{"id":41048,"date":"2026-06-17T18:06:00","date_gmt":"2026-06-17T10:06:00","guid":{"rendered":"https:\/\/17aitech.com\/?p=41048"},"modified":"2026-06-17T18:06:00","modified_gmt":"2026-06-17T10:06:00","slug":"cvpr-2026-%e6%a8%a1%e5%9e%8b%e9%80%82%e5%ba%94%e6%80%a7%e7%a0%94%e7%a9%b6%e7%9b%98%e7%82%b9%ef%bc%9a%e4%bb%8e%e4%bf%9d%e7%95%99%e6%97%a7%e7%9f%a5%e8%af%86%ef%bc%8c%e5%88%b0%e9%80%82%e5%ba%94%e7%9c%9f","status":"publish","type":"post","link":"https:\/\/17aitech.com\/?p=41048","title":{"rendered":"CVPR 2026 \u6a21\u578b\u9002\u5e94\u6027\u7814\u7a76\u76d8\u70b9\uff1a\u4ece\u4fdd\u7559\u65e7\u77e5\u8bc6\uff0c\u5230\u9002\u5e94\u771f\u5b9e\u4e16\u754c"},"content":{"rendered":"
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\u5f53 AI \u6a21\u578b\u4ece\u201c\u5355\u6b21\u5b8c\u6210\u4efb\u52a1\u201d\u8d70\u5411\u771f\u5b9e\u4e16\u754c\u90e8\u7f72\u65f6\uff0c\u771f\u6b63\u7684\u6311\u6218\u4e0d\u518d\u53ea\u662f\u53c2\u6570\u89c4\u6a21\u548c\u5355\u70b9\u6027\u80fd\uff0c\u800c\u662f\u6a21\u578b\u80fd\u5426\u5728\u53d8\u5316\u4e2d\u4fdd\u6301\u7a33\u5b9a\u3002<\/span><\/p>\n

\u5b83\u8981\u5728\u6301\u7eed\u51fa\u73b0\u7684\u65b0\u7c7b\u522b\u4e2d\u4e0d\u9057\u5fd8\u65e7\u77e5\u8bc6\uff0c\u8981\u4ece\u5927\u89c4\u6a21\u771f\u5b9e\u6570\u636e\u4e2d\u83b7\u5f97\u66f4\u5f3a\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u8981\u5728\u591a\u5ba2\u6237\u7aef\u3001\u6570\u636e\u5206\u5e03\u4e0d\u65ad\u53d8\u5316\u7684\u73af\u5883\u4e0b\u7ee7\u7eed\u5b66\u4e60\uff0c\u4e5f\u8981\u628a\u56fe\u50cf\u3001\u89c6\u9891\u548c 3D \u7b49\u4e0d\u540c\u89c6\u89c9\u7ecf\u9a8c\u7ec4\u7ec7\u6210\u7edf\u4e00\u7406\u89e3\u3002<\/span><\/p>\n

\u8fd9\u79cd\u53d8\u5316\u4e5f\u4f53\u73b0\u5728 CVPR 2026 \u7684\u76f8\u5173\u7814\u7a76\u8d8b\u52bf\u4e2d\u3002\u8d8a\u6765\u8d8a\u591a\u5de5\u4f5c\u4e0d\u518d\u53ea\u8ffd\u6c42\u67d0\u4e2a\u5355\u4e00\u4efb\u52a1\u4e0a\u7684\u6027\u80fd\u63d0\u5347\uff0c\u800c\u662f\u66f4\u5173\u6ce8\u6a21\u578b\u5728\u957f\u671f\u5b66\u4e60\u3001\u771f\u5b9e\u6570\u636e\u3001\u5206\u5e03\u53d8\u5316\u548c\u591a\u6a21\u6001\u534f\u540c\u4e2d\u7684\u7a33\u5b9a\u6027\u4e0e\u9002\u5e94\u80fd\u529b\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u6a21\u578b\u4e0d\u4ec5\u8981\u201c\u4f1a\u505a\u201d\uff0c\u8fd8\u8981\u80fd\u5728\u590d\u6742\u73af\u5883\u4e2d\u6301\u7eed\u505a\u5f97\u597d\u3002<\/span><\/p>\n

\u8fd9\u4e00\u8d8b\u52bf\u80cc\u540e\uff0c\u53cd\u6620\u7684\u662f\u5927\u6a21\u578b\u7814\u7a76\u6b63\u5728\u4ece\u201c\u80fd\u529b\u6269\u5f20\u201d\u8fdb\u5165\u201c\u80fd\u529b\u7ba1\u7406\u201d\u9636\u6bb5\u3002\u6a21\u578b\u4e0d\u4ec5\u8981\u5b66\u5f97\u591a\uff0c\u8fd8\u8981\u77e5\u9053\u54ea\u4e9b\u65e7\u77e5\u8bc6\u503c\u5f97\u4fdd\u7559\uff0c\u54ea\u4e9b\u7ecf\u9a8c\u53ef\u4ee5\u8fc1\u79fb\uff0c\u54ea\u4e9b\u7279\u5f81\u9700\u8981\u5bf9\u9f50\uff0c\u54ea\u4e9b\u6a21\u6001\u80fd\u591f\u4e92\u76f8\u8865\u5145\u3002<\/span><\/p>\n

\u65e0\u8bba\u662f\u6301\u7eed\u5b66\u4e60\u3001\u6570\u5b57\u4eba\u5efa\u6a21\u3001\u8054\u90a6\u5b66\u4e60\uff0c\u8fd8\u662f\u7edf\u4e00\u5927\u89c6\u89c9\u6a21\u578b\uff0c\u7814\u7a76\u8005\u771f\u6b63\u5173\u5fc3\u7684\u90fd\u662f\u540c\u4e00\u4e2a\u95ee\u9898\uff1a\u5982\u4f55\u8ba9 AI \u5728\u590d\u6742\u3001\u52a8\u6001\u3001\u4e0d\u5b8c\u6574\u7684\u73b0\u5b9e\u73af\u5883\u4e2d\uff0c\u4f9d\u7136\u4fdd\u6301\u53ef\u6cdb\u5316\u3001\u53ef\u9002\u5e94\u3001\u53ef\u534f\u540c\u548c\u53ef\u6301\u7eed\u8fdb\u5316\u7684\u80fd\u529b\u3002<\/span><\/p>\n

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\u300aQuantum-Gated Task-interaction Knowledge Distillation for Pre-trained Model-based Class-Incremental Learning\u300b\u5173\u6ce8\u7684\u662f\u57fa\u4e8e\u9884\u8bad\u7ec3\u6a21\u578b\u7684\u7c7b\u589e\u91cf\u5b66\u4e60\u95ee\u9898\uff0c\u76f8\u5173\u7814\u7a76\u6765\u81ea\u5317\u4eac\u90ae\u7535\u5927\u5b66\u4fe1\u606f\u4e0e\u901a\u4fe1\u5de5\u7a0b\u5b66\u9662\u548c\u6559\u80b2\u90e8\u4fe1\u606f\u7f51\u7edc\u5de5\u7a0b\u7814\u7a76\u4e2d\u5fc3\u3002<\/span><\/p>\n

