{"id":2921,"date":"2025-04-21T13:53:18","date_gmt":"2025-04-21T05:53:18","guid":{"rendered":"https:\/\/www.gnn.club\/?p=2921"},"modified":"2025-04-21T13:53:18","modified_gmt":"2025-04-21T05:53:18","slug":"clip%ef%bc%9a%e5%9f%ba%e4%ba%8e%e5%af%b9%e6%af%94%e5%ad%a6%e4%b9%a0%e7%9a%84%e5%9b%be%e5%83%8f%e6%96%87%e6%9c%ac%e9%a2%84%e8%ae%ad%e7%bb%83%e6%a8%a1%e5%9e%8b","status":"publish","type":"post","link":"http:\/\/gnn.club\/?p=2921","title":{"rendered":"CLIP\uff1a\u57fa\u4e8e\u5bf9\u6bd4\u5b66\u4e60\u7684\u56fe\u50cf\u6587\u672c\u9884\u8bad\u7ec3\u6a21\u578b"},"content":{"rendered":"\n

1. \u6458\u8981<\/h2>\n\n\n\n

\u76ee\u524d\u6700\u5148\u8fdb\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\u7cfb\u7edf\u901a\u5e38\u901a\u8fc7\u9884\u5b9a\u4e49\u7684\u5bf9\u8c61\u7c7b\u522b\u8fdb\u884c\u8bad\u7ec3\u3002\u8fd9\u79cd\u53d7\u9650\u7684\u76d1\u7763\u5f62\u5f0f\u4e25\u91cd\u9650\u5236\u4e86\u6a21\u578b\u7684\u901a\u7528\u6027\u548c\u5b9e\u7528\u6027\u2014\u2014\u56e0\u4e3a\u4efb\u4f55\u65b0\u89c6\u89c9\u6982\u5ff5\u7684\u8bc6\u522b\u90fd\u9700\u8981\u989d\u5916\u7684\u6807\u6ce8\u6570\u636e\u3002\u800c\u76f4\u63a5\u4ece\u56fe\u50cf\u76f8\u5173\u7684\u539f\u59cb\u6587\u672c\u4e2d\u5b66\u4e60\uff0c\u5219\u63d0\u4f9b\u4e86\u4e00\u79cd\u66f4\u5177\u6f5c\u529b\u7684\u66ff\u4ee3\u65b9\u6848\uff0c\u5b83\u80fd\u5229\u7528\u66f4\u5e7f\u6cdb\u7684\u76d1\u7763\u4fe1\u53f7\u6765\u6e90\u3002<\/p>\n\n\n\n

\u8bba\u6587\u7684\u7814\u7a76\u8868\u660e\uff0c\u4e00\u4e2a\u7b80\u5355\u7684\u9884\u8bad\u7ec3\u4efb\u52a1\u2014\u2014\u5373\u9884\u6d4b\u54ea\u6bb5\u6587\u672c\u63cf\u8ff0\u4e0e\u54ea\u5f20\u56fe\u50cf\u5339\u914d\u2014\u2014\u53ef\u4ee5\u9ad8\u6548\u3001\u53ef\u6269\u5c55\u5730\u4ece\u4e92\u8054\u7f51\u6536\u96c6\u76844\u4ebf\u4e2a\uff08\u56fe\u50cf\uff0c\u6587\u672c\uff09\u914d\u5bf9\u6570\u636e\u96c6\u4e2d\uff0c\u4ece\u5934\u5f00\u59cb\u5b66\u4e60\u6700\u5148\u8fdb\u7684\u56fe\u50cf\u8868\u5f81\u3002\u9884\u8bad\u7ec3\u5b8c\u6210\u540e\uff0c\u901a\u8fc7\u81ea\u7136\u8bed\u8a00\u5373\u53ef\u8c03\u7528\u5df2\u5b66\u4e60\u7684\u89c6\u89c9\u6982\u5ff5\uff08\u6216\u63cf\u8ff0\u65b0\u6982\u5ff5\uff09\uff0c\u4ece\u800c\u5b9e\u73b0\u6a21\u578b\u5728\u4e0b\u6e38\u4efb\u52a1\u4e2d\u7684\u96f6\u6837\u672c\u8fc1\u79fb\u3002<\/p>\n\n\n\n

\u8bba\u6587\u5730\u5740\uff1ahttps:\/\/arxiv.org\/pdf\/2103.00020<\/a><\/p>\n\n\n\n

