf\u7ed3\u679c\u63ed\u793a\u4e86\u4ec5\u901a\u8fc7\u5bf9\u524d\u666f\u533a\u57df\u8fdb\u884c\u8ba1\u7b97\u6765\u52a0\u901f\u68c0\u6d4b\u5934\u7684\u6f5c\u529b\u3002<\/p>\n
\u6700\u8fd1\u63d0\u51fa\u4e86\u7a00\u758f\u5377\u79ef\uff08SC\uff09\uff0c\u5b83\u901a\u8fc7\u4f7f\u7528\u7a00\u758f\u63a9\u6a21\u6765\u5b66\u4e60\u5bf9\u524d\u666f\u533a\u57df\u7684\u64cd\u4f5c\uff0c\u5e76\u88ab\u8bc1\u660e\u5728\u52a0\u901f\u5404\u79cd\u89c6\u89c9\u4efb\u52a1\u7684\u63a8\u7406\u9636\u6bb5\u65b9\u9762\u662f\u6709\u6548\u7684\u3002\u53d7\u4ed6\u4eec\u7684\u542f\u53d1\uff0c\u4f5c\u8005\u6784\u5efa\u4e86\u57fa\u4e8eSC\u7684\u7f51\u7edc\u3002<\/p>\n
\u5177\u4f53\u6765\u8bf4\uff0c\u7ed9\u5b9aFPN\u7b2c\u5c42\u7684\u7279\u5f81\u6620\u5c04\uff0cSC\u91c7\u7528\u7531\u5171\u4eab\u6838\u7ec4\u6210\u7684\u63a9\u7801\u7f51\u7edc\uff0c\u5176\u4e2dB\u3001C\u3001H\u3001W\u5206\u522b\u8868\u793a\u6279\u91cf\u5927\u5c0f\u3001\u901a\u9053\u5927\u5c0f\u3001\u9ad8\u5ea6\u548c\u5bbd\u5ea6\u3002\u57fa\u4e8e\u5bf9\u8fdb\u884c\u5377\u79ef\uff0c\u751f\u6210Soft\u7279\u5f81\uff0c\u901a\u8fc7\u4f7f\u7528Gumbel-Softmax\u5c06\u5176\u8fdb\u4e00\u6b65\u8f6c\u5316\u4e3a\u63a9\u7801\u77e9\u9635\uff0c\u516c\u5f0f\u5982\u4e0b\uff1a<\/p>\n
<\/p>\n
\u5176\u4e2d\u8868\u793a2\u4e2a\u968f\u673agumbel\u566a\u58f0\uff0c\u03c3\u8868\u793aSigmoid\u51fd\u6570\uff0c\u03c4\u662fGumbel-Softmax\u4e2d\u5bf9\u5e94\u7684\u6e29\u5ea6\u53c2\u6570\u3002<\/p>\n
\u6839\u636e\u7b49\u5f0f\uff081\uff09\uff0c\u53ea\u6709\u63a9\u7801\u503c\u4e3a1\u7684\u533a\u57df\u5728\u63a8\u7406\u8fc7\u7a0b\u4e2d\u6d89\u53ca\u5377\u79ef\uff0c\u4ece\u800c\u964d\u4f4e\u4e86\u603b\u4f53\u8ba1\u7b97\u6210\u672c\u3002\u7684\u7a00\u758f\u6027\u7531\u63a9\u6a21\u6bd4\u63a7\u5236\uff0c\u5728\u73b0\u6709\u7814\u7a76\u4e2d\uff0c\u63a9\u6a21\u6bd4\u901a\u5e38\u624b\u52a8\u8bbe\u7f6e\u4e3a\u5927\u4e8e0.9\u3002<\/p>\n
\u7531\u4e8e\u57fa\u672c\u68c0\u6d4b\u5668\uff08\u8fd9\u91cc\u4ee5GFL V1\u4e3a\u4f8b\uff09\u5728\u68c0\u6d4b\u6846\u67b6\u4e2d\u6709\u4e00\u4e2a\u5206\u7c7b\u5934\u548c\u4e00\u4e2a\u56de\u5f52\u5934\uff0c\u8003\u8651\u5230\u5b83\u4eec\u901a\u5e38\u5173\u6ce8\u4e0d\u540c\u7684\u533a\u57df\uff0c\u8fd9\u91cc\u4e3a\u6bcf\u4e2a\u5934\u5206\u522b\u5f15\u5165\u4e86\u4e00\u4e2a\u63a9\u7801\u7f51\u7edc\u3002\u6bcf\u4e2a\u68c0\u6d4b\u5934\u91c7\u75284\u4e2aConv-GN-ReLU\u5c42\u548c1\u4e2a\u5355\u72ec\u5377\u79ef\u5c42\u8fdb\u884c\u9884\u6d4b\uff0c\u5176\u4e2d\u7528SC\u5c42\u53d6\u4ee3\u4f20\u7edf\u5377\u79ef\u5c42\u3002<\/p>\n