\u8bba\u6587\u4e3b\u8981\u7814\u7a76\u5982\u4f55\u8ba9\u6a21\u578b\u5728\u4e0d\u65ad\u5b66\u4e60\u65b0\u7c7b\u522b\u7684\u540c\u65f6\uff0c\u5c3d\u91cf\u4fdd\u7559\u65e7\u7c7b\u522b\u77e5\u8bc6\uff0c\u51cf\u5c11\u707e\u96be\u6027\u9057\u5fd8\u3002\u7c7b\u589e\u91cf\u5b66\u4e60\u7684\u96be\u70b9\u5728\u4e8e\uff0c\u6a21\u578b\u4f1a\u6309\u4efb\u52a1\u987a\u5e8f\u4e0d\u65ad\u63a5\u89e6\u65b0\u7c7b\u522b\uff0c\u4f46\u6d4b\u8bd5\u65f6\u901a\u5e38\u4e0d\u77e5\u9053\u6837\u672c\u6765\u81ea\u54ea\u4e2a\u4efb\u52a1\u3002<\/span><\/p>\n

\u968f\u7740\u4efb\u52a1\u6570\u91cf\u589e\u52a0\uff0c\u4e0d\u540c\u4efb\u52a1\u7684\u7279\u5f81\u7a7a\u95f4\u53ef\u80fd\u53d1\u751f\u91cd\u53e0\uff0c\u65b0\u4efb\u52a1\u5b66\u4e60\u5bb9\u6613\u8986\u76d6\u65e7\u4efb\u52a1\u77e5\u8bc6\uff0c\u5bfc\u81f4\u6a21\u578b\u5bf9\u65e7\u7c7b\u522b\u8bc6\u522b\u80fd\u529b\u4e0b\u964d\u3002<\/span><\/p>\n

\u8fd1\u5e74\u6765\uff0c\u57fa\u4e8e\u9884\u8bad\u7ec3\u6a21\u578b\u7684\u7c7b\u589e\u91cf\u5b66\u4e60\u65b9\u6cd5\u901a\u5e38\u4f1a\u51bb\u7ed3\u4e3b\u5e72\u7f51\u7edc\uff0c\u53ea\u8bad\u7ec3\u8f7b\u91cf\u7ea7\u7684 prompt \u6216 adapter \u6a21\u5757\uff0c\u4ee5\u964d\u4f4e\u8bad\u7ec3\u6210\u672c\u5e76\u4fdd\u6301\u57fa\u7840\u8868\u793a\u80fd\u529b\u3002<\/span><\/p>\n

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\u9488\u5bf9\u8fd9\u4e00\u95ee\u9898\uff0c\u8bba\u6587\u63d0\u51fa\u4e86 QKD\uff0c\u4e5f\u5c31\u662f Quantum-Gated Task-interaction Knowledge Distillation\u3002\u5b83\u7684\u6838\u5fc3\u601d\u8def\u662f\u7528\u91cf\u5b50\u95e8\u63a7\u673a\u5236\u6765\u5efa\u6a21\u6837\u672c\u4e0e\u4e0d\u540c\u4efb\u52a1\u4e4b\u95f4\u7684\u76f8\u5173\u6027\uff0c\u518d\u6839\u636e\u8fd9\u79cd\u76f8\u5173\u6027\u6307\u5bfc\u77e5\u8bc6\u84b8\u998f\u548c\u63a8\u7406\u65f6\u7684 adapter \u878d\u5408\u3002<\/span><\/p>\n

\u7b80\u5355\u6765\u8bf4\uff0c\u6a21\u578b\u4e0d\u4ec5\u8981\u5b66\u4e60\u65b0\u4efb\u52a1\uff0c\u8fd8\u8981\u5224\u65ad\u54ea\u4e9b\u65e7\u4efb\u52a1\u548c\u5f53\u524d\u6837\u672c\u66f4\u76f8\u5173\uff0c\u4ece\u800c\u6709\u9009\u62e9\u5730\u5438\u6536\u65e7\u77e5\u8bc6\uff0c\u51cf\u5c11\u65e0\u5173\u4efb\u52a1\u5e26\u6765\u7684\u5e72\u6270\u3002<\/span><\/p>\n

\u65b9\u6cd5\u4e0a\uff0c\u8bba\u6587\u5148\u4e3a\u6bcf\u4e2a\u4efb\u52a1\u6784\u5efa task embedding\uff0c\u5e76\u901a\u8fc7\u53c2\u6570\u5316\u91cf\u5b50\u7535\u8def\u628a\u6837\u672c\u7279\u5f81\u548c\u4efb\u52a1\u8868\u793a\u6620\u5c04\u5230\u66f4\u9ad8\u7ef4\u7684 Hilbert \u7a7a\u95f4\u4e2d\uff0c\u7528\u91cf\u5b50\u95e8\u63a7\u8f93\u51fa\u6837\u672c\u5230\u5404\u4efb\u52a1\u7684\u76f8\u5173\u6027\u6743\u91cd\u3002<\/span><\/p>\n

\u968f\u540e\uff0c\u8fd9\u4e9b\u6743\u91cd\u4f1a\u88ab\u7528\u4e8e task-interaction knowledge distillation\uff0c\u8ba9\u5f53\u524d adapter \u4ece\u66f4\u76f8\u5173\u7684\u5386\u53f2 adapter \u4e2d\u5b66\u4e60\u7279\u5f81\u4fe1\u606f\uff0c\u800c\u4e0d\u662f\u5e73\u5747\u5438\u6536\u6240\u6709\u65e7\u4efb\u52a1\u77e5\u8bc6\u3002<\/span><\/p>\n

\u5728\u63a8\u7406\u9636\u6bb5\uff0c\u8bba\u6587\u7ee7\u7eed\u590d\u7528\u8fd9\u4e9b\u91cf\u5b50\u95e8\u63a7\u5f97\u5230\u7684\u76f8\u5173\u6027\u6743\u91cd\uff0c\u7528\u6765\u8fdb\u884c\u81ea\u9002\u5e94 adapter \u878d\u5408\u3002\u8fd9\u6837\u8bad\u7ec3\u548c\u6d4b\u8bd5\u4f7f\u7528\u7684\u662f\u540c\u4e00\u5957\u4efb\u52a1\u76f8\u5173\u6027\u4f30\u8ba1\u673a\u5236\uff0c\u53ef\u4ee5\u51cf\u5c11\u8bad\u7ec3\u9636\u6bb5\u77e5\u8bc6\u8fc1\u79fb\u548c\u6d4b\u8bd5\u9636\u6bb5\u4efb\u52a1\u8def\u7531\u4e4b\u95f4\u7684\u4e0d\u4e00\u81f4\u95ee\u9898\u3002<\/span><\/p>\n