2. \u65b9\u6cd5<\/h2>\n\n\n\n

\uff08\u56fe1\uff09\u8be6\u7ec6\u5c55\u793a\u4e86CLIP\uff08Contrastive Language-Image Pre-training\uff09\u6a21\u578b\u7684\u6838\u5fc3\u5de5\u4f5c\u6d41\u7a0b\uff0c\u5206\u4e3a\u4e09\u4e2a\u5173\u952e\u9636\u6bb5\u3002\u56fe\u793a\u7b26\u53f7\u8be6\u89e3<\/strong>\uff1a<\/p>\n\n\n\n

\u7b26\u53f7<\/td>\u542b\u4e49<\/td>\u6280\u672f\u4f5c\u7528<\/td><\/tr>
l_i<\/td>\u7b2ci\u5f20\u56fe\u50cf\u7684\u7279\u5f81\u5411\u91cf<\/td>\u89c6\u89c9\u6982\u5ff5\u7684\u5411\u91cf\u5316\u8868\u793a<\/td><\/tr>
T_j<\/td>\u7b2cj\u4e2a\u6587\u672c\u7684\u7279\u5f81\u5411\u91cf<\/td>\u8bed\u8a00\u6982\u5ff5\u7684\u5411\u91cf\u5316\u8868\u793a<\/td><\/tr>
l_i-T_j<\/td>\u56fe\u50cf-\u6587\u672c\u5bf9\u76f8\u4f3c\u5ea6\u5f97\u5206<\/td>\u5bf9\u6bd4\u5b66\u4e60\u7684\u4f18\u5316\u76ee\u6807<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n
2.1. \u5bf9\u6bd4\u9884\u8bad\u7ec3\u9636\u6bb5\uff08Contrastive Pre-training\uff09<\/strong><\/h5>\n\n\n\n

\u7ed3\u6784\u8bf4\u660e\uff1a<\/strong><\/p>\n\n\n\n

    \n
  • \u6b63\u6837\u672c\u91c7\u6837\uff1a<\/strong>\u5c06\u914d\u5bf9\u7684\u56fe\u6587\u91c7\u6837\u4f5c\u4e3a\u6b63\u6837\u672c\u3002<\/li>\n\n\n\n
  • \u8d1f\u6837\u672c\u91c7\u6837<\/strong>\uff1a\u67d0\u56fe\u7247\u4e0e\u540c\u4e00\u6279\u6b21\u5185\u5176\u4ed6\u56fe\u50cf\u7684\u6587\u672c\u63cf\u8ff0\u7ec4\u6210\u8d1f\u6837\u672c\u3002<\/li>\n\n\n\n
  • \u56fe\u50cf\u7f16\u7801\u5668\uff08Image Encoder\uff09<\/strong>\uff1a\u5c06\u8f93\u5165\u56fe\u50cf\u8f6c\u6362\u4e3a\u56fe\u50cf\u7279\u5f81\u5411\u91cf\uff08l\u2081, l\u2082... l_N\uff09\uff0c\u5177\u4f53\u505a\u6cd5\u7c7b\u4f3cVIT\u6a21\u578b<\/li>\n\n\n\n
  • \u6587\u672c\u7f16\u7801\u5668\uff08Text Encoder\uff09<\/strong>\uff1a\u5c06\u5bf9\u5e94\u7684\u6587\u672c\u63cf\u8ff0\uff08T\u2081, T\u2082... T_N\uff09\u8f6c\u6362\u4e3a\u6587\u672c\u7279\u5f81\u5411\u91cf\uff0c\u5177\u4f53\u505a\u6cd5\u7c7b\u4f3cTransformer\u6a21\u578b<\/li>\n\n\n\n
  • \u5bf9\u6bd4\u5b66\u4e60\u77e9\u9635<\/strong>\uff1a
    \u77e9\u9635\u4e2d\u6bcf\u4e2a\u5143\u7d20\u8868\u793a\u56fe\u50cf\u7279\u5f81\uff08$l_i$\uff09\u4e0e\u6587\u672c\u7279\u5f81\uff08$T_j$\uff09\u7684\u76f8\u4f3c\u5ea6\u5f97\u5206\uff08$l_i-T_j$\uff09\u3002\u6a21\u578b\u901a\u8fc7\u6700\u5927\u5316\u6b63\u786e\u914d\u5bf9\uff08\u5bf9\u89d2\u7ebf\u5143\u7d20\uff09\u7684\u76f8\u4f3c\u5ea6\uff0c\u6700\u5c0f\u5316\u9519\u8bef\u914d\u5bf9\uff08\u975e\u5bf9\u89d2\u7ebf\u5143\u7d20\uff09\u7684\u76f8\u4f3c\u5ea6\u8fdb\u884c\u8bad\u7ec3\u3002<\/li>\n<\/ul>\n\n\n\n
    2.2. \u96f6\u6837\u672c\u5206\u7c7b\u5668\u751f\u6210\uff08Create Dataset Classifier\uff09<\/strong><\/h5>\n\n\n\n

