{"id":4849,"date":"2022-07-20T19:23:01","date_gmt":"2022-07-20T11:23:01","guid":{"rendered":"http:\/\/139.9.1.231\/?p=4849"},"modified":"2022-07-20T19:23:03","modified_gmt":"2022-07-20T11:23:03","slug":"efficient-self-supervised-vision-pretraining-with-local-masked-reconstruction","status":"publish","type":"post","link":"http:\/\/139.9.1.231\/index.php\/2022\/07\/20\/efficient-self-supervised-vision-pretraining-with-local-masked-reconstruction\/","title":{"rendered":"Efficient Self-supervised Vision Pretraining with Local Masked Reconstruction"},"content":{"rendered":"\n

\u8bba\u6587\u5730\u5740\uff1ahttps:\/\/arxiv.org\/abs\/2206.00790<\/p>\n\n\n\n

https:\/\/github.com\/junchen14\/LoMaR<\/a><\/p>\n\n\n\n

Efficient Self-supervised Vision Pretraining with Local Masked Reconstruction<\/strong>\uff0c\u6bd4MAE\u5feb3.1\u500d\uff0c\u6bd4BEiT\u5feb5.3\u500d\uff01KAUST&\u5357\u6d0b\u7406\u5de5\u63d0\u51fa\u57fa\u4e8e\u5c40\u90e8mask\u91cd\u5efa\u7684\u9ad8\u6548\u81ea\u76d1\u7763\u89c6\u89c9\u9884\u8bad\u7ec3\u65b9\u6cd5LoMaR\uff0c\u540c\u65f6\u63d0\u9ad8\u8bad\u7ec3\u7cbe\u5ea6\u548c\u6548\u7387\uff01<\/p>\n\n\n\n

\u8ba1\u7b97\u673a\u89c6\u89c9\u7684\u81ea\u76d1\u7763\u5b66\u4e60\u53d6\u5f97\u4e86\u5de8\u5927\u7684\u8fdb\u6b65\uff0c\u6539\u8fdb\u4e86\u8bb8\u591a\u4e0b\u6e38\u89c6\u89c9\u4efb\u52a1\uff0c\u5982\u56fe\u50cf\u5206\u7c7b\u3001\u8bed\u4e49\u5206\u5272\u548c\u76ee\u6807\u68c0\u6d4b\u3002\u5176\u4e2d\uff0cMAE\u548cBEiT\u7b49\u751f\u6210\u6027\u81ea\u76d1\u7763\u89c6\u89c9\u5b66\u4e60\u65b9\u6cd5\u8868\u73b0\u51fa\u4e86\u826f\u597d\u7684\u6027\u80fd\u3002\u7136\u800c\uff0c\u5b83\u4eec\u7684\u5168\u5c40\u63a9\u853d\u91cd\u5efa\u673a\u5236\u5bf9\u8ba1\u7b97\u7684\u8981\u6c42\u5f88\u9ad8\u3002
\u4e3a\u4e86\u89e3\u51b3\u8fd9\u4e2a\u95ee\u9898\uff0c\u4f5c\u8005\u63d0\u51fa\u4e86\u5c40\u90e8\u63a9\u853d\u91cd\u5efa\uff08local masked reconstruction\uff0cLoMaR\uff09\uff0c\u8fd9\u662f\u4e00\u79cd\u7b80\u5355\u800c\u6709\u6548\u7684\u65b9\u6cd5\uff0c\u5728\u4e00\u4e2a\u7b80\u5355\u7684Transformer\u7f16\u7801\u5668\u4e0a\uff0c\u57287\u00d77\u5757\u7684\u5c0f\u7a97\u53e3\u5185\u6267\u884c\u63a9\u853d\u91cd\u5efa\uff0c\u4e0e\u6574\u4e2a\u56fe\u50cf\u7684\u5168\u5c40\u63a9\u853d\u91cd\u5efa\u76f8\u6bd4\uff0c\u63d0\u9ad8\u4e86\u6548\u7387\u548c\u7cbe\u5ea6\u4e4b\u95f4\u7684\u6743\u8861\u3002
\u5927\u91cf\u5b9e\u9a8c\u8868\u660e\uff0cLoMaR\u5728ImageNet-1K\u5206\u7c7b\u4e2d\u8fbe\u523084.1%\u7684top-1\u7cbe\u5ea6\uff0c\u4f18\u4e8eMAE 0.5%\u3002\u5728384\u00d7384\u56fe\u50cf\u4e0a\u5bf9\u9884\u8bad\u7ec3\u540e\u7684LoMaR\u8fdb\u884c\u5fae\u8c03\u540e\uff0c\u53ef\u4ee5\u8fbe\u523085.4%\u7684top-1\u7cbe\u5ea6\uff0c\u8d85\u8fc7MAE 0.6%\u3002\u5728MS COCO\u4e0a\uff0cLoMaR\u5728\u76ee\u6807\u68c0\u6d4b\u4e0a\u6bd4MAE\u597d0.5\uff0c\u5728\u5b9e\u4f8b\u5206\u5272\u4e0a\u6bd4MAE\u597d0.5\u3002LoMaR\u5728\u9884\u8bad\u7ec3\u9ad8\u5206\u8fa8\u7387\u56fe\u50cf\u4e0a\u7684\u8ba1\u7b97\u6548\u7387\u5c24\u5176\u9ad8\uff0c\u4f8b\u5982\uff0c\u5728\u9884\u8bad\u7ec3448\u00d7448\u56fe\u50cf\u4e0a\uff0cLoMaR\u6bd4MAE\u5feb3.1\u500d\uff0c\u5206\u7c7b\u7cbe\u5ea6\u9ad80.2%\u3002\u8fd9\u79cd\u5c40\u90e8\u63a9\u853d\u91cd\u5efa\u5b66\u4e60\u673a\u5236\u53ef\u4ee5\u5f88\u5bb9\u6613\u5730\u96c6\u6210\u5230\u4efb\u4f55\u5176\u4ed6\u751f\u6210\u6027\u81ea\u76d1\u7763\u5b66\u4e60\u65b9\u6cd5\u4e2d\u3002<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

