{"id":45877,"date":"2025-10-24T18:00:48","date_gmt":"2025-10-24T10:00:48","guid":{"rendered":"https:\/\/case.ntu.edu.tw\/blog\/?p=45877"},"modified":"2025-10-22T10:05:55","modified_gmt":"2025-10-22T02:05:55","slug":"%e8%ae%93%e9%9b%bb%e8%85%a6%e3%80%8c%e7%9c%8b%e5%9c%96%e8%aa%aa%e6%95%85%e4%ba%8b%e3%80%8d%ef%bc%9a%e9%81%8b%e7%94%a8%e5%a4%a7%e8%aa%9e%e8%a8%80%e6%a8%a1%e5%9e%8b%e7%9a%84%e8%a6%96%e8%a6%ba%e5%95%8f","status":"publish","type":"post","link":"https:\/\/case.ntu.edu.tw\/blog\/?p=45877","title":{"rendered":"\u8b93\u96fb\u8166\u300c\u770b\u5716\u8aaa\u6545\u4e8b\u300d\uff1a\u904b\u7528\u5927\u8a9e\u8a00\u6a21\u578b\u7684\u8996\u89ba\u554f\u7b54"},"content":{"rendered":"

\u5927\u8a9e\u8a00\u6a21\u578b\u9664\u4e86\u89e3\u6c7a\u81ea\u7136\u8a9e\u8a00\u8655\u7406\u7684\u76f8\u95dc\u554f\u984c\u4e4b\u5916\uff0c\u5728\u300c\u8996\u89ba\u300d\u7b49\u5176\u4ed6\u591a\u6a21\u614b\u5b78\u7fd2\u9818\u57df\u65b9\u9762\uff0c\u4e5f\u53ef\u4ee5\u6709\u4e0d\u540c\u7684\u61c9\u7528\uff0c\u8996\u89ba\u554f\u7b54 (Visual Question Answering, VQA) \u5c31\u662f\u5176\u4e2d\u4e00\u500b\u7576\u4eca\u4eba\u5de5\u667a\u6167\u767c\u5c55\u65b9\u9762\u4e00\u500b\u91cd\u8981\u7684\u7814\u7a76\u65b9\u5411\u3002\u800c\u77e5\u8b58\u578b\u8996\u89ba\u554f\u7b54 (Knowledge-Based VQA, KB-VQA) \u66f4\u662f\u9032\u4e00\u6b65\u6311\u6230\u4e86\u6a21\u578b\u7684\u63a8\u7406\u80fd\u529b\uff0c\u8981\u8b93\u7cfb\u7d71\u9664\u4e86\u7406\u89e3\u5716\u50cf\u4e4b\u5916\uff0c\u9084\u8981\u80fd\u7406\u89e3\u4f7f\u7528\u8005\u63d0\u51fa\u7684\u554f\u984c\uff0c\u518d\u6574\u5408\u5916\u90e8\u77e5\u8b58\u4ee5\u7522\u751f\u6b63\u78ba\u7b54\u6848\u3002\u4ee5\u4eba\u985e\u800c\u8a00\uff0c\u770b\u4f3c\u76f4\u89c0\u7684\u300c\u770b\u5716\u56de\u7b54\u554f\u984c\u300d\uff0c\u5c0d\u65bc\u4eba\u5de5\u667a\u6167\u800c\u8a00\uff0c\u5fc5\u9808\u6574\u5408\u597d\u5e7e\u6bb5\u6a21\u7d44\u624d\u6709\u53ef\u80fd\u767c\u751f\uff01<\/strong><\/span><\/p>\n

\u64b0\u6587\uff5c\u738b\u51a0\u4e91<\/strong><\/p>\n

\"\"
\u4f86\u6e90\uff1aMotionElements<\/figcaption><\/figure>\n
\u73fe\u6709\u65b9\u6cd5\u7684\u4fb7\u9650\u6027<\/strong><\/span><\/h6>\n

