{"id":37620,"date":"2025-02-01T06:00:59","date_gmt":"2025-01-31T22:00:59","guid":{"rendered":"https:\/\/17aitech.com\/?p=37620"},"modified":"2025-02-01T06:00:59","modified_gmt":"2025-01-31T22:00:59","slug":"%e6%8f%ad%e7%a4%ba%e5%b1%80%e9%99%90%e6%80%a7%ef%bc%9aclip-%e6%a8%a1%e5%9e%8b%e5%af%b9%e7%9c%9f%e5%ae%9e%e4%b8%96%e7%95%8c%e4%b8%ad%e5%81%87%e7%9b%b8%e5%85%b3%e7%9a%84%e4%be%9d%e8%b5%96","status":"publish","type":"post","link":"https:\/\/17aitech.com\/?p=37620","title":{"rendered":"\u63ed\u793a\u5c40\u9650\u6027\uff1aCLIP \u6a21\u578b\u5bf9\u771f\u5b9e\u4e16\u754c\u4e2d\u5047\u76f8\u5173\u7684\u4f9d\u8d56"},"content":{"rendered":"

\u6587\u7ae0\u6765\u6e90\u4e8e\u4e92\u8054\u7f51:\u63ed\u793a\u5c40\u9650\u6027\uff1aCLIP \u6a21\u578b\u5bf9\u771f\u5b9e\u4e16\u754c\u4e2d\u5047\u76f8\u5173\u7684\u4f9d\u8d56<\/a><\/p>\n

<\/a><\/p>\n

\u56fe 1\uff1aCounterAnimal \u6d4b\u8bd5\u793a\u4f8b\uff0c\u8fd9\u5f20\u5317\u6781\u718a\u56fe\u50cf\u5c55\u793a\u4e86\u4ece\u5e38\u89c1\u7684\u96ea\u5730\u80cc\u666f 97.62% \u7684\u96f6\u6837\u672c\u51c6\u786e\u7387<\/mark>\u4e0b\u964d\u5230\u5c11\u89c1\u7684\u8349\u5730\u80cc\u666f 70.91%<\/em>\u3002<\/em><\/p>\n

\u5f15\u8a00<\/strong><\/p>\n

\u5927\u578b\u89c6\u89c9\u8bed\u8a00\u6a21\u578b\uff0c\u5982 CLIP\uff0c\u5728\u5e7f\u6cdb\u7684\u591a\u6a21\u6001\u4efb\u52a1\u4e2d\u5c55\u793a\u4e86\u4ee4\u4eba\u5370\u8c61\u6df1\u523b\u7684\u6027\u80fd\u4f18\u52bf\uff0c\u5728\u5206\u5e03\u5916\u6cdb\u5316\u4e0a\u8d85\u8d8a\u4e86\u5728\u5982 ImageNet \u8fd9\u7c7b\u6570\u636e\u96c6\u4e0a\u8bad\u7ec3\u7684\u5355\u6a21\u6001\u6a21\u578b[1]\u3002\u7136\u800c\uff0c\u73b0\u6709\u7684\u6d4b\u8bd5\u6570\u636e\u96c6\u4e3b\u8981\u662f\u57fa\u4e8e ImageNet \u8bad\u7ec3\u6a21\u578b\u901a\u5e38\u9047\u5230\u7684\u5047\u76f8\u5173\u6027\u6765\u6784\u5efa\u7684\u3002\u8fd9\u4e9b\u6570\u636e\u96c6\u53ef\u80fd\u65e0\u6cd5\u5145\u5206\u53cd\u6620 CLIP \u6a21\u578b\u5728\u5982 LAION \u8fd9\u6837\u7684\u6570\u636e\u96c6\u4e0a\u8bad\u7ec3\u65f6\u6240\u9762\u4e34\u7684\u72ec\u7279\u5047\u76f8\u5173\u6027\u3002\u8fd9\u79cd\u4e0d\u5339\u914d\u5f15\u53d1\u4e86\u5bf9 CLIP \u6a21\u578b\u4e0e\u4ee5 ImageNet \u4e3a\u5bfc\u5411\u7684\u5047\u76f8\u5173\u6027\u57fa\u51c6<\/mark>\u6d4b\u8bd5\u7684\u6709\u6548\u6027\u7684\u62c5\u5fe7\uff0c\u56e0\u4e3a\u5b83\u4eec\u53ef\u80fd\u672a\u80fd\u5168\u9762\u6355\u6349 CLIP \u6a21\u578b\u5728\u5904\u7406\u771f\u5b9e\u7684\u5047\u76f8\u5173\u6027\u65f6\u6240\u9762\u4e34\u7684\u6311\u6218[3,4,5,6,7,8]\u3002<\/p>\n