\u5b9e\u9a8c\u65b9\u9762\uff0c\u8bba\u6587\u5728 CIFAR-100\u3001CUB-200\u3001ImageNet-A\u3001ImageNet-R \u548c VTAB \u7b49\u591a\u4e2a\u65e0\u6837\u672c\u56de\u653e\u7c7b\u589e\u91cf\u5b66\u4e60\u57fa\u51c6\u4e0a\u8fdb\u884c\u4e86\u9a8c\u8bc1\u3002\u96f7\u5cf0\u7f51<\/span><\/p>\n

\u7ed3\u679c\u663e\u793a\uff0cQKD \u80fd\u591f\u63d0\u5347\u6700\u7ec8\u51c6\u786e\u7387\u548c\u5e73\u5747\u589e\u91cf\u51c6\u786e\u7387\uff0c\u5e76\u4e14\u5728\u591a\u79cd\u8bbe\u7f6e\u4e0b\u8fbe\u5230\u9886\u5148\u6216\u6709\u7ade\u4e89\u529b\u7684\u8868\u73b0\u3002\u6d88\u878d\u5b9e\u9a8c\u4e5f\u8868\u660e\uff0c\u7528\u91cf\u5b50\u95e8\u63a7\u66ff\u4ee3\u666e\u901a\u4f59\u5f26\u76f8\u4f3c\u5ea6\u6216\u795e\u7ecf\u7f51\u7edc\u63a7\u5236\u5668\u540e\uff0c\u6a21\u578b\u80fd\u66f4\u597d\u5730\u6355\u6349\u590d\u6742\u4efb\u52a1\u5173\u7cfb\u3002<\/span><\/p>\n

\u8fd9\u7bc7\u8bba\u6587\u7684\u4eae\u70b9\u5728\u4e8e\uff0c\u5b83\u628a\u91cf\u5b50\u95e8\u63a7\u673a\u5236\u5f15\u5165\u5230\u7c7b\u589e\u91cf\u5b66\u4e60\u4e2d\u7684\u4efb\u52a1\u8def\u7531\u548c\u77e5\u8bc6\u84b8\u998f\u8fc7\u7a0b\u3002\u76f8\u6bd4\u7b80\u5355\u5730\u4fdd\u5b58\u65e7\u77e5\u8bc6\u6216\u72ec\u7acb\u8bad\u7ec3\u4e0d\u540c\u4efb\u52a1 adapter\uff0cQKD \u66f4\u5173\u6ce8\u4efb\u52a1\u4e4b\u95f4\u7684\u76f8\u5173\u6027\u5efa\u6a21\uff0c\u8ba9\u6a21\u578b\u5728\u5b66\u4e60\u65b0\u7c7b\u522b\u65f6\u6709\u9009\u62e9\u5730\u8fc1\u79fb\u65e7\u4efb\u52a1\u77e5\u8bc6\u3002<\/span><\/p>\n

\u603b\u4f53\u6765\u770b\uff0c\u8fd9\u9879\u5de5\u4f5c\u4e3a\u9884\u8bad\u7ec3\u6a21\u578b\u65f6\u4ee3\u7684\u6301\u7eed\u5b66\u4e60\u63d0\u4f9b\u4e86\u4e00\u79cd\u65b0\u7684\u601d\u8def\uff1a\u6a21\u578b\u4e0d\u4ec5\u8981\u907f\u514d\u9057\u5fd8\uff0c\u8fd8\u8981\u5b66\u4f1a\u5224\u65ad\u201c\u54ea\u4e9b\u65e7\u77e5\u8bc6\u503c\u5f97\u88ab\u5f53\u524d\u4efb\u52a1\u7ee7\u627f\u201d\u3002<\/span><\/p>\n

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\u8bba\u6587\u5730\u5740\uff1ahttps:\/\/arxiv.org\/pdf\/2604.11112v1<\/span><\/span><\/p>\n

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\u5982\u679c\u8bf4\u8fd9\u9879\u5de5\u4f5c\u5173\u6ce8\u7684\u662f\u6a21\u578b\u5728\u4e0d\u65ad\u5b66\u4e60\u65b0\u4efb\u52a1\u65f6\u5982\u4f55\u4fdd\u7559\u65e7\u77e5\u8bc6\uff0c\u90a3\u4e48\u300aLarge-scale Codec Avatars: The Unreasonable Effectiveness of Large-scale Avatar Pretraining\u300b\u5219\u628a\u89c6\u89d2\u8f6c\u5411\u4e86\u5927\u89c4\u6a21\u9884\u8bad\u7ec3\u5728 3D \u6570\u5b57\u4eba\u5efa\u6a21\u4e2d\u7684\u4f5c\u7528\u3002\u96f7\u5cf0\u7f51<\/span><\/p>\n

\u5b83\u540c\u6837\u5173\u5fc3\u6a21\u578b\u5982\u4f55\u4ece\u5927\u89c4\u6a21\u6570\u636e\u4e2d\u83b7\u5f97\u66f4\u5f3a\u7684\u6cdb\u5316\u80fd\u529b\uff0c\u53ea\u4e0d\u8fc7\u5e94\u7528\u5bf9\u8c61\u4ece\u5206\u7c7b\u4efb\u52a1\u8f6c\u5411\u4e86\u53ef\u9a71\u52a8\u7684\u9ad8\u8d28\u91cf\u6570\u5b57\u4eba\u3002\u8bba\u6587\u4e3b\u8981\u7814\u7a76\u5982\u4f55\u5728\u771f\u5b9e\u4e16\u754c\u8f93\u5165\u4e0b\uff0c\u5feb\u901f\u751f\u6210\u65e2\u80fd\u4fdd\u6301\u8eab\u4efd\u7279\u5f81\u3001\u53c8\u80fd\u8fdb\u884c\u7cbe\u7ec6\u8868\u60c5\u548c\u5168\u8eab\u52a8\u4f5c\u9a71\u52a8\u7684 3D \u6570\u5b57\u4eba\u3002<\/span><\/p>\n

\u8fd9\u9879\u4efb\u52a1\u7684\u6838\u5fc3\u96be\u70b9\u5728\u4e8e\uff0c\u9ad8\u4fdd\u771f\u548c\u5f3a\u6cdb\u5316\u4e4b\u95f4\u957f\u671f\u5b58\u5728\u77db\u76fe\u3002\u57fa\u4e8e\u591a\u89c6\u89d2\u5f71\u68da\u6570\u636e\u7684\u65b9\u6cd5\u901a\u5e38\u80fd\u751f\u6210\u7ec6\u8282\u4e30\u5bcc\u3001\u8868\u60c5\u548c\u52a8\u4f5c\u63a7\u5236\u7cbe\u786e\u7684\u6570\u5b57\u4eba\uff0c\u4f46\u91c7\u96c6\u6210\u672c\u9ad8\uff0c\u4e5f\u5f88\u96be\u6cdb\u5316\u5230\u666e\u901a\u7528\u6237\u968f\u624b\u62cd\u6444\u7684\u771f\u5b9e\u4e16\u754c\u89c6\u9891\u3002<\/span><\/p>\n