2\u3001\u4e0a\u4e0b\u6587\u589e\u5f3a<\/p>\n
\u5982FGD\u6240\u8ff0\uff0c\u4e0a\u4e0b\u6587\u7ebf\u7d22\uff08\u4f8b\u5982\uff0c\u76ee\u6807\u5468\u56f4\u7684\u80cc\u666f\uff09\u6709\u5229\u4e8e\u76ee\u6807\u68c0\u6d4b\uff1b\u7136\u800c\uff0cSC\u4ec5\u5728\u524d\u666f\u4e0a\u6267\u884c\u5377\u79ef\uff0c\u5e76\u653e\u5f03\u5177\u6709\u6709\u7528\u4fe1\u606f\u7684\u80cc\u666f\uff0c\u8fd9\u53ef\u80fd\u4f1a\u7834\u574f\u6574\u4f53\u51c6\u786e\u6027\uff0c\u5c24\u5176\u662f\u5728\u65e0\u4eba\u673a\u56fe\u50cf\u4e2d\u5b58\u5728\u5fae\u5c0f\u76ee\u6807\u7684\u60c5\u51b5\u4e0b\u3002\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0cStochastic sampling-interpolation network\u8bd5\u56fe\u901a\u8fc7\u63d2\u503c\u6765\u6062\u590d\u5468\u56f4\u7684\u4e0a\u4e0b\u6587\uff0c\u4f46\u8fd9\u662f\u4e0d\u53ef\u9760\u7684\uff0c\u56e0\u4e3a\u7126\u70b9\u548c\u80cc\u666f\u533a\u57df\u8868\u73b0\u51fa\u5f88\u5927\u7684\u5dee\u5f02\u3002<\/p>\n
\u5728\u8fd9\u9879\u5de5\u4f5c\u4e2d\u63d0\u51fa\u4e86\u4e00\u4e2a\u8f7b\u91cf\u7ea7\u7684CESC\u6a21\u5757\uff0c\u8054\u5408\u5229\u7528\u7126\u70b9\u4fe1\u606f\u548c\u5168\u5c40\u4e0a\u4e0b\u6587\u8fdb\u884c\u589e\u5f3a\uff0c\u540c\u65f6\u63d0\u9ad8\u540e\u7eed\u8ba1\u7b97\u7684\u7a33\u5b9a\u6027\u3002\u5982\u56fe2\u6240\u793a\uff0c\u5bf9\u7279\u5f81\u56fe\u5e94\u7528 point-wise\u5377\u79ef\uff0c\u751f\u6210\u5168\u5c40\u4e0a\u4e0b\u6587\u7279\u5f81\u3002\u7531\u4e8eSC\u53ea\u5904\u7406\u7684\u5c11\u6570\u5143\u7d20\uff0c\u56e0\u6b64Gi\u5728\u7ecf\u8fc7\u591a\u8f6eSC\u540e\u8d8b\u4e8e\u7a33\u5b9a\uff0c\u800c\u4e0d\u9700\u8981\u592a\u591a\u989d\u5916\u7684\u8ba1\u7b97\u5f00\u9500\u3002<\/p>\n
\u4f5c\u4e3aSC\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\uff0c\u4f5c\u8005\u5c06\u5168\u5c40\u4e0a\u4e0b\u6587\u4fe1\u606f\u5d4c\u5165\u5230SparseConvolution-GN-ReLU\u5c42\u4e2d\uff0c\u8be5\u5c42\u4ee5\u7279\u5f81\u5730\u56fe\u3001\u63a9\u7801\u548c\u5168\u5c40\u7279\u5f81\u4f5c\u4e3a\u8f93\u5165\uff0c\u5176\u4e2d\u8868\u793a\u7b2c\u4e2aSparseEnvolution-GN-ReLU\u56fe\u5c42\u3002<\/p>\n
CEASA\u6ca1\u6709\u50cf\u4f20\u7edf\u7684SC\u90a3\u6837\u4f7f\u7528\u6fc0\u6d3b\u5143\u7d20\u6765\u8ba1\u7b97\u7ec4\u5f52\u4e00\u5316\u7684\u7edf\u8ba1\u6570\u636e\uff0c\u800c\u662f\u91c7\u7528\u7684\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\u6765\u8fdb\u884c\u5f52\u4e00\u5316\uff0c\u65e8\u5728\u8865\u507f\u7f3a\u5931\u7684\u4e0a\u4e0b\u6587\u3002\u5047\u8bbe\u662f\u5728\u4e0a\u5e94\u7528SC\u540e\u7684\u8f93\u51fa\u7279\u5f81\u56fe\uff0cCE-GN\u83b7\u5f97\u4e0a\u4e0b\u6587\u589e\u5f3a\u7279\u5f81\u5982\u4e0b<\/p>\n