\u63a5\u4e0b\u6765\u6211\u4eec\u5411\u5927\u5bb6\u5206\u4eab\u5728 NeurIPS 2024 \u4e0a\u53d1\u8868\u7684\u7814\u7a76\u6210\u679c\u3002\u8fd9\u9879\u5de5\u4f5c\u63a2\u8ba8\u4e86\u5982\u4f55\u53ef\u9760\u5730\u8bc4\u4f30\u5e76\u63d0\u9ad8\u5927\u89c4\u6a21\u89c6\u89c9\u8bed\u8a00\u6a21\u578b\u5728\u9762\u5bf9\u771f\u5b9e\u5206\u5e03\u5916\u6570\u636e\u7684\u9c81\u68d2\u6027\uff0c\u7279\u522b\u662f\u901a\u8fc7\u65b0\u6784\u5efa\u7684 CounterAnimal \u6570\u636e\u96c6\u8bc4\u4f30 CLIP \u6a21\u578b\u5728\u5206\u5e03\u5916\u573a\u666f\u4e0b\u7684\u8868\u73b0\u3002\u8be5\u6570\u636e\u96c6\u7279\u522b\u8bbe\u8ba1\u7528\u6765\u6d4b\u8bd5 CLIP \u6a21\u578b\u5bf9\u4e8e\u73af\u5883\u80cc\u666f\u53d8\u5316\u7684\u9c81\u90a6\u5ea6\uff0c\u4ece\u800c\u63ed\u793a\u5728\u4e0d\u540c\u80cc\u666f\u4e0b\uff0cCLIP \u6a21\u578b\u6027\u80fd\u7684\u53d8\u5316\uff0c\u4e3a\u7406\u89e3\u548c\u6539\u8fdb\u6a21\u578b\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\u7684\u6cdb\u5316\u80fd\u529b\u63d0\u4f9b\u4e86\u53ef\u9760\u7684\u4f9d\u636e\u3002<\/p>\n

\u6211\u4eec\u63d0\u51fa\u4e86\u4e00\u4e2a\u540d\u4e3a CounterAnimal \u7684\u65b0\u6570\u636e\u96c6\u3002\u8fd9\u4e2a\u6570\u636e\u96c6\u65e8\u5728\u7279\u522b\u63ed\u793a CLIP \u6a21\u578b\u5bf9\u5176\u8bad\u7ec3\u73af\u5883\u4e2d\u771f\u5b9e\u5b58\u5728\u7684\u5047\u76f8\u5173\u6027\u7684\u4f9d\u8d56\u6027\u3002CounterAnimal \u5c06\u52a8\u7269\u56fe\u50cf\u6839\u636e\u80cc\u666f\u5206\u7ec4\uff0c\u4e3a\u6bcf\u4e2a\u7c7b\u522b\u8bbe\u7acb\u5bf9\u4e8e CLIP \u6765\u8bb2\u7684\u201c\u7b80\u5355\u201d\u548c\u201c\u56f0\u96be\u201d\u4e24\u4e2a\u5b50\u7c7b\u522b\u3002\u8fd9\u79cd\u8bbe\u7f6e\u4f7f\u6211\u4eec\u80fd\u591f\u76f4\u63a5\u8bc4\u4f30 CLIP \u6a21\u578b\u5bf9\u6bd4\u901a\u5e38\u5728 ImageNet \u4e2d\u53d1\u73b0\u7684\u5047\u76f8\u5173\u6027\u96c6\u5408\u66f4\u591a\u6837\u5316\u548c\u771f\u5b9e\u7684\u5047\u76f8\u5173\u6027\u3002<\/p>\n

<\/a><\/p>\n

\u6211\u4eec\u7684\u8bc4\u4f30\u8868\u660e\uff0cCounterAnimal \u6355\u83b7\u7684\u5047\u76f8\u5173\u6027\u901a\u5e38\u4f1a\u88ab\u4f7f\u7528\u4e0d\u540c\u6a21\u578b\u548c\u8bad\u7ec3\u6570\u636e\u96c6\u7684 CLIP \u6a21\u578b\u5b66\u4e60\u3002\u6211\u4eec\u4e0d\u4ec5\u4ece\u5b9e\u9a8c\u5c42\u9762\u6311\u6218\u4e86\u5148\u524d\u5bf9 CLIP \u9c81\u68d2\u6027\u7684\u8ba4\u77e5\u5c40\u9650\uff0c\u8fd8\u63d0\u4f9b\u4e86\u5bf9\u5e94\u7684\u7406\u8bba\u5206\u6790\uff0c\u4ee5\u89e3\u91ca\u4e3a\u4f55 CLIP \u7684\u8bad\u7ec3\u76ee\u6807\u65e0\u6cd5\u63d0\u4f9b\u989d\u5916\u7684\u9c81\u68d2\u6027\u3002\u6b64\u5916\uff0c\u6211\u4eec\u8fd8\u63a2\u7d22\u4e86\u5982\u589e\u52a0\u6a21\u578b\u53c2\u6570<\/mark>\uff0c\u6570\u636e\u89c4\u6a21\uff0c\u4ee5\u53ca\u4f7f\u7528\u9ad8\u8d28\u91cf\u6570\u636e\u7b49\u7b56\u7565\u5bf9 CLIP \u6a21\u578b\u9c81\u68d2\u6027\u7684\u5f71\u54cd\u3002\u5c3d\u7ba1\u6211\u4eec\u53d1\u73b0\u4e86\u4e00\u4e9b\u6709\u76ca\u7684\u7b56\u7565\uff0c\u4f46\u5b83\u4eec\u5e76\u4e0d\u8db3\u4ee5\u5b8c\u5168\u6d88\u89e3\u5047\u76f8\u5173\u6027\u5e26\u6765\u7684\u5f71\u54cd\u3002\u8fd9\u8868\u660e\u6211\u4eec\u5728\u672a\u6765\u8fd8\u9700\u8981\u8fdb\u4e00\u6b65\u6539\u8fdb\u8bad\u7ec3\u7b56\u7565\uff0c\u4ee5\u66f4\u597d\u5730\u5e94\u5bf9 CLIP \u6a21\u578b\u5728\u771f\u5b9e\u4e16\u754c\u7684\u5e7f\u6cdb\u5e94\u7528\u3002<\/p>\n

CounterAnimal \u6570\u636e\u96c6\uff1aCLIP \u9c81\u68d2\u6027\u7684\u65b0\u57fa\u51c6<\/mark><\/strong><\/p>\n