\u800c\u57fa\u4e8e\u5927\u89c4\u6a21\u91ce\u5916\u6570\u636e\u8bad\u7ec3\u7684\u65b9\u6cd5\u867d\u7136\u6cdb\u5316\u8303\u56f4\u66f4\u5e7f\uff0c\u4f46\u7531\u4e8e 3D \u4fe1\u606f\u4e0d\u5b8c\u6574\uff0c\u751f\u6210\u7ed3\u679c\u5f80\u5f80\u5bb9\u6613\u51fa\u73b0\u6a21\u7cca\u3001\u7ed3\u6784\u53d8\u5f62\u6216\u7ec6\u8282\u4e0d\u8db3\u7684\u95ee\u9898\u3002<\/span><\/p>\n

<\/a><\/section>\n

\u8bba\u6587\u5730\u5740\uff1ahttps:\/\/arxiv.org\/pdf\/2604.02320v2<\/span><\/span><\/p>\n

\u9488\u5bf9\u8fd9\u4e00\u95ee\u9898\uff0c\u8bba\u6587\u63d0\u51fa\u4e86 Large-Scale Codec Avatars\uff0c\u4e5f\u5c31\u662f LCA \u6846\u67b6\u3002\u5b83\u501f\u9274\u5927\u8bed\u8a00\u6a21\u578b\u548c\u89c6\u89c9\u57fa\u7840\u6a21\u578b\u4e2d\u7684\u201c\u9884\u8bad\u7ec3 + \u540e\u8bad\u7ec3\u201d\u601d\u8def\uff0c\u5148\u5728 100 \u4e07\u4e2a\u771f\u5b9e\u4e16\u754c\u5355\u76ee\u4eba\u7269\u89c6\u9891\u4e0a\u8fdb\u884c\u5927\u89c4\u6a21\u9884\u8bad\u7ec3\uff0c\u5b66\u4e60\u4eba\u4f53\u5916\u89c2\u548c\u51e0\u4f55\u7684\u901a\u7528\u5148\u9a8c\uff1b\u518d\u7528\u9ad8\u8d28\u91cf\u591a\u89c6\u89d2\u5f71\u68da\u6570\u636e\u8fdb\u884c\u540e\u8bad\u7ec3\uff0c\u63d0\u5347\u6a21\u578b\u7684\u53ef\u9a71\u52a8\u6027\u30013D \u5b8c\u6574\u6027\u548c\u89c6\u89c9\u4fdd\u771f\u5ea6\u3002<\/span><\/p>\n

\u65b9\u6cd5\u4e0a\uff0cLCA \u4f1a\u4ece\u5168\u8eab\u56fe\u50cf\u548c\u9762\u90e8\u7279\u5199\u4e2d\u63d0\u53d6\u56fe\u50cf token\uff0c\u540c\u65f6\u4ece\u6a21\u677f\u4eba\u4f53\u7f51\u683c\u4e2d\u63d0\u53d6\u51e0\u4f55 token\u3002\u6a21\u578b\u901a\u8fc7\u56fe\u50cf\u6ce8\u610f\u529b\u3001\u51e0\u4f55\u6ce8\u610f\u529b\u548c\u591a\u6a21\u6001\u6ce8\u610f\u529b\u878d\u5408\u4e0d\u540c\u6765\u6e90\u7684\u4fe1\u606f\uff0c\u518d\u7531 3D Gaussian \u89e3\u7801\u5668\u751f\u6210\u6570\u5b57\u4eba\u7684\u51e0\u4f55\u548c\u5916\u89c2\u5c5e\u6027\u3002<\/span><\/p>\n

\u89e3\u7801\u5668\u5206\u4e3a canonical \u5206\u652f\u548c pose-dependent \u5206\u652f\uff0c\u524d\u8005\u8d1f\u8d23\u9759\u6001\u8eab\u4efd\u4e0e\u5916\u89c2\uff0c\u540e\u8005\u8d1f\u8d23\u8868\u60c5\u3001\u773c\u795e\u3001\u624b\u90e8\u59ff\u6001\u548c\u670d\u88c5\u5f62\u53d8\u7b49\u52a8\u6001\u53d8\u5316\u3002<\/span><\/p>\n

\u8bba\u6587\u8fd8\u5c55\u793a\u4e86 LCA \u7684\u6269\u5c55\u80fd\u529b\u3002\u901a\u8fc7\u8f83\u5c0f\u6539\u52a8\uff0c\u5b83\u53ef\u4ee5\u652f\u6301\u5bbd\u677e\u670d\u88c5\u53d8\u5f62\u548c\u91cd\u65b0\u6253\u5149\u7b49\u529f\u80fd\uff0c\u5e76\u4e14\u5728\u6ca1\u6709\u76f4\u63a5\u76d1\u7763\u7684\u60c5\u51b5\u4e0b\uff0c\u5bf9\u773c\u955c\u3001\u5e3d\u5b50\u3001\u4e0d\u540c\u53d1\u578b\u3001\u4e0d\u540c\u670d\u88c5\u751a\u81f3\u98ce\u683c\u5316\u89d2\u8272\u90fd\u6709\u8f83\u597d\u7684\u6cdb\u5316\u80fd\u529b\u3002<\/span><\/p>\n

\u5b9e\u9a8c\u7ed3\u679c\u663e\u793a\uff0cLCA \u5728\u591a\u89c6\u89d2\u548c\u5355\u89c6\u89d2\u8f93\u5165\u4e0b\u90fd\u4f18\u4e8e\u5df2\u6709 3D \u6570\u5b57\u4eba\u65b9\u6cd5\uff0c\u5728\u9762\u90e8\u7ec6\u8282\u3001\u624b\u6307\u52a8\u4f5c\u3001\u8eab\u4f53\u59ff\u6001\u548c\u8eab\u4efd\u4fdd\u6301\u65b9\u9762\u8868\u73b0\u66f4\u7a33\u5b9a\u3002<\/span><\/p>\n

\u8fd9\u7bc7\u8bba\u6587\u7684\u4eae\u70b9\u5728\u4e8e\uff0c\u5b83\u628a 3D \u6570\u5b57\u4eba\u5efa\u6a21\u4ece\u201c\u5c0f\u89c4\u6a21\u9ad8\u8d28\u91cf\u91c7\u96c6\u201d\u63a8\u8fdb\u5230\u201c\u5927\u89c4\u6a21\u9884\u8bad\u7ec3\u9a71\u52a8\u201d\u7684\u65b0\u8303\u5f0f\u3002LCA \u4e0d\u53ea\u662f\u63d0\u9ad8\u4e86\u67d0\u4e2a\u6570\u5b57\u4eba\u6a21\u578b\u7684\u6548\u679c\uff0c\u800c\u662f\u8bc1\u660e\u4e86\u5927\u89c4\u6a21\u9884\u8bad\u7ec3\u540c\u6837\u53ef\u4ee5\u5e2e\u52a9 3D avatar \u540c\u65f6\u83b7\u5f97\u6cdb\u5316\u80fd\u529b\u548c\u9ad8\u4fdd\u771f\u8868\u73b0\u3002<\/span><\/p>\n