\u672c\u6587\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u6a21\u578b\uff0c\u79f0\u4e3a\u5c40\u90e8\u63a9\u853d\u91cd\u5efa\u6216LoMaR\u3002\u8be5\u6a21\u578b\u5c06\u6ce8\u610f\u529b\u533a\u57df\u9650\u5236\u5728\u4e00\u4e2a\u5c0f\u7a97\u53e3\u5185\uff0c\u59827\u00d77\u7684\u56fe\u50cf\u5757\uff0c\u8fd9\u8db3\u4ee5\u8fdb\u884c\u91cd\u5efa\u3002\u5bf9\u4e8e\u90a3\u4e9b\u9700\u8981\u5728\u957f\u5e8f\u5217\u4e0a\u64cd\u4f5c\u7684\u4efb\u52a1\uff0c\u5728\u8bb8\u591aNLP\u9886\u57df\u4e2d\u4e5f\u53ef\u4ee5\u770b\u5230\u7c7b\u4f3c\u7684\u65b9\u6cd5\u3002\u5728\u89c6\u89c9\u9886\u57df\u4e5f\u63a2\u7d22\u4e86\u5c0f\u7a97\u53e3\uff0c\u4ee5\u63d0\u9ad8\u8bad\u7ec3\u548c\u63a8\u7406\u901f\u5ea6\u3002\u4f46\u4e0e\u4e4b\u524d\u7684\u89c6\u89c9Transformer\uff08\u5982Swin Transformer\uff09\u4e0d\u540c\uff0cSwin Transformer\u4e3a\u6bcf\u4e2a\u56fe\u50cf\u521b\u5efa\u5177\u6709\u56fa\u5b9a\u5750\u6807\u7684\u79fb\u52a8\u7a97\u53e3\u3002\u672c\u6587\u53d6\u800c\u4ee3\u4e4b\u7684\u662f\u5bf9\u51e0\u4e2a\u968f\u673a\u4f4d\u7f6e\u7684\u7a97\u53e3\u8fdb\u884c\u91c7\u6837\uff0c\u8fd9\u6837\u53ef\u4ee5\u66f4\u597d\u5730\u6355\u83b7\u4e0d\u540c\u7a7a\u95f4\u533a\u57df\u4e2d\u7684\u5bf9\u8c61\u3002<\/p>\n\n\n\n