\u8996\u89ba\u554f\u7b54 (VQA) \u662f\u8b93\u6a5f\u5668\u53ef\u4ee5\u5728\u7d66\u5b9a\u7684\u5716\u7247\u4e2d\uff0c\u91dd\u5c0d\u4f7f\u7528\u8005\u63d0\u51fa\u7684\u81ea\u7531\u5f62\u5f0f\u7684\u554f\u984c\u4f86\u9032\u884c\u56de\u7b54\uff0c\u4f8b\u5982\uff0c\u96a8\u6a5f\u770b\u5230\u5982\u4e0b\u65b9\u7684\u5716\u7247\u4e4b\u5f8c\uff0c\u8a62\u554f\u96fb\u8166\u300c\u5de6\u624b\u908a\u7684\u90a3\u500b\u98df\u7269\u662f\u4ec0\u9ebc\uff1f\u300d\u770b\u770b\u96fb\u8166\u662f\u5426\u80fd\u56de\u7b54\u90a3\u662f\u4ec0\u9ebc\u98df\u7269\u3002\u5982\u679c\u80fd\u9806\u5229\u5b8c\u6210\u4e00\u5f35\u5716\u7247\u3001\u4e00\u7d44\u554f\u7b54\u7684\u8a71\uff0c\u5c31\u8868\u793a\u96fb\u8166\u80fd\u5920\u9806\u5229\u7684\u5b8c\u6210\u4e00\u7d44\u8996\u89ba\u554f\u7b54\u4e86\u3002<\/p>\n

\"\"<\/p>\n

\u800c\u5982\u679c\u8981\u512a\u5316VQA\u6a21\u578b\uff0c\u5927\u81f4\u53ef\u4ee5\u5206\u6210\u5e7e\u7a2e\u65b9\u6cd5\u4f86\u512a\u5316\uff0c\u4f8b\u5982\u88fd\u4f5c\u66f4\u597d\u7684\u8996\u89ba\u7279\u5fb5\u3001\u4f7f\u7528\u66f4\u5177\u6709\u6548\u80fd\u7684\u6a21\u578b\u67b6\u69cb\uff0c\u6216\u8005\u662f\u63d0\u5347\u5b78\u7fd2\u7684\u6f14\u7b97\u6cd5\u7b49\u7b49\uff0c\u800c\u81ea\u5f9e\u300cTransformer\u300d\u67b6\u69cb\u5728\u8655\u7406\u81ea\u7136\u8a9e\u8a00\u554f\u984c\u53d6\u5f97\u6210\u529f\u4e4b\u5f8c\uff0c\u4e5f\u6709\u8a31\u591a\u7814\u7a76\u958b\u59cb\u5c07Transformer\u67b6\u69cb\u512a\u5316\u9032VQA\u6a21\u578b\u4e4b\u4e2d\u3002<\/p>\n

\u66f4\u6709\u751a\u8005\uff0c\u5982\u679c\u8981\u628aVQA\u6a21\u578b\u61c9\u7528\u5230\u66f4\u9ad8\u7b49\u7684\u77e5\u8b58\u5c64\u6b21\u7684\u8b70\u984c\u65b9\u9762\uff0c\u5247\u6703\u9032\u4e00\u6b65\u7684\u9700\u8981\u5efa\u6210\u300cKB-VQA\u300d\uff0c\u800c\u65e9\u671f\u9019\u985e\u578b\u7684\u7cfb\u7d71\uff0c\u5927\u591a\u9700\u8981\u4ef0\u8cf4\u56fa\u5b9a\u7684\u77e5\u8b58\u5eab\u4f86\u53d6\u5f97\u7b54\u6848\uff0c\u800c\u9019\u4e9b\u77e5\u8b58\u4f86\u81ea\u65bc\u975e\u5e38\u5ee3\u6cdb\u7684\u77e5\u8b58\u4f86\u6e90\uff0c\u4f8b\u5982\u300cConceptNet\u300d\u3001\u300cWikipedia\u300d\u7b49\u7b49\uff0c\u53e6\u5916\uff0c\u9664\u4e86\u9019\u4e9b\u5916\u5728\u7684\u77e5\u8b58\u5eab\u4f86\u6e90\u4e4b\u5916\uff0c\u4e5f\u53ef\u4ee5\u76f4\u63a5\u4f7f\u7528\u5927\u8a9e\u8a00\u6a21\u578b (LLM) \u672c\u8eab\u6240\u5177\u6709\u7684\u300c\u96b1\u6027\u77e5\u8b58\u300d\uff0c\u7562\u7adf\u5728\u5982OpenAI\u7b49\u5927\u516c\u53f8\u5728\u9032\u884cLLM\u8a13\u7df4\u7684\u6642\u5019\uff0c\u65e9\u5df2\u7d93\u9935\u904eLLM\u975e\u5e38\u5927\u91cf\u7684\u8a13\u7df4\u8cc7\u6599\uff0c\u4f7f\u5f97\u5927\u8a9e\u8a00\u6a21\u578b\u672c\u8eab\u5df2\u7d93\u5177\u6709\u4e00\u5b9a\u7684\u77e5\u8b58\u91cf\uff0c\u5728\u56de\u61c9\u554f\u984c\u4ee5\u53ca\u7d44\u7e54\u7b54\u6848\u65b9\u9762\uff0c\u4e5f\u5177\u6709\u4e00\u5b9a\u7684\u6f5b\u529b\u3002<\/p>\n