    \u6d41\u7a0b\u8bf4\u660e\uff1a<\/strong><\/p>\n\n\n\n

      \n
    • \u6807\u7b7e\u6587\u672c\u6a21\u677f\u5316<\/strong>\uff1a\u5c06\u76ee\u6807\u6570\u636e\u96c6\u7684\u7c7b\u522b\uff08\u5982\"plane, car, dog, bird\"\uff09\u901a\u8fc7\u63d0\u793a\u5de5\u7a0b\u8f6c\u6362\u4e3a\u81ea\u7136\u8bed\u8a00\u63cf\u8ff0\uff08\u5982\"A photo of a [object]\"\uff09<\/li>\n\n\n\n
    • \u6587\u672c\u7f16\u7801\u5668\u5d4c\u5165<\/strong>\uff1a\u901a\u8fc7\u9884\u8bad\u7ec3\u597d\u7684\u6587\u672c\u7f16\u7801\u5668\uff0c\u5c06\u8fd9\u4e9b\u6a21\u677f\u5316\u63cf\u8ff0\u8f6c\u6362\u4e3a\u7c7b\u522b\u7279\u5f81\u5411\u91cf\uff0c\u5373\"\u6587\u672c\u539f\u578b\uff08T\u2081...$T_N$\uff09\"\u3002<\/li>\n<\/ul>\n\n\n\n

      \u5173\u952e\u521b\u65b0\uff1a<\/strong><\/p>\n\n\n\n

        \n
      • \u52a8\u6001\u5206\u7c7b\u5934\u751f\u6210<\/strong>\uff1a\u4f20\u7edf\u6a21\u578b\u9700\u8981\u56fa\u5b9a\u7c7b\u522b\u6570\u7684\u7ebf\u6027\u5206\u7c7b\u5c42\uff0c\u800cCLIP\u901a\u8fc7\u63d0\u793a\u5de5\u7a0b(prompt)\u4e0e\u6587\u672c\u7f16\u7801\u5668\u5b9e\u65f6\u751f\u6210\u4e0e\u7c7b\u522b\u63cf\u8ff0\u5bf9\u5e94\u7684\u5206\u7c7b\u6743\u91cd\u3002<\/li>\n<\/ul>\n\n\n\n
        2.3. \u96f6\u6837\u672c\u9884\u6d4b\uff08Zero-shot Prediction\uff09<\/strong><\/h5>\n\n\n\n

        \u5de5\u4f5c\u6d41\u7a0b\uff1a<\/strong><\/p>\n\n\n\n

          \n
        1. \u56fe\u50cf\u7f16\u7801\u5668\u63d0\u53d6\u5f85\u9884\u6d4b\u56fe\u50cf\u7684\u7279\u5f81\u5411\u91cf\uff08$l_i$\uff09<\/li>\n\n\n\n
        2. \u8ba1\u7b97\u8be5\u7279\u5f81\u4e0e\u6240\u6709\u6587\u672c\u539f\u578b\uff08T\u2081...$T_N$\uff09\u7684\u4f59\u5f26\u76f8\u4f3c\u5ea6<\/li>\n\n\n\n
        3. \u9009\u62e9\u76f8\u4f3c\u5ea6\u6700\u9ad8\u7684\u6587\u672c\u539f\u578b\u5bf9\u5e94\u7684\u7c7b\u522b\u4f5c\u4e3a\u9884\u6d4b\u7ed3\u679c\uff08\u5982\u56fe\u4e2d\u5339\u914d\u5230\"A photo of a dog\"\uff09<\/li>\n<\/ol>\n\n\n\n
            \n
          • \u6574\u4e2a\u8fc7\u7a0b\u65e0\u9700\u4efb\u4f55\u5fae\u8c03<\/mark><\/strong>\uff0c\u76f4\u63a5\u5229\u7528\u9884\u8bad\u7ec3\u597d\u7684\u53cc\u7f16\u7801\u5668\u5b8c\u6210\u9884\u6d4b<\/li>\n<\/ul>\n\n\n\n