\u603b\u4f53\u6765\u770b\uff0c\u8fd9\u9879\u5de5\u4f5c\u8ba9\u6570\u5b57\u4eba\u66f4\u63a5\u8fd1\u771f\u5b9e\u5e94\u7528\u573a\u666f\uff1a\u7528\u6237\u53ea\u9700\u8981\u5c11\u91cf\u666e\u901a\u56fe\u50cf\u6216\u89c6\u9891\uff0c\u5c31\u6709\u53ef\u80fd\u5feb\u901f\u751f\u6210\u4e00\u4e2a\u53ef\u5b9e\u65f6\u9a71\u52a8\u3001\u53ef\u4fdd\u6301\u8eab\u4efd\u3001\u53ef\u8868\u8fbe\u7ec6\u817b\u8868\u60c5\u548c\u52a8\u4f5c\u7684\u9ad8\u8d28\u91cf 3D \u6570\u5b57\u4eba\u3002<\/span><\/p>\n

<\/a><\/section>\n

\u4ece\u6301\u7eed\u5b66\u4e60\u5230\u6570\u5b57\u4eba\u9884\u8bad\u7ec3\uff0c\u524d\u9762\u4e24\u9879\u5de5\u4f5c\u90fd\u5728\u8ba8\u8bba\u6a21\u578b\u5982\u4f55\u5728\u590d\u6742\u6570\u636e\u548c\u771f\u5b9e\u5e94\u7528\u6761\u4ef6\u4e0b\u4fdd\u6301\u7a33\u5b9a\u80fd\u529b\u3002\u63a5\u4e0b\u6765\uff0c\u300aFrom Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity\u300b\u8fdb\u4e00\u6b65\u628a\u95ee\u9898\u653e\u5230\u8054\u90a6\u5b66\u4e60\u573a\u666f\u4e2d\uff0c\u5173\u6ce8\u591a\u4e2a\u5ba2\u6237\u7aef\u3001\u6570\u636e\u4e0d\u65ad\u53d8\u5316\u3001\u5386\u53f2\u6837\u672c\u6709\u9650\u65f6\uff0c\u6a21\u578b\u8be5\u5982\u4f55\u66f4\u7a33\u5065\u5730\u6301\u7eed\u5b66\u4e60\u3002<\/span><\/p>\n

\u8bba\u6587\u4e3b\u8981\u7814\u7a76\u7684\u662f\uff1a\u5728\u591a\u4e2a\u5ba2\u6237\u7aef\u4e0d\u65ad\u5b66\u4e60\u65b0\u4efb\u52a1\u7684\u8fc7\u7a0b\u4e2d\uff0c\u5982\u4f55\u66f4\u6709\u6548\u5730\u5229\u7528\u6709\u9650\u7684\u5386\u53f2\u6837\u672c\uff0c\u51cf\u5c11\u6a21\u578b\u5bf9\u65e7\u77e5\u8bc6\u7684\u9057\u5fd8\uff0c\u540c\u65f6\u7f13\u89e3\u4e0d\u540c\u5ba2\u6237\u7aef\u6570\u636e\u5206\u5e03\u6301\u7eed\u53d8\u5316\u5e26\u6765\u7684\u5f71\u54cd\u3002<\/span><\/p>\n

\u8fd9\u9879\u4efb\u52a1\u7684\u96be\u70b9\u5728\u4e8e\uff0c\u8054\u90a6\u5b66\u4e60\u4e2d\u7684\u5ba2\u6237\u7aef\u901a\u5e38\u4e0d\u80fd\u5171\u4eab\u539f\u59cb\u6570\u636e\uff0c\u800c\u4e14\u6bcf\u4e2a\u5ba2\u6237\u7aef\u770b\u5230\u7684\u6570\u636e\u7c7b\u522b\u548c\u5206\u5e03\u5e76\u4e0d\u4e00\u81f4\u3002\u968f\u7740\u65b0\u4efb\u52a1\u4e0d\u65ad\u5230\u6765\uff0c\u6a21\u578b\u5f88\u5bb9\u6613\u88ab\u65b0\u7c7b\u522b\u5f71\u54cd\uff0c\u9010\u6e10\u5fd8\u8bb0\u8fc7\u53bb\u5b66\u8fc7\u7684\u65e7\u7c7b\u522b\u3002<\/span><\/p>\n

\u4ee5\u5f80\u7684\u6837\u672c\u56de\u653e\u65b9\u6cd5\u5927\u591a\u5173\u6ce8\u201c\u5e94\u8be5\u4fdd\u5b58\u54ea\u4e9b\u4ee3\u8868\u6027\u6837\u672c\u201d\uff0c\u4f46\u8bba\u6587\u6307\u51fa\uff0c\u4ec5\u4ec5\u9009\u51fa\u6837\u672c\u8fd8\u4e0d\u591f\uff0c\u66f4\u5173\u952e\u7684\u662f\u5982\u4f55\u5728\u8bad\u7ec3\u548c\u63a8\u7406\u9636\u6bb5\u66f4\u597d\u5730\u4f7f\u7528\u8fd9\u4e9b\u6837\u672c\u3002<\/span><\/p>\n

<\/a><\/section>\n

\u8bba\u6587\u5730\u5740\uff1ahttps:\/\/arxiv.org\/pdf\/2604.08617v1<\/span><\/span><\/p>\n

\u9488\u5bf9\u8fd9\u4e00\u95ee\u9898\uff0c\u8bba\u6587\u63d0\u51fa\u4e86 FEAT\uff0c\u4e5f\u5c31\u662f\u8054\u90a6\u51e0\u4f55\u611f\u77e5\u6821\u6b63\u65b9\u6cd5\u3002\u5b83\u7684\u6838\u5fc3\u601d\u8def\u662f\u4ece\u7279\u5f81\u7a7a\u95f4\u7684\u51e0\u4f55\u7ed3\u6784\u5165\u624b\uff0c\u7f13\u89e3\u5ba2\u6237\u7aef\u4e4b\u95f4\u7684\u6570\u636e\u5f02\u8d28\u6027\u548c\u7c7b\u522b\u4e0d\u5e73\u8861\u95ee\u9898\u3002\u7b80\u5355\u6765\u8bf4\uff0c\u6a21\u578b\u4e0d\u4ec5\u8981\u8bb0\u4f4f\u8fc7\u53bb\u4efb\u52a1\u4e2d\u7684\u6837\u672c\uff0c\u8fd8\u8981\u8ba9\u4e0d\u540c\u5ba2\u6237\u7aef\u3001\u4e0d\u540c\u4efb\u52a1\u5b66\u5230\u7684\u7279\u5f81\u7ed3\u6784\u5c3d\u91cf\u4fdd\u6301\u4e00\u81f4\uff0c\u907f\u514d\u5c11\u6570\u7c7b\u7279\u5f81\u88ab\u591a\u6570\u7c7b\u201c\u62c9\u504f\u201d\u3002<\/span><\/p>\n