\u4eba\u5de5\u667a\u80fd<\/mark>\u5e94\u7528\u9700\u8981\u9891\u7e41\u9762\u4e34\u5f00\u653e\u4e16\u754c\u4e2d\u5e7f\u6cdb\u4e14\u4e0d\u53ef\u9884\u6d4b\u7684\u73af\u5883\uff0c\u56e0\u6b64 CLIP \u6a21\u578b\u4e0d\u4ec5\u9700\u8981\u5c55\u73b0\u603b\u4f53\u8f83\u9ad8\u7684\u51c6\u786e\u7387<\/mark>\uff0c\u8fd8\u5fc5\u987b\u4fdd\u6301\u5bf9\u5e38\u88ab\u4f20\u7edf\u57fa\u51c6<\/mark>\u5ffd\u89c6\u7684\u5047\u76f8\u5173\u6027\u7684\u9c81\u68d2\u6027\u3002\u4e3a\u6b64\uff0c\u6211\u4eec\u5f15\u5165\u4e86\u4e13\u95e8\u6784\u5efa\u7684 CounterAnimal \u6570\u636e\u96c6\uff0c\u7528\u4ee5\u8bc4\u4f30 CLIP \u6a21\u578b\u5bf9\u53ef\u80fd\u5728\u4f20\u7edf\u6570\u636e\u96c6\u5982 ImageNet \u4e2d\u672a\u80fd\u5145\u5206\u4ee3\u8868\u7684\u5047\u76f8\u5173\u6027\u7684\u9c81\u68d2\u6027\u3002CounterAnimal \u6570\u636e\u96c6\u5305\u542b 45 \u4e2a\u52a8\u7269\u7c7b\u522b\uff0c\u5171\u8ba1 7,174 \u5f20\u201c\u7b80\u5355\u201d\u7167\u7247\u548c 5,926 \u5f20\u201c\u56f0\u96be\u201d\u7167\u7247\u3002<\/p>\n

\u6570\u636e\u96c6\u521b\u5efa\u8fc7\u7a0b<\/strong><\/p>\n

\u6211\u4eec\u4f7f\u7528\u4e86\u6765\u81ea iNaturalist \u7684\u56fe\u50cf\uff0c\u5e76\u6839\u636e\u80cc\u666f\u5bf9\u5176\u8fdb\u884c\u5206\u7c7b\u3002\u4f8b\u5982\uff0c\u5317\u6781\u718a\u7684\u56fe\u7247\u901a\u5e38\u5448\u73b0\u96ea\u5730\u80cc\u666f\u3002\u7136\u800c\uff0c\u6211\u4eec\u7684\u6570\u636e\u96c6\u989d\u5916\u5305\u542b\u4e00\u7ec4\u53cd\u4f8b\uff0c\u4f8b\u5982\u5317\u6781\u718a\u51fa\u73b0\u5728\u8349\u5730\u73af\u5883\u7684\u56fe\u7247\uff0c\u8fd9\u5bf9 CLIP \u6a21\u578b\u5b66\u4e60\u5230\u7684\u5173\u8054\u6027\u6784\u6210\u4e86\u4e00\u5b9a\u7684\u6311\u6218\u3002<\/p>\n

1.\u6570\u636e\u6536\u96c6<\/strong>\uff1a\u6211\u4eec\u4ece ImageNet-1K \u6570\u636e\u96c6\u4e2d\u9009\u62e9\u4e00\u6279\u5177\u6709\u4ee3\u8868\u6027\u7684\u52a8\u7269\u79cd\u7c7b\uff0c\u7136\u540e\u4ece iNaturalist \u68c0\u7d22\u76f8\u5e94\u7684\u56fe\u50cf\u3002<\/p>\n

2.\u6570\u636e\u7b56\u5212<\/strong>\uff1a\u6bcf\u5f20\u56fe\u50cf\u90fd\u7ecf\u8fc7\u4e25\u683c\u7684\u7b5b\u9009\uff0c\u6211\u4eec\u624b\u52a8\u79fb\u9664\u4e86\u5305\u542b\u6b67\u4e49\u6216\u8d28\u91cf\u4f4e\u4e0b\u7684\u56fe\u50cf\uff0c\u786e\u4fdd\u6570\u636e\u96c6\u53ea\u5305\u542b\u6e05\u6670\u3001\u660e\u786e\u7684\u56fe\u50cf\u3002<\/p>\n

3.\u80cc\u666f\u6807\u8bb0<\/strong>\uff1a\u6211\u4eec\u5bf9\u6bcf\u5f20\u56fe\u50cf\u8fdb\u884c\u4e86\u7684\u80cc\u666f\u6807\u6ce8\uff0c\u9009\u62e9\u53cd\u6620\u6bcf\u4e2a\u7269\u79cd\u7684\u73af\u5883\u6807\u6ce8\u3002\u8fd9\u4e00\u7cbe\u786e\u7684\u6807\u8bb0\u5bf9\u4e8e\u8bbe\u7f6e\u6211\u4eec\u7684\u5b9e\u9a8c\u81f3\u5173\u91cd\u8981\u3002<\/p>\n

4.\u53d1\u73b0\u5047\u76f8\u5173\u6027<\/strong>\uff1a\u6700\u540e\u4e00\u6b65\u6d89\u53ca\u6839\u636e\u80cc\u666f\u5c06\u56fe\u50cf\u5206\u7c7b<\/mark>\u4e3a\u201c\u7b80\u5355\u201d\u548c\u201c\u56f0\u96be\u201d\u4e24\u7ec4\u3002\u8fd9\u79cd\u5206\u7c7b\u57fa\u4e8e\u9884\u8bad\u7ec3\u7684 CLIP \u6a21\u578b\u5728\u4e0d\u540c\u80cc\u666f\u4e0b\u7684\u8868\u73b0\u3002<\/p>\n