            CLIP\u5960\u5b9a\u4e86\u5bf9\u6bd4\u5b66\u4e60\u5728\u591a\u6a21\u6001\u9884\u8bad\u7ec3\u4e2d\u7684\u7edf\u6cbb\u5730\u4f4d\uff0c<\/strong>\u540e\u7eed\u5de5\u4f5c\uff08\u5982CoCa\u3001BLIP\u7b49\uff09\u5747\u5ef6\u7eed\u4e86\u8fd9\u4e00\u6838\u5fc3\u8bbe\u8ba1\u601d\u60f3\u3002<\/p>\n\n\n\n

            3. \u5b9e\u9a8c<\/h2>\n\n\n\n

            3.1 Comparing CLIP to prior zero-shot transfer image classification results.<\/h4>\n\n\n\n

            CLIP\u8bba\u6587\u8fdb\u884c\u4e86\u5927\u91cf\u7684\u5b9e\u9a8c\u6765\u8bc1\u660eCLIP\u7684\u6027\u80fd\uff0c\u5c24\u5176\u662fzero-shot\u7684\u80fd\u529b\u3002\u540c\u65f6\uff0c\u4e5f\u8ba8\u8bba\u4e86CLP\u6a21\u578b\u7684\u4e00\u4e9b\u9650\u5236\uff0c\u4e0b\u9762\u4e3e\u4f8b\u5176\u4e2d\u7684\u4e00\u4e9b\u5b9e\u9a8c\u7ed3\u679c\u3002\u5176\u4ed6\u5b9e\u9a8c\u8be6\u89c1\u539f\u56fe\u3002<\/p>\n\n\n\n

            \u5982\u88681\u6240\u793a\uff1aCLIP\u96f6\u6837\u672c\u5206\u7c7b\u5168\u9762\u78be\u538bVisual N-Grams\uff082017\uff09\uff0c\u4e09\u5927\u6570\u636e\u96c6\u63d0\u534726%~65%<\/strong>\uff0c\u5c24\u5176ImageNet\u4ece11.5%\u219276.2%\u3002<\/p>\n\n\n

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

            \u5173\u952e\u539f\u56e0<\/strong>\uff1a\u5bf9\u6bd4\u5b66\u4e60+4\u4ebf\u56fe\u6587\u5bf9\u7684\u8de8\u6a21\u6001\u9884\u8bad\u7ec3 >> N-gram\u7edf\u8ba1\u65b9\u6cd5\u3002CLIP\u8bc1\u660e\u4e86\u5927\u89c4\u6a21\u9884\u8bad\u7ec3\u5bf9\u96f6\u6837\u672c\u4efb\u52a1\u7684\u9769\u547d\u6027\u6539\u8fdb\uff0c\u4f46\u9886\u57df\u9002\u914d\u548c\u8ba1\u7b97\u6548\u7387\u4ecd\u662f\u6311\u6218\u3002<\/p>\n\n\n\n

            3.2 Zero-shot CLIP is much more robust to distribution shift than standard ImageNet models. <\/h4>\n\n\n\n

            \u5982\u56fe13\u662f\u4e00\u4e2a\u5927\u89c4\u6a21\u7684\u7cfb\u7edf\u6027\u8bc4\u4f30\uff0c\u56fe\u8868\u663e\u793a\u4e86\u4eceImageNet\u7684\u6269\u5c55\u6570\u636e\u96c6\uff0c\u5b58\u5728\u591a\u4e2a\u7279\u6b8a\u7248\u672c\uff08\u5982ImageNet-R\u3001ImageNet-Sketch\u3001ImageNet-A\u7b49\uff09\uff0c\u8fd9\u4e9b\u901a\u5e38\u7528\u4e8e\u6d4b\u8bd5\u6a21\u578b\u5728\uff1a<\/p>\n\n\n\n