\u65b9\u6cd5\u4e0a\uff0cFEAT \u5305\u542b\u4e24\u4e2a\u5173\u952e\u6a21\u5757\u3002\u7b2c\u4e00\u4e2a\u662f Geometric Structure Alignment\uff0c\u7528\u56fa\u5b9a\u5171\u4eab\u7684 ETF \u539f\u578b\u4f5c\u4e3a\u53c2\u8003\u7ed3\u6784\uff0c\u5bf9\u9f50\u7279\u5f81\u4e4b\u95f4\u7684\u89d2\u5ea6\u5173\u7cfb\uff0c\u4ece\u800c\u8ba9\u4e0d\u540c\u5ba2\u6237\u7aef\u548c\u4e0d\u540c\u4efb\u52a1\u4e2d\u7684\u7c7b\u522b\u7279\u5f81\u4fdd\u6301\u66f4\u7a33\u5b9a\u7684\u51e0\u4f55\u5206\u5e03\u3002<\/span><\/p>\n

\u7b2c\u4e8c\u4e2a\u662f Energy-based Geometric Correction\uff0c\u5b83\u5728\u63a8\u7406\u9636\u6bb5\u79fb\u9664\u4e0e\u5f53\u524d\u4efb\u52a1\u65e0\u5173\u7684\u65b9\u5411\u6210\u5206\uff0c\u51cf\u5c11\u6a21\u578b\u5bf9\u591a\u6570\u7c7b\u7684\u504f\u5411\uff0c\u63d0\u9ad8\u5bf9\u5c11\u6570\u7c7b\u548c\u65e7\u7c7b\u522b\u7684\u8bc6\u522b\u80fd\u529b\u3002<\/span><\/p>\n

\u5b9e\u9a8c\u4e2d\uff0c\u8bba\u6587\u5728 CIFAR10\u3001CIFAR100 \u548c TinyImageNet-Subset \u7b49\u6570\u636e\u96c6\u4e0a\u8fdb\u884c\u9a8c\u8bc1\uff0c\u5e76\u8bbe\u7f6e\u4e86\u4e0d\u540c\u5ba2\u6237\u7aef\u6570\u91cf\u3001\u4efb\u52a1\u5212\u5206\u548c\u5f02\u8d28\u6027\u7a0b\u5ea6\u3002\u7ed3\u679c\u663e\u793a\uff0cFEAT \u80fd\u591f\u5728\u591a\u79cd\u8bbe\u7f6e\u4e0b\u63d0\u5347 Top-1 Accuracy\uff0c\u5e76\u4e14\u53ef\u4ee5\u4e0e Re-Fed+\u3001FedCBDR \u7b49\u5df2\u6709\u6837\u672c\u56de\u653e\u65b9\u6cd5\u7ed3\u5408\u4f7f\u7528\uff0c\u5728\u4fdd\u6301\u8f83\u4f4e\u901a\u4fe1\u5f00\u9500\u7684\u540c\u65f6\u8fdb\u4e00\u6b65\u63d0\u5347\u6027\u80fd\u3002<\/span><\/p>\n

\u8fd9\u7bc7\u8bba\u6587\u7684\u4eae\u70b9\u5728\u4e8e\uff0c\u5b83\u6ca1\u6709\u7ee7\u7eed\u628a\u91cd\u70b9\u653e\u5728\u201c\u5982\u4f55\u6311\u9009\u56de\u653e\u6837\u672c\u201d\u4e0a\uff0c\u800c\u662f\u8fdb\u4e00\u6b65\u601d\u8003\u201c\u9009\u51fa\u6765\u7684\u6837\u672c\u8be5\u5982\u4f55\u88ab\u66f4\u6709\u6548\u5730\u4f7f\u7528\u201d\u3002<\/span><\/p>\n

\u901a\u8fc7\u51e0\u4f55\u7ed3\u6784\u5bf9\u9f50\u548c\u63a8\u7406\u9636\u6bb5\u6821\u6b63\uff0cFEAT \u8ba9\u8054\u90a6\u6301\u7eed\u5b66\u4e60\u5728\u9762\u5bf9\u52a8\u6001\u5f02\u8d28\u6027\u548c\u7c7b\u522b\u4e0d\u5e73\u8861\u65f6\u66f4\u52a0\u7a33\u5b9a\u3002\u603b\u4f53\u6765\u770b\uff0c\u8fd9\u9879\u5de5\u4f5c\u4e3a\u8d44\u6e90\u53d7\u9650\u3001\u591a\u5ba2\u6237\u7aef\u3001\u4efb\u52a1\u6301\u7eed\u53d8\u5316\u7684\u5b9e\u9645\u8054\u90a6\u5b66\u4e60\u573a\u666f\u63d0\u4f9b\u4e86\u4e00\u79cd\u66f4\u7cbe\u7ec6\u3001\u66f4\u7a33\u5065\u7684\u6837\u672c\u56de\u653e\u589e\u5f3a\u65b9\u6848\u3002<\/span><\/p>\n

<\/a><\/section>\n

\u5982\u679c\u8bf4 FEAT \u5173\u6ce8\u7684\u662f\u591a\u5ba2\u6237\u7aef\u3001\u591a\u4efb\u52a1\u73af\u5883\u4e0b\u7684\u6301\u7eed\u5b66\u4e60\u7a33\u5b9a\u6027\uff0c\u90a3\u4e48\u300aModeling Cross-vision Synergy for Unified Large Vision Model\u300b\u5219\u8fdb\u4e00\u6b65\u8ba8\u8bba\u5927\u89c6\u89c9\u6a21\u578b\u5982\u4f55\u5728\u66f4\u5e7f\u6cdb\u7684\u89c6\u89c9\u6a21\u6001\u4e4b\u95f4\u5efa\u7acb\u534f\u540c\u5173\u7cfb\uff0c\u5b83\u9762\u5bf9\u7684\u95ee\u9898\u4e0d\u518d\u53ea\u662f\u201c\u5982\u4f55\u4fdd\u7559\u65e7\u77e5\u8bc6\u201d\uff0c\u800c\u662f\u5982\u4f55\u8ba9\u56fe\u50cf\u3001\u89c6\u9891\u548c 3D \u8fd9\u51e0\u79cd\u89c6\u89c9\u7ecf\u9a8c\u771f\u6b63\u4e92\u76f8\u8865\u5145\u3002<\/span><\/p>\n

\u76f8\u5173\u7814\u7a76\u6765\u81ea\u65b0\u52a0\u5761\u56fd\u7acb\u5927\u5b66\u548c\u5357\u6d0b\u7406\u5de5\u5927\u5b66\u3002\u8bba\u6587\u4e3b\u8981\u7814\u7a76\u5982\u4f55\u8ba9\u4e00\u4e2a\u5927\u89c6\u89c9\u6a21\u578b\u540c\u65f6\u5904\u7406\u56fe\u50cf\u3001\u89c6\u9891\u548c 3D \u6570\u636e\uff0c\u5e76\u4e14\u4e0d\u662f\u7b80\u5355\u5730\u628a\u4e0d\u540c\u6a21\u6001\u653e\u8fdb\u540c\u4e00\u4e2a\u6846\u67b6\u91cc\uff0c\u800c\u662f\u8ba9\u5b83\u4eec\u4e4b\u95f4\u771f\u6b63\u5f62\u6210\u4e92\u8865\u548c\u534f\u540c\u3002<\/span><\/p>\n