<\/p>\n
\u5176\u4e2d*mean[\u00b7]\u548cstd[\u00b7]*\u5206\u522b\u8868\u793a\u5e73\u5747\u503c\u548c\u6807\u51c6\u5dee\uff0c\u548c\u662f\u53ef\u5b66\u4e60\u7684\u53c2\u6570\u3002<\/p>\n
\u4e3a\u4e86\u8fdb\u4e00\u6b65\u51cf\u8f7bSC\u4e2d\u7684\u4fe1\u606f\u635f\u5931\uff0c\u4f7f\u8bad\u7ec3\u8fc7\u7a0b\u66f4\u52a0\u7a33\u5b9a\uff0c\u4f5c\u8005\u5728\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u9664\u4e86\u7a00\u758f\u5377\u79ef\u4e4b\u5916\uff0c\u8fd8\u4fdd\u7559\u4e86\u6b63\u5e38\u7684\u5bc6\u96c6\u5377\u79ef\uff0c\u751f\u6210\u4e86\u5728\u5168\u8f93\u5165\u7279\u5f81\u56fe\u4e0a\u5377\u79ef\u7684\u7279\u5f81\u56fe\u3002\u7136\u540e\uff0c\u4f7f\u7528\u6765\u901a\u8fc7\u4f18\u5316MSE\u635f\u5931\u6765\u589e\u5f3a\u7a00\u758f\u7279\u5f81\u56fe\uff0c\u5982\u4e0b\u6240\u793a\uff1a<\/p>\n
<\/p>\n
\u5176\u4e2dL\u662fFPN\u4e2d\u7684\u5c42\u7684\u6570\u91cf\u3002<\/p>\n
\u6700\u540e\uff0c\u5728\u6fc0\u6d3b\u5c42\u4e4b\u524d\u91c7\u7528\u4e86\u4e00\u79cd\u6b8b\u5dee\u7ed3\u6784\uff0c\u5c06\u6dfb\u52a0\u5230\u4e2d\uff0c\u5373\uff0c\u8fd9\u52a0\u5f3a\u4e86\u4e0a\u4e0b\u6587\u4fdd\u5b58\u3002CESC\u6a21\u5757\u548cCE-GN\u5c42\u7684\u5b8c\u6574\u67b6\u6784\u5982\u56fe2\u6240\u793a\u3002<\/p>\n
2.2\u3001\u81ea\u9002\u5e94\u591a\u5c42\u5c4f\u853d<\/h4>\n
\u5728\u6ca1\u6709\u4efb\u4f55\u989d\u5916\u7ea6\u675f\u7684\u60c5\u51b5\u4e0b\uff0c\u7a00\u758f\u68c0\u6d4b\u5668\u503e\u5411\u4e8e\u751f\u6210\u5177\u6709\u5927\u6fc0\u6d3b\u6bd4\uff08\u6216\u5c0f\u63a9\u6a21\u6bd4\uff09\u7684\u63a9\u6a21\u4ee5\u83b7\u5f97\u66f4\u9ad8\u7684\u7cbe\u5ea6\uff0c\u4ece\u800c\u589e\u52a0\u4e86\u603b\u4f53\u8ba1\u7b97\u6210\u672c\u3002<\/p>\n
\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u5927\u591a\u6570\u73b0\u6709\u7684\u5c1d\u8bd5\u90fd\u4f7f\u7528\u56fa\u5b9a\u7684\u6fc0\u6d3b\u6bd4\u7387\u3002\u7136\u800c\uff0c\u7531\u4e8e\u822a\u7a7a\u56fe\u50cf\u7684\u524d\u666f\u8868\u73b0\u51fa\u4e25\u91cd\u7684\u6ce2\u52a8\uff0c\u56fa\u5b9a\u6bd4\u7387\u5bb9\u6613\u5bfc\u81f4\u8ba1\u7b97\u7684\u663e\u8457\u589e\u52a0\u6216\u7531\u4e8e\u524d\u666f\u533a\u57df\u7684\u8986\u76d6\u4e0d\u8db3\u800c\u5bfc\u81f4\u7cbe\u5ea6\u7684\u964d\u4f4e\u3002\u4e3a\u4e86\u5728\u7cbe\u5ea6\u548c\u6548\u7387\u4e4b\u95f4\u8fdb\u884c\u6743\u8861\uff0c\u4f5c\u8005\u63d0\u51fa\u4e86AMM\u65b9\u6848\u6765\u81ea\u9002\u5e94\u5730\u63a7\u5236\u6fc0\u6d3b\u7387\uff08\u6216\u53cd\u5411\u63a7\u5236\u63a9\u6a21\u7387\uff09\u3002<\/p>\n