\u4e0d\u904e\uff0cYu\u7b49\u4eba (2025) \u8a8d\u70ba\uff0c\u904e\u53bb\u7684\u9019\u4e9b\u65b9\u6cd5\u4ecd\u7136\u7121\u6cd5\u5145\u5206\u767c\u63eeLLM\u7684\u80fd\u529b\uff0c\u6bd4\u65b9\u8aaa\uff0c\u5728\u9032\u884c\u5716\u8aaa\u751f\u6210\u7684\u6642\u5019\uff0cLLM\u6703\u751f\u6210\u6f2b\u7121\u76ee\u7684\u7684\u5167\u5bb9\uff0c\u8b93\u751f\u6210\u51fa\u4f86\u7684\u6587\u5b57\u7f3a\u4e4f\u610f\u7fa9\u611f\u3002\u6216\u8005\u5373\u4f7f\u4f7f\u7528\u4e86\u300c\u5c11\u6a23\u672c\u5b78\u7fd2\u300d\u4f86\u5f15\u5c0eLLM\u7522\u51fa\u6587\u5b57\uff0c\u4f46\u662f\u5be6\u969b\u4e0a\u82e5\u53bb\u9a57\u8b49\u5176\u6b63\u78ba\u6027\uff0c\u4ecd\u7136\u9084\u662f\u8ddd\u96e2\u4eba\u985e\u771f\u6b63\u60f3\u8981\u7684\u4efb\u52d9\u6548\u679c\u6709\u6bb5\u5dee\u8ddd\u3002<\/p>\n

 <\/p>\n

\u5168\u65b0\u7684\u8996\u89ba\u554f\u7b54\u65b0\u6846\u67b6\u2500Prophet<\/strong><\/span><\/h6>\n

Yu\u7b49\u4eba (2025) \u70ba\u4e86\u958b\u767c\u66f4\u597d\u7684\u8996\u89ba\u554f\u7b54\u7cfb\u7d71\uff0c\u63d0\u51fa\u4e86\u4e00\u500b\u65b0\u6846\u67b6\u300cProphet\u300d\uff0c\u53d6\u540d\u7684\u6838\u5fc3\u6982\u5ff5\u662f\u300c\u63d0\u793aLLM\u4ee5\u7b54\u6848\u555f\u767c\u9032\u884c\u77e5\u8b58\u8996\u89ba\u554f\u7b54 (pro<\/u><\/strong>mpt LLMs with answer he<\/u><\/strong>urist<\/u><\/strong>ics for knowledge-based VQA)\u300d\u3002\u5728\u9019\u500b\u65b0\u6846\u67b6\u7576\u4e2d\uff0c\u900f\u904e\u5169\u500b\u968e\u6bb5\u4f86\u5f37\u5316LLM\u7684\u63a8\u7406\u80fd\u529b\u3002<\/p>\n