\u8fd9\u9879\u4efb\u52a1\u7684\u80cc\u666f\u5728\u4e8e\uff0c\u56fe\u50cf\u3001\u89c6\u9891\u548c 3D \u573a\u666f\u867d\u7136\u5f62\u5f0f\u4e0d\u540c\uff0c\u4f46\u672c\u8d28\u4e0a\u90fd\u6765\u81ea\u89c6\u89c9\u4e16\u754c\u3002\u56fe\u50cf\u66f4\u64c5\u957f\u63d0\u4f9b\u989c\u8272\u3001\u7eb9\u7406\u3001\u5e03\u5c40\u7b49\u9759\u6001\u4fe1\u606f\uff0c\u89c6\u9891\u5305\u542b\u8fd0\u52a8\u548c\u65f6\u95f4\u53d8\u5316\uff0c3D \u6570\u636e\u5219\u80fd\u63d0\u4f9b\u7a7a\u95f4\u7ed3\u6784\u3001\u8ddd\u79bb\u548c\u51e0\u4f55\u5173\u7cfb\u3002<\/span><\/p>\n

\u4ee5\u5f80\u7684\u7edf\u4e00\u89c6\u89c9\u6a21\u578b\u5927\u591a\u8ffd\u6c42\u201c\u529f\u80fd\u6574\u5408\u201d\uff0c\u4e5f\u5c31\u662f\u8ba9\u4e00\u4e2a\u6a21\u578b\u80fd\u63a5\u6536\u591a\u79cd\u89c6\u89c9\u8f93\u5165\uff0c\u4f46\u4e0d\u540c\u6a21\u6001\u4e4b\u95f4\u7f3a\u5c11\u6df1\u5165\u4ea4\u4e92\uff0c\u6a21\u578b\u5f88\u96be\u628a\u89c6\u9891\u4e2d\u7684\u65f6\u95f4\u5148\u9a8c\u30013D \u4e2d\u7684\u7a7a\u95f4\u5148\u9a8c\u548c\u56fe\u50cf\u4e2d\u7684\u89c6\u89c9\u7ec6\u8282\u771f\u6b63\u7ed3\u5408\u8d77\u6765\u3002<\/span><\/p>\n

<\/a><\/section>\n

\u8bba\u6587\u5730\u5740\uff1ahttps:\/\/arxiv.org\/pdf\/2603.03564v1<\/span><\/span><\/p>\n

\u9488\u5bf9\u8fd9\u4e00\u95ee\u9898\uff0c\u8bba\u6587\u63d0\u51fa\u4e86 PolyV \u6846\u67b6\uff0c\u76ee\u6807\u662f\u5b9e\u73b0\u8de8\u89c6\u89c9\u534f\u540c\u3002\u5b83\u91c7\u7528\u7a00\u758f MoE \u67b6\u6784\uff0c\u5e76\u901a\u8fc7\u52a8\u6001\u8def\u7531\u5668\u534f\u8c03\u4e0d\u540c\u4e13\u5bb6\u6a21\u5757\uff0c\u8ba9\u6bcf\u4e2a\u4e13\u5bb6\u5b66\u4e60\u7279\u5b9a\u6a21\u6001\u7684\u5148\u9a8c\u77e5\u8bc6\uff0c\u540c\u65f6\u53c8\u80fd\u5728\u56fe\u50cf\u3001\u89c6\u9891\u548c 3D \u4e4b\u95f4\u8fdb\u884c\u53cc\u5411\u4ea4\u4e92\u548c\u76f8\u4e92\u8865\u5145\u3002\u4e5f\u5c31\u662f\u8bf4\uff0c\u6a21\u578b\u4e0d\u662f\u628a\u4e0d\u540c\u6a21\u6001\u7684\u7279\u5f81\u7b80\u5355\u62fc\u63a5\u8d77\u6765\uff0c\u800c\u662f\u8ba9\u4e0d\u540c\u6a21\u6001\u4e2d\u7684\u77e5\u8bc6\u5728\u63a8\u7406\u8fc7\u7a0b\u4e2d\u4e92\u76f8\u5f71\u54cd\u3001\u4e92\u76f8\u589e\u5f3a\u3002<\/span><\/p>\n

\u8bad\u7ec3\u4e0a\uff0c\u8bba\u6587\u8bbe\u8ba1\u4e86\u534f\u540c\u611f\u77e5\u8bad\u7ec3\u6d41\u7a0b\u3002\u7b2c\u4e00\u9636\u6bb5\u662f\u6a21\u6001\u7279\u5b9a\u9884\u8bad\u7ec3\uff0c\u8ba9\u6a21\u578b\u5206\u522b\u5b66\u4e60\u56fe\u50cf\u3001\u89c6\u9891\u548c 3D \u6570\u636e\u4e2d\u7684\u57fa\u7840\u80fd\u529b\uff1b\u7b2c\u4e8c\u9636\u6bb5\u662f\u8de8\u89c6\u89c9\u534f\u540c\u8bad\u7ec3\uff0c\u5305\u62ec\u7c97\u7c92\u5ea6\u548c\u7ec6\u7c92\u5ea6\u4e24\u4e2a\u5c42\u9762\u3002<\/span><\/p>\n

\u7c97\u7c92\u5ea6\u8bad\u7ec3\u901a\u8fc7\u77e5\u8bc6\u84b8\u998f\u5f15\u5165\u89c6\u9891\u57fa\u7840\u6a21\u578b\u548c 3D \u57fa\u7840\u6a21\u578b\u4e2d\u7684\u65f6\u95f4\u3001\u7a7a\u95f4\u5148\u9a8c\uff1b\u7ec6\u7c92\u5ea6\u8bad\u7ec3\u5219\u8fdb\u4e00\u6b65\u5bf9\u9f50\u4e0d\u540c\u6a21\u6001\u4e2d\u7684\u5bf9\u8c61\u5c5e\u6027\u3001\u7a7a\u95f4\u5173\u7cfb\u548c\u4e8b\u4ef6\u4e00\u81f4\u6027\uff0c\u4f7f\u6a21\u578b\u80fd\u5728\u66f4\u7ec6\u7684\u5c42\u9762\u4e0a\u5efa\u7acb\u8de8\u6a21\u6001\u5bf9\u5e94\u5173\u7cfb\u3002<\/span><\/p>\n