\u8be5\u6570\u636e\u96c6\u4e3b\u8981\u4f7f\u7528 CLIP-LAION400M-ViT-B\/32 \u6a21\u578b\u6765\u53d1\u73b0\u5047\u76f8\u5173\u6027\u3002<\/p>\n

\u53ef\u89c6\u5316 CounterAnimal \u6570\u636e\u96c6\u7684\u7ed3\u6784\u548c\u5e03\u5c40<\/strong><\/p>\n

\u6211\u4eec\u5728\u56fe 3 \u4e2d\u5c55\u793a\u4e86 CounterAnimal \u6570\u636e\u96c6\u7684\u5206\u5e03\uff0c\u5e76\u5728\u9644\u56fe\u4e2d\u5c55\u793a\u4e86\u5176\u76ee\u5f55\u7ed3\u6784\u3002\u56fe 3 \u663e\u793a\u4e86\u6bcf\u4e2a\u52a8\u7269\u7c7b\u522b\u7684\u56fe\u50cf\u6570\u91cf\uff0c\u5206\u4e3a\u201c\u7b80\u5355\u201d\u548c\u201c\u56f0\u96be\u201d\u7684\u80cc\u666f\u7ec4\u3002\u76ee\u5f55\u7ed3\u6784\u56fe\u8bf4\u660e\u4e86\u6570\u636e\u662f\u5982\u4f55\u88ab\u7ec4\u7ec7\u7684\uff0c\u80cc\u666f\u5219\u88ab\u5206\u7c7b\u4e3a\u201c\u7b80\u5355\u201d\u6216\u201c\u56f0\u96be\u201d\u7ec4\u3002<\/p>\n

<\/a><\/p>\n

\u56fe 3\uff1a\u5404\u79cd\u52a8\u7269\u7c7b\u522b\u7684\u6570\u636e\u5e03\u5c40\u3002\u6c34\u5e73\u8f74\u8868\u793a\u7c7b\u522b ID\uff0c\u5782\u76f4\u8f74\u8868\u793a\u7b80\u5355\u548c\u56f0\u96be\u7ec4\u7684\u7167\u7247\u6570\u91cf\u3002<\/em><\/p>\n

<\/a><\/p>\n

\u56fe<\/em>4<\/em>\uff1aCounterAnimal \u6570\u636e\u96c6\u7684\u76ee\u5f55\u7ed3\u6784\u3002<\/em><\/p>\n

CounterAnimal \u7684\u57fa\u672c\u6027\u8d28<\/strong><\/p>\n

\u56fe 5\u5c55\u793a\u4e86\u5728\u6bd4\u8f83\u201c\u7b80\u5355\u201d\u548c\u201c\u56f0\u96be\u201d\u80cc\u666f\u8bbe\u7f6e\u4e0b CLIP \u6a21\u578b\u7684\u96f6\u6837\u672c\u5206\u7c7b\u51c6\u786e\u7387<\/mark>\u7684\u53d8\u5316\u3002\u8be5\u56fe\u8868\u660e CLIP \u6a21\u578b\u5728\u5e38\u89c1\u73af\u5883\u4e2d\u867d\u7136\u8868\u73b0\u51fa\u8f83\u5f3a\u7684\u96f6\u6837\u672c\u6cdb\u5316\u6027\u80fd\uff0c\u4f46\u9762\u5bf9\u5206\u5e03\u5916\u6216\u201c\u56f0\u96be\u201d\u80cc\u666f\u65f6\u6027\u80fd\u4f1a\u663e\u8457\u4e0b\u964d\u3002<\/p>\n

\u6b64\u53ef\u89c6\u5316\u5c55\u793a\u4e86\u6a21\u578b\u5bf9\u80cc\u666f\u4fe1\u606f\u7684\u4f9d\u8d56\u3002\u5728\u80cc\u666f\u4e0e\u8bad\u7ec3\u6570\u636e\u5339\u914d\u7684\u573a\u666f\u4e2d\uff08\u201c\u7b80\u5355\u201d\uff09\uff0c\u6a21\u578b\u8868\u73b0\u826f\u597d\u3002\u7136\u800c\uff0c\u5f53\u5f15\u5165\u975e\u5178\u578b\u80cc\u666f\u65f6\uff08\u201c\u56f0\u96be\u201d\uff09\uff0c\u51c6\u786e\u7387<\/mark>\u4f1a\u663e\u8457\u53d7\u5230\u5f71\u54cd\uff0c\u5c55\u793a\u4e86 CLIP \u6a21\u578b\u5206\u5e03\u5916\u6cdb\u5316\u80fd\u529b\u7684\u5dee\u8ddd\u3002\u8fd9\u4e00\u73b0\u8c61\u8868\u660e\u4e86 CLIP \u6a21\u578b\u5bf9\u73af\u5883\u4fe1\u606f\u7684\u4f9d\u8d56\uff0c\u5f88\u5927\u7a0b\u5ea6\u4e0a\u9650\u5236\u4e86\u8fd9\u4e9b\u6a21\u578b\u5728\u771f\u5b9e\u4e16\u754c\u5e94\u7528\u4e2d\u7684\u6709\u6548\u6027\u3002<\/p>\n