\u5728\u4e0b\u56fe\u4e2d\uff0c\u4f5c\u8005\u6bd4\u8f83\u4e86LoMaR\u548cMAE\uff0c\u5e76\u6ce8\u610f\u5230\u4e24\u4e2a\u4e3b\u8981\u533a\u522b\uff1aa\uff09\u672c\u6587\u5bf9\u4e00\u4e2a\u533a\u57df\u8fdb\u884c\u4e86k\u00d7k\u4e2apatch\u91c7\u6837\uff0c\u4ee5\u8fdb\u884c\u63a9\u853d\u91cd\u5efa\uff0c\u800c\u4e0d\u662f\u4ece\u5168\u90e8patch\u4e2d\u8fdb\u884c\u91cd\u5efa\u3002\u4f5c\u8005\u53d1\u73b0\uff0c\u53ea\u9700\u4e00\u4e9b\u5c40\u90e8\u89c6\u89c9\u7ebf\u7d22\uff0c\u5c31\u8db3\u4ee5\u6062\u590d\u4e22\u5931\u7684\u4fe1\u606f\uff0c\u800c\u4e0d\u662f\u4ece\u56fe\u50cf\u4e2d\u5168\u5c4025%\u7684\u53ef\u89c1patch\u91cd\u5efa\u906e\u7f69patch\u3002b\uff09 \u672c\u6587\u5c06MAE\u4e2d\u7684\u91cd\u91cf\u7ea7\u89e3\u7801\u5668\u66ff\u6362\u4e3a\u8f7b\u91cf\u7ea7MLP\u5934\u3002\u5c06\u6240\u6709\u56fe\u50cfpatch\u76f4\u63a5\u8f93\u5165\u7f16\u7801\u5668\uff0c\u5305\u62ecmasked\u548cvisible patches\u3002\u76f8\u6bd4\u4e4b\u4e0b\uff0c\u5728MAE\u4e2d\uff0c\u53ea\u6709\u53ef\u89c1\u7684patch\u88ab\u9988\u9001\u5230\u7f16\u7801\u5668\u3002\u5b9e\u9a8c\u8868\u660e\uff0c\u8fd9\u4e9b\u7ed3\u6784\u53d8\u5316\u4e3a\u5c0f\u7a97\u53e3\u7684\u5c40\u90e8\u63a9\u853d\u91cd\u5efa\u5e26\u6765\u4e86\u66f4\u5927\u7684\u6027\u80fd\u589e\u76ca\u3002
\u7ecf\u8fc7\u5e7f\u6cdb\u7684\u5b9e\u9a8c\uff0c\u4f5c\u8005\u53d1\u73b0<\/p>\n\n\n\n

  1. LoMaR\u5728ImageNet-1K\u6570\u636e\u96c6\u4e0a\u53ef\u4ee5\u5b9e\u73b084.1 top-1 acc\uff0c\u6bd4MAE\u9ad8\u51fa0.5 acc\u3002\u6b64\u5916\uff0cLoMaR\u7684\u6027\u80fd\u53ef\u4ee5\u8fdb\u4e00\u6b65\u63d0\u9ad8\u523084.3 acc\uff0c\u5728ViT B\/8\u4e3b\u5e72\u4e0a\u53ea\u9700\u9884\u8bad\u7ec3400\u4e2a\u9636\u6bb5\uff0c\u4e0eViT B\/16\u76f8\u6bd4\uff0c\u8fd9\u4e0d\u4f1a\u5e26\u6765\u989d\u5916\u7684\u9884\u8bad\u7ec3\u6210\u672c\u3002\u5728\u5206\u8fa8\u7387\u4e3a384\u00d7384\u7684\u56fe\u50cf\u4e0a\u5bf9\u9884\u8bad\u7ec3\u6a21\u578b\u8fdb\u884c\u5fae\u8c03\u540e\uff0cLoMaR\u53ef\u4ee5\u8fbe\u523085.4 acc\uff0c\u6bd4MAE\u9ad8\u51fa0.6 acc\u3002<\/li>
  2. LoMaR\u5728\u9ad8\u5206\u8fa8\u7387\u56fe\u50cf\u9884\u8bad\u7ec3\u4e2d\u6bd4\u5176\u4ed6baseline\u66f4\u6709\u6548\uff0c\u56e0\u4e3a\u5b83\u7684\u8ba1\u7b97\u91cf\u5bf9\u4e0d\u540c\u7684\u56fe\u50cf\u5206\u8fa8\u7387\u662f\u4e0d\u53d8\u7684\u3002\u7136\u800c\uff0c\u5176\u4ed6\u65b9\u6cd5\u7684\u8ba1\u7b97\u6210\u672c\u662f\u56fe\u50cf\u5206\u8fa8\u7387\u589e\u52a0\u7684\u4e8c\u6b21\u65b9\uff0c\u8fd9\u5bfc\u81f4\u4e86\u6602\u8d35\u7684\u9884\u8bad\u7ec3\u3002\u6bd4\u5982\uff0c\u5bf9\u4e8e448\u00d7448\u56fe\u50cf\u7684\u9884\u8bad\u7ec3\uff0cLoMaR\u6bd4MAE\u5feb3.1\u500d\uff0c\u5b9e\u73b0\u4e86\u66f4\u9ad8\u7684\u5206\u7c7b\u6027\u80fd\u3002<\/li>
  3. LoMaR\u662f\u4e00\u79cd\u9ad8\u6548\u7684\u5b66\u4e60\u65b9\u6cd5\uff0c\u53ef\u4ee5\u5f88\u5bb9\u6613\u5730\u96c6\u6210\u5230\u4efb\u4f55\u5176\u4ed6\u751f\u6210\u6027\u81ea\u76d1\u7763\u5b66\u4e60\u65b9\u6cd5\u4e2d\u3002\u5c06\u672c\u6587\u7684\u5c40\u90e8\u63a9\u853d\u91cd\u5efa\u5b66\u4e60\u673a\u5236\u5b89\u88c5\u5230BEiT\u4e2d\u53ef\u4ee5\u5c06\u5176ImageNet-1K\u5206\u7c7b\u6027\u80fd\u4ece83.2\u63d0\u9ad8\u523083.4\uff0c\u53ea\u6d88\u8017\u6700\u521d\u9884\u8bad\u7ec3\u65f6\u95f4\u768435.8%\u3002LoMaR\u5728\u5176\u4ed6\u4efb\u52a1\uff08\u5982\u76ee\u6807\u68c0\u6d4b\uff09\u4e0a\u4e5f\u5177\u6709\u5f88\u5f3a\u7684\u6cdb\u5316\u80fd\u529b\u3002\u5728ViTDet\u7684\u76ee\u6807\u68c0\u6d4b\u6846\u67b6\u4e0b\uff0c\u5b83\u6bd4MAE\u7684\u6027\u80fd\u9ad8\u51fa0.5\u00a0\u3002<\/li><\/ol>\n\n\n\n