              \n
            • \u6e32\u67d3\u56fe\u50cf\uff08R\uff09.<\/li>\n\n\n\n
            • \u5bf9\u6297\u6837\u672c\uff08A\uff09\u7b49\u573a\u666f\u7684\u9c81\u68d2\u6027<\/li>\n\n\n\n
            • ImageNet-Sketch \u7d20\u63cf\u98ce\u683c<\/li>\n<\/ul>\n\n\n\n
              \"\"<\/figure>\n\n\n\n

              \u7ed3\u679c\u8868\u660e\uff0cCLIP\u5728\u4e0d\u540c\u6570\u636e\u5206\u5e03\u4e0a\u5177\u4f53\u5f3a\u5927\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u5176\u96f6\u6837\u672c\u9884\u6d4b\u80fd\u529b\u6bd4\u57fa\u4e8e\u6709\u76d1\u7763\u8bad\u7ec3\u51fa\u7684ResNet101\u6a21\u578b\u6027\u80fd\u6709\u5927\u5e45\u5ea6\u63d0\u5347\uff0c\u5c24\u5176\u662f\u5728\u5bf9\u6297\u6837\u672c\u65b9\u9762\u3002<\/p>\n\n\n\n

              3.3 Few statistically significant improvements in accuracy due to detected data overlap.<\/h4>\n\n\n\n

              \u4f5c\u8005\u62c5\u5fc3\u6d4b\u8bd5\u6570\u636e\u53ef\u80fd\"\u6df7\u5165\"\u4e86\u8bad\u7ec3\u6570\u636e\uff08\u5373\u6570\u636e\u91cd\u53e0 data overlap\uff09\uff0c\u5c31\u50cf\u8003\u8bd5\u524d\u5077\u5077\u770b\u5230\u4e86\u8003\u9898\uff0c\u8fd9\u4f1a\u5f71\u54cd\u6210\u7ee9\u7684\u771f\u5b9e\u6027\u3002\u4ed6\u4eec\u7528\u7279\u6b8a\u65b9\u6cd5\u68c0\u6d4b\u51fa\u4e86\u8fd9\u4e9b\"\u7591\u4f3c\u6cc4\u9898\"\u7684\u6570\u636e\uff0c\u60f3\u770b\u770bCLIP\u6a21\u578b\u662f\u4e0d\u662f\u9760\"\u4f5c\u5f0a\"\u624d\u5f97\u9ad8\u5206\u7684\u3002\u7ed3\u679c\u5982\u56fe17\uff1a<\/p>\n\n\n\n

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

              \u5982\u56fe\u6587\u63cf\u8ff0\uff0c\u572835\u4e2a\u6570\u636e\u96c6\u4e2d\uff0c\u4ec55\u4e2a\u51fa\u73b0\u7edf\u8ba1\u663e\u8457\u5dee\u5f02\uff0899.5%\u7f6e\u4fe1\u533a\u95f4\uff09\uff0c\u5176\u4e2d2\u4e2a\u6570\u636e\u96c6\u5728\u91cd\u53e0\u6570\u636e\u4e0a\u8868\u73b0\u66f4\u5dee\u3002\u91cd\u53e0\u6837\u672c\u5360\u6bd4\u901a\u5e38\u4e2a\u4f4d\u6570\uff0c\u6700\u5927\u51c6\u786e\u7387\u63d0\u5347\u4ec50.6%\uff08Birdsnap\uff09\u5355\u4fa7\u4e8c\u9879\u5f0f\u68c0\u9a8c\u663e\u793a\u4ec56\u4e2a\u6570\u636e\u96c6\u6709\u663e\u8457\u6539\u8fdb\u3002<\/p>\n\n\n\n

              \u8bf4\u5927\u767d\u8bdd\u5c31\u662f\uff0c\u572835\u573a\"\u8003\u8bd5\"\u4e2d\uff1a\u53ea\u67095\u573a\u8003\u8bd5\u7684\u6210\u7ee9\u53d8\u5316\u660e\u663e\uff08\u7edf\u8ba1\u663e\u8457\uff09\uff0c\u5176\u4e2d2\u573a\u53cd\u800c\u8003\u5f97\u66f4\u5dee\u4e86\uff08\u91cd\u53e0\u6570\u636e\u62d6\u540e\u817f\uff09\u6700\u597d\u7684\u60c5\u51b5\u4e5f\u53ea\u662f\u4ece90\u5206\u63d0\u523090.6\u5206\uff08Birdsnap\u6570\u636e\u96c6\uff09\u3002<\/p>\n\n\n\n

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