\u7b2c\u4e00\u968e\u6bb5\u662f\u300c\u7b54\u6848\u555f\u767c\u751f\u6210 (Answer Heuristics Generation)\u300d\uff0c\u5148\u8a13\u7df4\u4e00\u500b\u4e0d\u4f9d\u8cf4\u5916\u90e8\u77e5\u8b58\u7684\u57fa\u672cVQA\u6a21\u578b\uff0c\u5177\u6709\u63a8\u8ad6\u529f\u80fd\uff0c\u7136\u5f8c\u5b83\u80fd\u5920\u91dd\u5c0d\u554f\u984c\uff0c\u63d0\u51fak\u500b\u53ef\u80fd\u7684\u7b54\u6848\uff0c\u7136\u5f8c\u518d\u4ef0\u8cf4\u5c11\u6a23\u672c\u5b78\u7fd2\uff0c\u8b93\u6a21\u578b\u80fd\u5920\u56e0\u6b64\u53ef\u4ee5\u5f9e\u6f5b\u5728\u7684\u7b54\u6848\u7576\u4e2d\u9078\u51fa\u4e00\u500b\u9069\u5408\u7684\u7b54\u6848\u3002<\/p>\n

\u7b2c\u4e8c\u968e\u6bb5\u5247\u662f\u300c\u5f37\u5316\u63d0\u793a\u751f\u6210 (Heuristics-Enhanced Prompting)\u300d\u91dd\u5c0d\u5927\u8a9e\u8a00\u6a21\u578b\u80cc\u5f8c\u6240\u8a2d\u8a08\u7684\u63d0\u793a\u5de5\u7a0b\u90e8\u5206\uff0c\u5728\u7cfb\u7d71\u63d0\u793a\u8a5e\u88e1\u9762\uff0c\u5305\u542b\u4e86\u63d0\u793a\u8a5e\u6a19\u984c\u3001\u4e0a\u4e0b\u6587\u6848\u4f8b\u7d44\u5408\u3001\u4ee5\u53ca\u6e2c\u8a66\u8f38\u5165\u8f38\u51fa\uff0c\u800c\u9019\u500b\u63d0\u793a\u8a5e\u6a19\u984c\u4ee5\u53ca\u5167\u5bb9\uff0c\u4f7f\u7528\u81ea\u7136\u8a9e\u8a00\u5beb\u6210\uff0c\u4e0d\u904e\u91dd\u5c0d\u6a21\u578b\u5728\u524d\u4e00\u968e\u6bb5\u6240\u751f\u6210\u7684\u5404\u500b\u5019\u9078\u7b54\u6848\uff0c\u6a21\u578b\u6703\u91dd\u5c0d\u90a3\u4e9b\u554f\u7b54\u7d44\u5408\u9032\u884c\u8a55\u4f30\uff0c\u8003\u616e\u4e0a\u4e0b\u6587\u7684\u95dc\u806f\u4e4b\u5f8c\uff0c\u4f86\u6c7a\u5b9a\u5beb\u51fa\u8981\u63a8\u7d66LLM\u4f86\u9032\u884c\u5224\u65b7\u7684\u63d0\u793a\u8a5e\u3002\u4e5f\u5c31\u662f\u8aaa\uff0c\u5b83\u6703\u5728N\u7d44\u53ef\u80fd\u7684\u6f5b\u5728\u5019\u9078\u7b54\u6848\u4e2d\uff0c\u5beb\u51faT\u7d44\u53ef\u80fd\u7684\u63d0\u793a\u8a5e\uff0c\u6240\u4ee5\u7e3d\u5171\u6703\u6709\u975e\u5e38\u591a\u7a2e\u53ef\u80fd\u6027\u8b93LLM\u9032\u884c\u6700\u7d42\u7b54\u6848\u7684\u6587\u5b57\u751f\u6210\u3002\u7528\u9019\u6a23\u7684\u65b9\u6cd5\uff0c\u53ef\u4ee5\u66f4\u52a0\u5f37KB\u8996\u89ba\u554f\u7b54\u7684\u7a69\u5b9a\u6027\u548c\u8868\u73fe\u3002<\/p>\n