<\/a><\/p>\n

\u56fe<\/em>5<\/em>\uff1a\u4f7f\u7528 CLIP-LAION400M-ViT-B\/32 \u7684\u6027\u80fd\u4e0b\u964d\uff08%\uff09\u3002\u6c34\u5e73\u8f74\u8868\u793a\u7c7b\u522b ID\uff0c\u5782\u76f4\u8f74\u8868\u793a\u4e0b\u964d\u7684\u767e\u5206\u70b9\u3002<\/em><\/p>\n

## \u7814\u7a76\u53d1\u73b0\u548c\u8be6\u7ec6\u89c2\u5bdf<\/p>\n

### \u8be6\u7ec6\u7684\u6027\u80fd\u5206\u6790<\/p>\n

\u8868 1 \u5c55\u793a\u4e86\u4f7f\u7528 CLIP-LAION400M-ViT-B\/32 \u6a21\u578b\u5728 CounterAnimal \u6570\u636e\u96c6\u4e0a\u7684\u96f6\u6837\u672c\u6027\u80fd\u7ed3\u679c\u3002\u8fd9\u79cd\u8bbe\u7f6e\u6d4b\u8bd5\u4e86\u6a21\u578b\u8bc6\u522b\u52a8\u7269\u7c7b\u522b\u7684\u80fd\u529b\uff0c\u7a81\u51fa\u663e\u793a\u4e86\u5b83\u5728\u5e38\u89c1\uff08\u201c\u7b80\u5355\u201d\uff09\u5206\u5e03\u5916\uff08\u201c\u56f0\u96be\u201d\uff09\u80cc\u666f\u4e0b\u7684\u6cdb\u5316\u80fd\u529b\u3002\u8fd9\u4e2a\u8868\u683c\u663e\u793a\u4e86\u6bcf\u4e2a\u52a8\u7269\u7c7b\u522b\u5728\u4e24\u79cd\u80cc\u666f\u7c7b\u578b\u4e0b\u7684\u51c6\u786e\u7387<\/mark>\uff0c\u5e76\u8ba1\u7b97\u4e86\u6a21\u578b\u5728\u9047\u5230\u5206\u5e03\u5916\u80cc\u666f\u65f6\u6027\u80fd\u4e0b\u964d\u7684\u767e\u5206\u6bd4\u3002\u8fd9\u91cf\u5316<\/mark>\u4e86 CLIP \u6a21\u578b\u5bf9\u80cc\u666f\u73af\u5883\u7684\u4f9d\u8d56\u4ee5\u53ca\u6027\u80fd\u53d8\u5316\u3002<\/p>\n\n\n\n\n\n\n\n\n
\n

\u52a8\u7269\u6807\u7b7e\u00a0<\/p>\n<\/td>\n

\n

\u7b80\u5355\u80cc\u666f<\/p>\n<\/td>\n

\n

\u51c6\u786e\u7387<\/mark>\uff08\u7b80\u5355\uff09<\/p>\n<\/td>\n

\n

\u56f0\u96be\u80cc\u666f<\/p>\n<\/td>\n

\n

\u51c6\u786e\u7387<\/mark>\uff08\u56f0\u96be\uff09<\/p>\n<\/td>\n

\n

\u4e0b\u964d\uff08%\uff09<\/p>\n<\/td>\n<\/tr>\n

\n

\u5317\u6781\u718a<\/p>\n<\/td>\n

\n

\u96ea\u00a0<\/p>\n<\/td>\n

\n

97.62%\u00a0<\/p>\n<\/td>\n

\n

\u8349\u5730\u00a0<\/p>\n<\/td>\n

\n

70.91%\u00a0<\/p>\n<\/td>\n

\n

26.71%\u00a0<\/p>\n<\/td>\n<\/tr>\n

\n

\u9ed1\u5929\u9e45<\/p>\n<\/td>\n

\n

\u6c34\u00a0<\/p>\n<\/td>\n

\n

93.63%\u00a0<\/p>\n<\/td>\n

\n

\u571f\u5730\u00a0<\/p>\n<\/td>\n

\n

68.87%\u00a0<\/p>\n<\/td>\n

\n

24.76%\u00a0<\/p>\n<\/td>\n<\/tr>\n

\n

\u706b\u70c8\u9e1f<\/p>\n<\/td>\n

\n

\u6c34\u00a0<\/p>\n<\/td>\n

\n

79.70%\u00a0<\/p>\n<\/td>\n

\n

\u5929\u7a7a\u00a0<\/p>\n<\/td>\n

\n

55.45%\u00a0<\/p>\n<\/td>\n

\n

24.25%\u00a0<\/p>\n<\/td>\n<\/tr>\n

\n

\u79c3\u9e6b\u00a0<\/p>\n<\/td>\n

\n

\u5929\u7a7a\u00a0<\/p>\n<\/td>\n

\n

87.76%\u00a0<\/p>\n<\/td>\n

\n

\u6811\u00a0<\/p>\n<\/td>\n

\n

41.84%\u00a0<\/p>\n<\/td>\n

\n

45.92%\u00a0<\/p>\n<\/td>\n<\/tr>\n

\n

\u5c4e\u58f3\u90ce\u00a0<\/p>\n<\/td>\n

\n

\u5730\u9762\u00a0<\/p>\n<\/td>\n

\n

56.92%\u00a0<\/p>\n<\/td>\n

\n

\u624b\u00a0<\/p>\n<\/td>\n

\n

17.02%\u00a0<\/p>\n<\/td>\n

\n

39.90%\u00a0<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u5b9e\u9a8c\u7ed3\u679c<\/strong><\/p>\n