\u5177\u4f53\u5730\uff0cAMM\u9996\u5148\u57fa\u4e8eGT\u6807\u7b7e\u6765\u4f30\u8ba1\u6700\u4f18\u63a9\u7801\u6bd4\u3002\u901a\u8fc7\u5229\u7528\u6807\u7b7e\u5206\u914d\u6280\u672f\uff0c\u5bf9\u4e8e\u7b2ci\u4e2aFPN\u5c42\uff0c\u83b7\u5f97\u4e86GT\u5206\u7c7b\u7ed3\u679c\uff0c\u5176\u4e2d\u8868\u793a\u5305\u62ec\u80cc\u666f\u5728\u5185\u7684\u7c7b\u522b\u6570\u91cf\uff1b\u548c\u5206\u522b\u6307\u793a\u7279\u5f81\u56fe\u7684\u9ad8\u5ea6\u548c\u5bbd\u5ea6\u3002\u7b2c\u4e2aFPN\u5c42\u4e2d\u7684\u6700\u4f73\u6fc0\u6d3b\u7387\u4f30\u8ba1\u4e3a<\/p>\n
<\/p>\n
\u5176\u4e2d\u548c\u5206\u522b\u6307\u793a\u5c5e\u4e8e\u6b63\uff08\u524d\u666f\uff09\u5b9e\u4f8b\u7684\u50cf\u7d20\u7684\u6570\u76ee\u548c\u6240\u6709\u50cf\u7d20\u7684\u6570\u76ee\u3002<\/p>\n
\u4e3a\u4e86\u5f15\u5bfc\u7f51\u7edc\u81ea\u9002\u5e94\u5730\u751f\u6210\u5177\u6709\u8db3\u591f\u63a9\u7801\u6bd4\u7684\u63a9\u7801\uff0c\u91c7\u7528\u4ee5\u4e0b\u635f\u5931<\/p>\n
<\/p>\n
\u5176\u4e2d\u548c\u8868\u793a\u63a9\u6a21\u7684\u6fc0\u6d3b\u7387\u3002\u901a\u8fc7\u6700\u5c0f\u5316\uff0c\u88ab\u8feb\u9075\u5b88\u4e0e\u771f\u5b9e\u524d\u666f\u6bd4\u7387\u76f8\u540c\u7684\u6fc0\u6d3b\u6bd4\u7387\uff0c\u4ece\u800c\u6709\u52a9\u4e8e\u751f\u6210\u8db3\u591f\u7684\u63a9\u6a21\u6bd4\u7387\u3002<\/p>\n
\u901a\u8fc7\u6dfb\u52a0\u4f20\u7edf\u7684\u68c0\u6d4b\u635f\u5931\uff0c\u5c06\u6574\u4f53\u8bad\u7ec3\u635f\u5931\u516c\u5f0f\u5316\u5982\u4e0b\uff1a<\/p>\n
<\/p>\n
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3\u3001\u5b9e\u9a8c<\/h3>\n
3.1\u3001\u6d88\u878d\u5b9e\u9a8c<\/h4>\n
1\u3001\u5728CESC\u548cAMM\u4e0a<\/p>\n
<\/p>\n
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2\u3001\u5173\u4e8e\u5728CESC\u4e2d\u7684\u8be6\u7ec6\u8bbe\u8ba1<\/p>\n
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<\/p>\n
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<\/p>\n
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<\/p>\n
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3\u3001\u5173\u4e8eAMM\u7684\u8be6\u7ec6\u5206\u6790<\/p>\n
<\/p>\n
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<\/p>\n
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3.2\u3001SOTA\u5bf9\u6bd4<\/h4>\n
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4\u3001\u53c2\u8003<\/h3>\n
[1].Adaptive Sparse Convolutional Networks with Global Context Enhancement for Faster Object Detection on Drone Images.
[2].https:\/\/github.com\/Cuogeihong\/CEASC.<\/p>\n<\/p>\n<\/li>\n<\/ol>\n
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