\u800c\u9664\u4e86\u4ee5\u5716\u50cf\u4ee5\u53ca\u8a9e\u8a00\u6587\u5b57\u70ba\u57fa\u790e\u7684Prophet\u6846\u67b6\u4e4b\u5916\uff0cYu\u7b49\u4eba (2025) \u4e5f\u63d0\u51fa\u4e86\u591a\u6a21\u614b\u6a21\u578b\u7684\u6846\u67b6\u300cProphet++\u300d\uff0c\u52a0\u5165\u4e86\u5176\u4ed6\u5927\u578b\u591a\u6a21\u614b\u6a21\u578b (Large Multimodal Models, LMMs)\uff0c\u4f8b\u5982GPT4o\u3001LLaVa\u7b49\uff0c\u8b93\u6a21\u578b\u9664\u4e86\u6709\u539f\u672cVQA\u6a21\u578b\u7684\u529f\u80fd\u4e4b\u5916\uff0c\u518d\u591a\u4e86\u53ef\u4ee5\u5c07\u591a\u6a21\u614b\u8cc7\u6599\u4e5f\u8f49\u6210\u5716\u8aaa\u7684\u5f62\u5f0f\uff0c\u6210\u70ba\u6a21\u578b\u751f\u6210\u7b54\u6848\u7684\u7279\u5fb5\u91cf\u4e4b\u4e00\u3002\u4e5f\u5c31\u662f\u66f4\u80fd\u5168\u9762\u7684\u7406\u89e3\u554f\u984c\u7684\u8a9e\u5883\u548c\u8996\u89ba\u5167\u5bb9\uff0c\u6574\u5408\u6027\u66f4\u9ad8\u3002<\/p>\n

 <\/p>\n

\u8b93\u96fb\u8166\u300c\u770b\u5716\u8aaa\u6545\u4e8b\u300d\uff0c\u8d85\u8d8a\u73fe\u6709\u65b9\u6cd5\u7684\u65b0\u6846\u67b6<\/strong><\/span><\/h6>\n

Yu\u7b49\u4eba (2025) \u7684\u7814\u7a76\u9054\u6210\u4e86\u975e\u5e38\u597d\u7684\u6a21\u578b\u8868\u73fe\uff0c\u4f5c\u8005\u5728\u56db\u500b\u4e0d\u540c\u7684\u77e5\u8b58\u578bVQA\u8cc7\u6599\u96c6\u4e0a\u9762\u505a\u6e2c\u8a66\uff0c\u5305\u542bOK-VQA\u3001A-OKVQA\u3001ScienceQA\u7684IMG\u5b50\u8cc7\u6599\u96c6\u4ee5\u53caTextVQA\u3002\u6700\u5f8c\u7684\u7d50\u679c\u986f\u793aProphet\u6846\u67b6\u5728\u6240\u6709\u8cc7\u6599\u96c6\u4e0a\u5747\u8d85\u8d8a\u4e86\u73fe\u6709\u7684\u6700\u4f73\u65b9\u6cd5\uff0c\u6b63\u78ba\u7387\u9054\u5230\u4e866\u6210\uff0c\u751a\u81f3\u53ef\u4ee5\u8d85\u8d8a\uff0c\u6bd4\u8d77\u5176\u4ed6\u6a21\u578b\u53ef\u80fd\u53ea\u67094\u6210\u7684\u6b63\u78ba\u7387\u4f86\u8aaa\uff0c\u63d0\u5347\u4e86\u76f8\u7576\u591a\u7684\u6b63\u78ba\u7387\u8868\u73fe\u3002\u800c\u4e14\u82e5\u662fVQA\u7684\u554f\u7b54\u662f\u9078\u64c7\u984c\u5f0f\u7684\u554f\u7b54\uff0cProphet\u7684\u6b63\u78ba\u7387\u751a\u81f3\u80fd\u8d85\u8d8a8\u6210\uff0c\u4e5f\u662f\u8d85\u8d8a\u4e86\u5176\u4ed6\u65b9\u6cd5\u53ea\u67094~5\u6210\u6b63\u78ba\u7387\u7684\u8868\u73fe\u3002\u6700\u51fa\u8272\u7684\u8868\u73fe\uff0c\u5247\u662f\u51fa\u73fe\u5728ScienceQA\u7684IMG\u5b50\u8cc7\u6599\u96c6\u90e8\u5206\uff0cProphet++\u9019\u500b\u591a\u6a21\u614b\u6a21\u578b\uff0c\u53ef\u4ee5\u8b93\u6b63\u78ba\u7387\u9054\u52309\u6210\u3002<\/p>\n