\u56fe\u00a06\u63d0\u4f9b\u4e86\u5728\u4e0d\u540c\u8bc4\u4f30\u8bbe\u7f6e\u4e0b\uff0c\u4e0d\u540c\u6a21\u578b\u5728 CounterAnimal \u6570\u636e\u96c6\u4e0a\u7684\u8868\u73b0\u6bd4\u8f83\u3002\u8fd9\u4e2a\u56fe\u7a81\u51fa\u4e86\u6a21\u578b\u9762\u5bf9\u7b80\u5355\u548c\u56f0\u96be\u5206\u7c7b\u573a\u666f\u65f6\u7684\u6027\u80fd\u53d8\u5316\u3002<\/p>\n

<\/a><\/p>\n

\u56fe\u00a0<\/em>6<\/em>\uff1a\u5728\u4e24\u79cd\u8bc4\u4f30\u8bbe\u7f6e\u4e0b\u7684\u6027\u80fd\u6bd4\u8f83\uff0c\u5c55\u793a\u4e86 CLIP \u6a21\u578b\u5bf9 CounterAnimal \u6570\u636e\u96c6\u4e2d\u5047\u76f8\u5173\u6027\u7684\u9c81\u68d2\u6027\u3002\u8f83\u5927\u7684\u6570\u636e\u96c6\u548c\u6a21\u578b\u5927\u5c0f\u7531\u6807\u8bb0\u7684\u5927\u5c0f\u548c\u989c\u8272\u6df1\u6d45\u8868\u793a\u3002<\/em><\/p>\n

\u56fe\u00a06\u4e2d\u7684\u5206\u6790\u53ef\u4ee5\u5f15\u7533\u5230\u5982\u4e0b\u7ed3\u8bba\uff1a<\/p>\n

–\u666e\u904d\u7684\u5047\u76f8\u5173\u6027<\/strong>\uff1aCLIP \u6a21\u578b\u5728\u4ece\u201c\u7b80\u5355\u201d\u7ec4\u8fc7\u6e21\u5230\u201c\u56f0\u96be\u201d\u7ec4\u65f6\u8868\u73b0\u51fa\u663e\u8457\u7684\u6027\u80fd\u4e0b\u964d\uff0c\u8868\u660e CounterAnimal \u6570\u636e\u96c6\u786e\u5b9e\u5305\u542b\u4e86\u5404\u79cd CLIP \u6a21\u578b\u914d\u7f6e\u4e2d\u666e\u904d\u5b58\u5728\u7684\u5047\u76f8\u5173\u6027\u3002<\/p>\n

– ImageNet \u6a21\u578b\u7684\u9c81\u68d2\u6027<\/strong>\uff1aImageNet \u6a21\u578b\uff08\u7ea2\u8272\u6807\u8bb0\uff09\u663e\u793a\u51fa\u6bd4 CLIP \u53d8\u4f53\u66f4\u5f3a\u7684\u9c81\u68d2\u6027\uff0c\u6311\u6218\u4e86\u5148\u524d\u5173\u4e8e CLIP \u5f3a\u9c81\u68d2\u6027\u548c\u5f3a\u6cdb\u5316\u80fd\u529b\u7684\u7247\u9762\u8ba4\u8bc6\u3002<\/p>\n

–\u6a21\u578b\u548c\u6570\u636e\u89c4\u6a21\u7684\u5f71\u54cd<\/strong>\uff1a\u8f83\u5927\u7684\u6a21\u578b\u663e\u793a\u51fa\u66f4\u5f3a\u7684\u6027\u80fd\uff0c\u8868\u660e\u589e\u52a0\u6a21\u578b\u5bb9\u91cf\u53ef\u4ee5\u63d0\u9ad8\u9c81\u68d2\u6027\u3002\u76f8\u53cd\uff0c\u4ec5\u6269\u5927\u6570\u636e\u96c6\u5927\u5c0f\uff08\u989c\u8272\u8f83\u6df1\u7684\u6807\u8bb0\uff09\u5e76\u672a\u5e26\u6765\u7c7b\u4f3c\u7684\u6539\u5584\uff0c\u8868\u660e\u6269\u5927\u6570\u636e\u89c4\u6a21\u5bf9\u6027\u80fd\u7684\u63d0\u5347\u662f\u6709\u9650\u7684\u3002<\/p>\n

– \u9ad8\u8d28\u91cf\u6570\u636e\u548c\u6a21\u578b\u9c81\u68d2\u6027<\/strong>\uff1a\u5728\u9ad8\u8d28\u91cf\u6570\u636e\u4e0a\u9884\u8bad\u7ec3\u7684 CLIP \u6a21\u578b\uff08CLIP-DC \u548c CLIP-DFN\uff09\u663e\u793a\u51fa\u66f4\u5f3a\u7684\u9c81\u68d2\u6027\uff0c\u5f3a\u8c03\u4e86\u6570\u636e\u8d28\u91cf\u5bf9\u4e8e\u6a21\u578b\u6027\u80fd\u7684\u91cd\u8981\u6027\u3002<\/p>\n

\u8fd9\u4e9b\u89c2\u5bdf\u5bf9\u4e8e\u7406\u89e3 CLIP \u53ca\u5176\u4ed6\u5927\u578b\u89c6\u89c9\u8bed\u8a00\u6a21\u578b\u5728\u73b0\u5b9e\u4e16\u754c\u591a\u6837\u6027\u6761\u4ef6\u4e0b\u7684\u884c\u4e3a\u81f3\u5173\u91cd\u8981\uff0c\u5e76\u5f3a\u8c03\u4e86\u5b9e\u73b0\u771f\u6b63\u9c81\u68d2\u7684\u591a\u6a21\u6001\u4eba\u5de5\u667a\u80fd<\/mark>\u7cfb\u7edf\u9762\u4e34\u7684\u6301\u7eed\u6311\u6218\u3002<\/p>\n