    LoMaR\u4f9d\u8d56\u4e8e\u4e00\u5806Transformer\u5757\uff0c\u901a\u8fc7\u4ece\u4e0eMAE\u7c7b\u4f3c\u7684\u635f\u574f\u56fe\u50cf\u4e2d\u6062\u590d\u7f3a\u5931\u7684patch\u6765\u9884\u8bad\u7ec3\u5927\u91cf\u672a\u6807\u8bb0\u56fe\u50cf\uff0c\u4f46LoMaR\u5728\u51e0\u4e2a\u5173\u952e\u4f4d\u7f6e\u5c06\u5176\u4e0eMAE\u533a\u5206\u5f00\u6765\u3002\u4e0a\u56fe\u5e76\u6392\u6bd4\u8f83\u4e86\u4e24\u8005\u3002\u5728\u672c\u8282\u4e2d\uff0c\u4f5c\u8005\u9996\u5148\u56de\u987eMAE\u6a21\u578b\uff0c\u7136\u540e\u63cf\u8ff0LoMaR\u548cMAE\u4e4b\u95f4\u7684\u5dee\u5f02\u3002<\/p>\n\n\n\n

    Architecture<\/strong><\/h3>\n\n\n\n

    LoMaR\u91c7\u7528\u4e86\u4e00\u79cd\u7b80\u5355\u7684\u7f16\u7801\u5668-\u7f16\u7801\u5668\u7ed3\u6784\uff0c\u800c\u4e0d\u662fMAE\u7684\u975e\u5bf9\u79f0\u7f16\u7801\u5668-\u89e3\u7801\u5668\u3002\u4f5c\u8005\u5c06\u91c7\u6837\u533a\u57df\u4e0b\u6240\u6709\u53ef\u89c1\u548cmask\u7684patch\u8f93\u5165\u7f16\u7801\u5668\u3002\u867d\u7136\u5c06mask patch\u8f93\u5165\u7f16\u7801\u5668\u53ef\u80fd\u88ab\u8ba4\u4e3a\u662f\u6bd4\u4ec5\u5c06mask patch\u8f93\u5165\u89e3\u7801\u5668\u7684MAE\u6548\u7387\u66f4\u4f4e\u7684\u64cd\u4f5c\uff0c\u4f46\u4f5c\u8005\u53d1\u73b0\uff0c\u5728\u65e9\u671f\u9636\u6bb5\u8f93\u5165mask patch\u53ef\u4ee5\u589e\u5f3a\u89c6\u89c9\u8868\u73b0\uff0c\u5e76\u4f7f\u5176\u5bf9\u8f83\u5c0f\u7684\u7a97\u53e3\u5927\u5c0f\u66f4\u5177\u9c81\u68d2\u6027\u3002\u8fd9\u53ef\u80fd\u662f\u56e0\u4e3a\u7f16\u7801\u5668\u53ef\u4ee5\u5728\u591a\u4e2a\u7f16\u7801\u5668\u5c42\u4e0e\u5176\u4ed6\u53ef\u89c1patch\u4ea4\u4e92\u540e\uff0c\u5c06mask patch\u8f6c\u6362\u56de\u5176\u539f\u59cbRGB\u8868\u793a\u3002\u9690\u85cf\u5c42\u4e2d\u6062\u590d\u7684mask patch\u53ef\u4ee5\u9690\u5f0f\u5730\u5bf9\u56fe\u50cf\u8868\u793a\u4f5c\u51fa\u8d21\u732e\u3002\u56e0\u6b64\uff0c\u672c\u6587\u5728LoMaR\u4e2d\u4fdd\u7559mask patch\u4f5c\u4e3a\u7f16\u7801\u5668\u8f93\u5165\u3002<\/p>\n\n\n\n