\u8bba\u6587\u8fd8\u5f15\u5165\u4e86 synergy token\uff0c\u8ba9\u6a21\u578b\u5728\u751f\u6210\u6700\u7ec8\u56de\u7b54\u4e4b\u524d\u5148\u5f62\u6210\u4e00\u79cd\u4e2d\u95f4\u7684\u201c\u534f\u540c\u8868\u793a\u201d\u3002\u8fd9\u79cd\u8bbe\u8ba1\u76f8\u5f53\u4e8e\u8ba9\u6a21\u578b\u5148\u7efc\u5408\u6765\u81ea\u4e0d\u540c\u89c6\u89c9\u6a21\u6001\u7684\u8865\u5145\u4fe1\u606f\uff0c\u518d\u8fdb\u884c\u56de\u7b54\uff0c\u4ece\u800c\u63d0\u5347\u5b83\u5728\u7a7a\u95f4\u63a8\u7406\u3001\u65f6\u95f4\u63a8\u7406\u548c\u8de8\u6a21\u6001\u7406\u89e3\u4efb\u52a1\u4e2d\u7684\u8868\u73b0\u3002<\/span><\/p>\n

\u5b9e\u9a8c\u4e2d\uff0cPolyV \u5728\u56fe\u50cf\u3001\u89c6\u9891\u548c 3D \u7406\u89e3\u76f8\u5173\u7684 10 \u4e2a\u4ee3\u8868\u6027\u57fa\u51c6\u4e0a\u8fdb\u884c\u4e86\u9a8c\u8bc1\uff0c\u5305\u62ec MMStar\u30013DSRBench\u3001MMSI-Bench\u3001VideoMME\u3001VSI-Bench\u3001CVBench\u3001ScanQA\u3001SQA3D \u548c Open-EQA \u7b49\u4efb\u52a1\u3002\u7ed3\u679c\u663e\u793a\uff0cPolyV \u5728\u591a\u4e2a\u4efb\u52a1\u4e0a\u90fd\u4f18\u4e8e\u5df2\u6709\u6a21\u578b\uff0c\u5e76\u4e14\u76f8\u6bd4\u5176\u57fa\u7840\u6a21\u578b Qwen2.5-VL-7B\uff0c\u5e73\u5747\u63d0\u5347\u7ea6 10%\u3002<\/span><\/p>\n

\u8fd9\u7bc7\u8bba\u6587\u7684\u4eae\u70b9\u5728\u4e8e\uff0c\u5b83\u628a\u7edf\u4e00\u89c6\u89c9\u6a21\u578b\u4ece\u201c\u80fd\u5904\u7406\u591a\u79cd\u8f93\u5165\u201d\u63a8\u8fdb\u5230\u201c\u80fd\u8ba9\u591a\u79cd\u89c6\u89c9\u6a21\u6001\u5f7c\u6b64\u534f\u540c\u201d\u3002\u8fc7\u53bb\u7684\u6a21\u578b\u53ef\u80fd\u53ea\u662f\u5206\u522b\u7406\u89e3\u56fe\u50cf\u3001\u89c6\u9891\u6216 3D\uff0c\u800c PolyV \u8bd5\u56fe\u8ba9\u6a21\u578b\u5229\u7528\u89c6\u9891\u4e2d\u7684\u8fd0\u52a8\u77e5\u8bc6\u589e\u5f3a\u56fe\u50cf\u63a8\u7406\uff0c\u7528 3D \u4e2d\u7684\u51e0\u4f55\u77e5\u8bc6\u589e\u5f3a\u7a7a\u95f4\u7406\u89e3\uff0c\u518d\u901a\u8fc7 MoE \u548c\u534f\u540c\u8bad\u7ec3\u673a\u5236\u5b9e\u73b0\u66f4\u7075\u6d3b\u7684\u89c6\u89c9\u63a8\u7406\u3002<\/span><\/p>\n

\u603b\u4f53\u6765\u770b\uff0c\u8fd9\u9879\u5de5\u4f5c\u5f3a\u8c03\u7684\u662f\u4e0b\u4e00\u4ee3\u5927\u89c6\u89c9\u6a21\u578b\u7684\u4e00\u4e2a\u91cd\u8981\u65b9\u5411\uff1a\u6a21\u578b\u4e0d\u4ec5\u8981\u770b\u5f97\u591a\uff0c\u8fd8\u8981\u80fd\u628a\u4e0d\u540c\u89c6\u89c9\u7ecf\u9a8c\u7ec4\u7ec7\u8d77\u6765\uff0c\u5f62\u6210\u66f4\u63a5\u8fd1\u4eba\u7c7b\u89c6\u89c9\u7cfb\u7edf\u7684\u7efc\u5408\u7406\u89e3\u80fd\u529b\u3002<\/span><\/p>\n

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\u53bb\u54ea\u770b CVPR \u6838\u5fc3\u3010\u6f14\u8bb2\/\u8bba\u6587\u3011\u8be6\u89e3\uff1f<\/span><\/span><\/strong><\/p>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n<\/section>\n

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\u4e3a\u4e86\u8ba9<\/span>\u56fd\u5185\u7684\u7814\u53d1\u8005\u3001\u521b\u4e1a\u8005\u4e0e\u6295\u8d44\u4eba\u80fd\u591f\u6beb\u65e0\u65f6\u5dee\u5730\u638c\u63e1\u672c\u5c4a CVPR 2026 \u7684\u5b8c\u6574\u5e72\u8d27\uff0c\u96f7\u5cf0\u7f51(\u516c\u4f17\u53f7\uff1a\u96f7\u5cf0\u7f51)<\/span>\u5df2\u5168\u9762\u4e0a\u7ebf<\/span><\/span>\u3010CVPR 2026 \u6df1\u5ea6\u4e13\u533a\u3011<\/span>\u3002<\/span><\/span><\/span><\/p>\n

\u4e13\u533a\u4e0d\u4ec5\u5168\u9762\u6536\u5f55\u4e86\u91cd\u78c5\u8bba\u6587\u7684\u5de5\u7a0b\u5316\u89e3\u8bfb\u3001\u4e13\u5bb6\u524d\u6cbf\u6f14\u8bb2\uff0c\u66f4\u5c06\u6301\u7eed\u66f4\u65b0\u524d\u65b9\u8bb0\u8005\u7684\u7b2c\u4e00\u624b\u4f1a\u8bae\u52a8\u6001\u3002<\/span><\/span><\/p>\n

\u626b\u63cf\u4e0b\u65b9\u4e8c\u7ef4\u7801\uff0c\u6216\u70b9\u51fb<\/span><\/span>\u300c<\/span><\/span>\u9605\u8bfb\u539f\u6587<\/span><\/span>\u300d<\/span><\/span>\u5173\u6ce8\u4e13\u533a\u3002<\/span><\/span><\/span><\/p>\n

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

\u4e0e\u5168\u7403 8000 \u540d\u9876\u5c16\u5927\u8111\u540c\u6b65\u547c\u5438\uff0c\u62a2\u5148\u900f\u89c6<\/span><\/span>\u5177\u8eab\u667a\u80fd\u7684\u4e0b\u4e00\u4e2a\u4e94\u5e74\uff01<\/span><\/span><\/p>\n<\/section>\n<\/section>\n

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\u96f7\u5cf0\u7f51\u539f\u521b\u6587\u7ae0\uff0c\u672a\u7ecf\u6388\u6743\u7981\u6b62\u8f6c\u8f7d\u3002\u8be6\u60c5\u89c1\u8f6c\u8f7d\u987b\u77e5<\/a>\u3002<\/p>\n

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

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