\u5b9e\u9a8c\u5206\u6790<\/strong><\/p>\n

\u6211\u4eec\u7684\u7814\u7a76\u901a\u8fc7\u4f7f\u7528\u7cbe\u5fc3\u6784\u5efa\u7684 CounterAnimal \u6570\u636e\u96c6\uff0c\u4e13\u6ce8\u4e8e\u8bc4\u4f30\u5728\u4e0d\u540c\u6570\u636e\u4ee5\u53ca\u6a21\u578b\u6761\u4ef6\u4e0b CLIP \u6a21\u578b\u5bf9\u5047\u76f8\u5173\u7684\u9c81\u68d2\u6027\u3002<\/p>\n

\u5047\u76f8\u5173\u7684\u666e\u904d\u6027<\/strong><\/p>\n

\u5728\u8fd9\u4e00\u90e8\u5206\uff0c\u6211\u4eec\u8bc4\u4f30 CounterAnimal \u6570\u636e\u96c6\u6355\u83b7\u7684\u5047\u76f8\u5173\u6027\u5728\u4e0d\u540c CLIP \u6a21\u578b\u8bbe\u5b9a\u4e2d\u7684\u666e\u904d\u6027\u3002\u6211\u4eec\u5206\u6790\u4e0d\u540c\u9884\u8bad\u7ec3\u6570\u636e\u96c6\u548c\u6a21\u578b\u5982\u4f55\u5f71\u54cd CLIP \u7684\u9c81\u68d2\u6027\u3002\u7ed3\u679c\u663e\u793a\uff0c\u5728\u6d4b\u8bd5\u201c\u7b80\u5355\u201d\u548c\u201c\u56f0\u96be\u201d\u7ec4\u65f6\uff0c\u4e0d\u540c\u6a21\u578b\u548c\u9884\u8bad\u7ec3\u6570\u636e\u96c6\u7684\u6027\u80fd\u90fd\u51fa\u73b0\u4e86\u663e\u8457\u4e0b\u964d\uff0c\u8868\u660e\u7531 CounterAnimal \u6807\u8bc6\u7684\u5047\u76f8\u5173\u6027\u786e\u5b9e\u5728\u4e0d\u540c\u7684 CLIP \u8bad\u7ec3\u8303\u5f0f\u4e2d\u666e\u904d\u5b58\u5728\u3002<\/p>\n

<\/a><\/p>\n

<\/p>\n

\u56fe\u00a0<\/em>7<\/em>\uff1a\u4e0d\u540c CLIP \u8bbe\u5b9a\u7684\u7ed3\u679c\u3002<\/em><\/p>\n

\u89c4\u6a21\u6269\u5927\u53ef\u4ee5\u63d0\u5347\u9c81\u68d2\u6027<\/strong><\/p>\n

\u6211\u4eec\u7684\u5206\u6790\u8fd8\u6269\u5c55\u5230\u6269\u5927\u89c4\u6a21\u548c\u8d28\u91cf\u5bf9\u4e8e CLIP \u9c81\u68d2\u6027\u7684\u5f71\u54cd\u3002\u6211\u4eec\u53d1\u73b0\uff0c\u8f83\u5927\u7684\u6a21\u578b\u548c\u9ad8\u8d28\u91cf\u7684\u6570\u636e\u901a\u5e38\u663e\u793a\u51fa\u66f4\u5f3a\u7684\u9c81\u68d2\u6027\u3002\u7136\u800c\uff0c\u5355\u7eaf\u589e\u52a0\u6570\u636e\u89c4\u6a21\u5e76\u4e0d\u8db3\u4ee5\u7f13\u89e3\u6a21\u578b\u53d7\u5230\u5047\u76f8\u5173\u7684\u5f71\u54cd\u3002<\/p>\n

<\/a><\/p>\n

\u8868 2\uff1a\u63d0\u9ad8\u9884\u8bad\u7ec3\u6570\u636e\u548c\u6a21\u578b\u89c4\u6a21\u7684\u7ed3\u679c\u3002<\/em><\/p>\n

\u8bc4\u4f30\u5176\u4ed6\u5b66\u4e60\u8303\u5f0f<\/strong><\/p>\n

\u6700\u540e\uff0c\u6211\u4eec\u6269\u5c55\u4e86\u5bf9\u5176\u4ed6\u5b66\u4e60\u8303\u5f0f\u7684\u8bc4\u4f30\uff0c\u4ee5\u4e86\u89e3\u5b83\u4eec\u5982\u4f55\u4e0e CLIP \u5728\u5904\u7406\u5047\u76f8\u5173\u6027\u65b9\u9762\u7684\u6bd4\u8f83\u3002\u8fd9\u90e8\u5206\u5206\u6790\u6d89\u53ca\u8bc4\u4f30\u5728\u4e0d\u540c\u6761\u4ef6\u4e0b\u8bad\u7ec3\u7684\u6a21\u578b\uff0c\u5305\u62ec\u90a3\u4e9b\u5728 ImageNet \u4e0a\u6709\u76d1\u7763\u8bad\u7ec3\u7684\u548c\u66f4\u5148\u8fdb\u7684\u8bed\u8a00\u89c6\u89c9\u6a21\u578b\u3002\u7ed3\u679c\u8868\u660e\uff0c\u5404\u7c7b\u89c6\u89c9\u8bed\u8a00\u6a21\u578b\u4ecd\u7136\u6613\u53d7\u5047\u7279\u5f81\u7684\u5f71\u54cd\uff0c\u51f8\u663e\u4e86\u89c6\u89c9\u8bed\u8a00\u9884\u8bad\u7ec3\u6240\u9762\u4e34\u7684\u6301\u7eed\u6311\u6218\u3002<\/p>\n