\u800c\u80fd\u5920\u64c1\u6709\u5f88\u597d\u7684\u6a21\u578b\u8868\u73fe\uff0c\u4e5f\u4f86\u81ea\u65bc\u8a72\u7814\u7a76\u5718\u968a\u5df2\u7d93\u6e2c\u8a66VQA\u7684\u65b0\u67b6\u69cb\u5f88\u9577\u7684\u4e00\u6bb5\u6642\u9593\uff0c\u4ed6\u5011\u8a8d\u70ba\uff0c\u9019\u500b\u6846\u67b6\u5176\u5be6\u4e5f\u544a\u8a34\u4e86\u7814\u7a76\u8005\u8207\u958b\u767c\u8005\uff0c\u518d\u9032\u884c\u63d0\u793a\u8a5e\u8a2d\u8a08\u7684\u6642\u5019\uff0c\u63d0\u4f9b\u7d66LLM\u5404\u7a2e\u53ef\u80fd\u7684\u7b54\u6848\u5019\u9078\uff0c\u4ee5\u7b54\u6848\u4f86\u9032\u884c\u555f\u767c\uff0c\u5176\u5be6\u4e5f\u80fd\u6709\u6548\u7684\u5e6b\u52a9LLM\u9078\u51fa\u6b63\u78ba\u7684\u7b54\u6848\u3002<\/p>\n

\u800cProphet\u9019\u500b\u6846\u67b6\uff0c\u6574\u5408\u4e86\u73fe\u6709\u7684VQA\u6a21\u578b\u3001LLM\uff0c\u751a\u81f3\u662fLMM\uff0c\u5c0d\u65bc\u672a\u4f86\u8981\u958b\u767c\u66f4\u5177\u6709\u5e6b\u52a9\u7684AI\u61c9\u7528\u5de5\u5177\u800c\u8a00\uff0c\u5177\u6709\u76f8\u7576\u7684\u52a9\u76ca\u3002\u6b63\u5982\u6211\u5011\u4eba\u985e\u5728\u9032\u884c\u89e3\u984c\u6642\uff0c\u5e38\u5e38\u80fd\u5920\u904e\u7b54\u6848\u5f97\u5230\u67d0\u4e9b\u7dda\u7d22\u548c\u555f\u767c\uff0cAI\u4e5f\u540c\u6a23\u5982\u6b64\uff0c\u4e0d\u904e\uff0c\u91dd\u5c0d\u67d0\u4e9b\u8cc7\u6599\u5eab\uff0cAI\u7684\u6b63\u78ba\u7387\u4ecd\u7136\u9084\u4e0d\u8db3\u4ee5\u771f\u6b63\u5b8c\u7f8e\u5230\u516b\u4e5d\u6210\u4ee5\u4e0a\uff0c\u79d1\u5b78\u5bb6\u5011\u4ecd\u7136\u6709\u8a31\u591a\u7a7a\u9593\uff0c\u53ef\u4ee5\u518d\u6301\u7e8c\u52a0\u5f37\u8a13\u7df4\u66f4\u597d\u7684\u6a21\u578b\u3002<\/p>\n

 <\/p>\n

\n

\u53c3\u8003\u6587\u737b<\/strong><\/p>\n

    \n
  1. Z. Yu, X. Ouyang, Z. Shao, M. Wang and J. Yu, \"Prophet: Prompting Large Language Models With Complementary Answer Heuristics for Knowledge-Based Visual Question Answering,\" in\u00a0IEEE Transactions on Pattern Analysis and Machine Intelligence<\/em>, vol. 47, no. 8, pp. 6797-6808, Aug. 2025, doi: 10.1109\/TPAMI.2025.3562422.<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

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