    Relative positional encoding<\/strong><\/h3>\n\n\n\n

    LoMaR\u5728MAE\u4e2d\u5e94\u7528\u76f8\u5bf9\u4f4d\u7f6e\u7f16\u7801\uff08RPE\uff09\u800c\u4e0d\u662f\u7edd\u5bf9\u4f4d\u7f6e\u7f16\u7801\u3002\u4f5c\u8005\u5e94\u7528\u4e86\u4e0a\u4e0b\u6587RPE\uff0c\u5728\u8ba1\u7b97\u81ea\u6ce8\u610f\u65f6\uff0c\u5b83\u4e3a\u6bcf\u4e2a\u67e5\u8be2i\u548c\u952ej\u5f15\u5165\u4e86\u4e00\u4e2a\u53ef\u5b66\u4e60\u7684\u5411\u91cf\u3002<\/p>\n\n\n\n

    Implementation<\/strong><\/h3>\n\n\n\n

    \u7ed9\u5b9a\u4e00\u5e45\u56fe\u50cf\uff0c\u9996\u5148\u5c06\u5176\u5212\u5206\u4e3a\u51e0\u4e2a\u4e0d\u91cd\u53e0\u7684patch\u3002\u6bcf\u4e2apatch\u7ebf\u6027\u6295\u5f71\u5230\u5d4c\u5165\u4e2d\u3002\u4f5c\u8005\u5728\u4e0d\u540c\u7684\u7a7a\u95f4\u4f4d\u7f6e\u968f\u673a\u62bd\u53d6\u51e0\u4e2a\u65b9\u5f62\u7684K\u00d7K \u4e2apatch\u3002\u7136\u540e\uff0c\u5c06\u6bcf\u4e2a\u7a97\u53e3\u4e2d\u56fa\u5b9a\u767e\u5206\u6bd4\u7684patch\u5f52\u96f6\u3002\u7136\u540e\uff0c\u5c06\u6240\u6709patch\u4ece\u6bcf\u4e2a\u7a97\u53e3\u6309\u987a\u5e8f\u63d0\u4f9b\u7ed9\u7f16\u7801\u5668\u3002\u7f16\u7801\u5668\u5728\u81ea\u6ce8\u610f\u5c42\u4e2d\u5e94\u7528\u53ef\u5b66\u4e60\u7684\u76f8\u5bf9\u4f4d\u7f6e\u7f16\u7801\u3002\u4f5c\u8005\u7528\u4e00\u4e2a\u7b80\u5355\u7684MLP\u5934\u5c06\u7f16\u7801\u5668\u8f93\u51fa\u7684\u6f5c\u5728\u8868\u793a\u8f6c\u6362\u56de\u5176\u539f\u59cb\u7279\u5f81\u7ef4\uff0c\u7136\u540e\u7528\u5f52\u4e00\u5316\u7684ground-truth\u56fe\u50cf\u8ba1\u7b97\u5747\u65b9\u8bef\u5dee\u3002<\/p>\n\n\n\n

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    \u8bba\u6587\u5730\u5740\uff1ahttps:\/\/arxiv.org\/abs\/2206.00790 https:\/\/github.co … \u7ee7\u7eed\u9605\u8bfbEfficient Self-supervised Vision Pretraining with Local Masked Reconstruction<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[9],"tags":[],"_links":{"self":[{"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/posts\/4849"}],"collection":[{"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/comments?post=4849"}],"version-history":[{"count":6,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/posts\/4849\/revisions"}],"predecessor-version":[{"id":4887,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/posts\/4849\/revisions\/4887"}],"wp:attachment":[{"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/media?parent=4849"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/categories?post=4849"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/139.9.1.231\/index.php\/wp-json\/wp\/v2\/tags?post=4849"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}