<\/a><\/p>\n

\u8868 3\uff1aImageNet \u6a21\u578b\u548c CLIP \u6a21\u578b\u7684\u6027\u80fd\u6bd4\u8f83\u3002<\/em><\/p>\n

\u7406\u89e3 CLIP \u4f9d\u8d56\u5047\u7279\u5f81\u7684\u539f\u56e0<\/strong><\/p>\n

\u8fdb\u4e00\u6b65\u800c\u8a00\uff0c\u6211\u4eec\u4ecb\u7ecd\u4e86\u4e00\u4e2a\u7406\u8bba\u6846\u67b6\u4ee5\u7cfb\u7edf\u6027\u5730\u5206\u6790 CLIP \u5bf9\u5047\u7279\u5f81\u7684\u4f9d\u8d56\u6027\u3002\u6211\u4eec\u5c06\u5b9a\u4e49\u4e24\u79cd\u7c7b\u578b\u7684\u7279\u5f81\uff1a\u4e0d\u53d8\u7279\u5f81\uff0c\u5bf9\u4efb\u52a1\u6709\u7528\uff0c\u4ee5\u53ca\u5047\u7279\u5f81\uff0c\u5bf9\u4efb\u52a1\u6ca1\u6709\u5e2e\u52a9\u3002\u6211\u4eec\u53d1\u73b0\uff0c\u8fd9\u4e9b\u5047\u7279\u5f81\u53ef\u80fd\u4e3b\u5bfc CLIP \u6a21\u578b\u7684\u5b66\u4e60\u8fc7\u7a0b\uff0c\u5bfc\u81f4\u6a21\u578b\u5728\u8bad\u7ec3\u4e2d\u8868\u73b0\u826f\u597d\u4f46\u5728\u5206\u5e03\u5916\u7684\u73af\u5883\u4e2d\u8868\u73b0\u4e0d\u4f73\u3002<\/p>\n

\u5047\u76f8\u5173\u6027\u7684\u5173\u952e\u5b9a\u7406<\/strong><\/p>\n

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<\/a><\/p>\n

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\u5b9e\u9645\u5f71\u54cd\u548c\u672a\u6765\u65b9\u5411<\/strong><\/p>\n

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\u6709\u5173\u6211\u4eec\u5b9e\u9a8c\u4ee5\u53ca\u7406\u8bba\u7684\u8be6\u7ec6\u8ba8\u8bba\uff0c\u8bf7\u53c2\u8003\u6211\u4eec\u5728https:\/\/counteranimal.github.io\/<\/u>\u4e0a\u7684\u5b8c\u6574\u7814\u7a76\u8bba\u6587\u7684\u8865\u5145\u6750\u6599\u3002<\/p>\n

\u53c2\u8003\u6587\u732e<\/strong><\/p>\n

1. Jingyi Zhang, Jiaxing Huang, Sheng Jin, and Shijian Lu. Vision-language models for vision tasks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.<\/p>\n

2. Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, et al. Laion5b: An open large-scale dataset for training next generation image-text models. In NeurIPS, 2022.<\/p>\n

3. Martin Arjovsky<\/mark>, L\u00e9on Bottou, Ishaan Gulrajani, and David Lopez-Paz. Invariant risk minimization. arXiv preprint arXiv:1907.02893, 2019.<\/p>\n

4. Xiao Zhou, Yong Lin, Weizhong Zhang, and Tong Zhang. Sparse invariant risk minimization. In ICML, 2022.<\/p>\n

5. Yong Lin, Hanze Dong, Hao Wang, and Tong Zhang. Bayesian invariant risk minimization. In CVPR, 2022.<\/p>\n

6. Xiao Zhou, Yong Lin, Renjie Pi, Weizhong Zhang, Renzhe Xu, Peng Cui, and Tong Zhang. Model agnostic sample reweighting for out-of-distribution learning. In ICML, 2022.<\/p>\n

7. Yong Lin, Fan Zhou, Lu Tan, Lintao Ma, Jiameng Liu, Yansu He, Yuan Yuan, Yu Liu, James Zhang, and Yujiu Yang. Continuous invariance learning. In ICLR, 2024.<\/p>\n

8. Yong Lin, Lu Tan, Yifan Hao, Honam Wong, Hanze Dong, Weizhong Zhang, Yujiu Yang, and Tong Zhang. Spurious feature diversification improves out-of-distribution generalization. In ICLR, 2024.<\/p>\n

9. Xiaoyu Tan, Lin Yong, Shengyu Zhu, Chao Qu, Xihe Qiu, Xu Yinghui, Peng Cui, and Yuan Qi. Provably invariant learning without domain information. In ICML, 2023.<\/p>\n

10. Yongqiang Chen, Kaiwen Zhou, Yatao Bian, Binghui Xie, Bingzhe Wu, Yonggang Zhang, MA KAILI, Han Yang, Peilin Zhao, Bo Han, and James Cheng. Pareto invariant risk minimization: Towards mitigating the optimization dilemma in out-of-distribution generalization. In ICLR, 2023.<\/p>\n

11. Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, and James Cheng. Understanding and improving feature learning for out-of-distribution generalization. In NeurIPS, 2023.<\/p>\n

12. Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song. Natural adversarial examples. In CVPR, 2021.<\/p>\n

13. Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. Do imagenet classifiers generalize to imagenet? In ICML, 2019.<\/p>\n

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