{"id":39169,"date":"2025-03-03T23:01:43","date_gmt":"2025-03-03T15:01:43","guid":{"rendered":"https:\/\/17aitech.com\/?p=39169"},"modified":"2025-03-03T23:01:43","modified_gmt":"2025-03-03T15:01:43","slug":"pvuw%e8%a7%86%e9%a2%91%e5%88%86%e5%89%b2workshopcvpr-2025-%e5%be%81%e7%a8%bf%ef%bc%81%e6%af%94%e8%b5%9b%ef%bc%81","status":"publish","type":"post","link":"https:\/\/17aitech.com\/?p=39169","title":{"rendered":"PVUW\u89c6\u9891\u5206\u5272Workshop@CVPR 2025 | \u5f81\u7a3f\uff01\u6bd4\u8d5b\uff01"},"content":{"rendered":"

\u6587\u7ae0\u6765\u6e90\u4e8e\u4e92\u8054\u7f51:PVUW\u89c6\u9891\u5206\u5272Workshop@CVPR 2025 | \u5f81\u7a3f\uff01\u6bd4\u8d5b\uff01<\/a><\/p>\n

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

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

\u7b2c\u56db\u5c4a\u771f\u5b9e\u4e16\u754c<\/strong>\u4e0b\u7684<\/strong>\u50cf\u7d20\u7ea7\u89c6\u9891\u7406\u89e3\u6311\u6218\u8d5b\uff08<\/strong>The 4th PVUW challenge<\/strong>\uff09<\/strong><\/p>\n

\u4e3b\u9875<\/strong>\/Call for Paper<\/strong>\uff1a<\/strong>https:\/\/pvuw.github.io\/<\/u><\/strong><\/a><\/p>\n

\u8d5b\u90531\uff1a\u590d\u6742\u573a\u666f\u89c6\u9891\u76ee\u6807\u5206\u5272\u6311\u6218\u8d5b\uff08MOSE<\/strong>\u00a0Challenge<\/strong>\uff09\uff1a<\/strong><\/p>\n

\u53c2\u8d5b\u3001\u6570\u636e\u96c6\u4e0b\u8f7d\uff1ahttps:\/\/codalab.lisn.upsaclay.fr\/competitions\/21948<\/u><\/a><\/p>\n

\u8d5b\u90532\uff1a\u57fa\u4e8e\u52a8\u4f5c\u63cf\u8ff0\u7684\u6307\u5411\u6027\u89c6\u9891\u5206\u5272\u6311\u6218\u8d5b\uff08MeViS<\/strong>\u00a0Challenge<\/strong>\uff09\uff1a<\/strong><\/p>\n

\u53c2\u8d5b\u3001\u6570\u636e\u96c6\u4e0b\u8f7d\uff1ahttps:\/\/codalab.lisn.upsaclay.fr\/competitions\/21944<\/u><\/a><\/p>\n

\u5927\u8d5b\u7b80<\/strong>\u4ecb<\/strong><\/p>\n

\u7b2c\u56db\u5c4a\u00a0\u771f\u5b9e\u4e16\u754c\u4e0b\u7684\u50cf\u7d20\u7ea7\u89c6\u9891\u7406\u89e3\uff08Pixel-level Video Understanding in the Wild, PVUW\uff09\u6311\u6218\u8d5b\u5c06\u4e8e CVPR 2025 \u671f\u95f4\u5728\u7f8e\u56fd\u7530\u7eb3\u897f\u5dde\u7eb3\u4ec0\u7ef4\u5c14\u7684 Music City Center \u4e3e\u529e\u3002\u50cf\u7d20\u7ea7\u573a\u666f\u7406\u89e3\u662f\u8ba1\u7b97\u673a\u89c6\u89c9\u4e2d\u7684\u6838\u5fc3\u95ee\u9898\u4e4b\u4e00\uff0c\u65e8\u5728\u8bc6\u522b\u56fe\u50cf\u4e2d\u6bcf\u4e2a\u50cf\u7d20\u7684\u7c7b\u522b\u3001\u63a9\u7801\u548c\u8bed\u4e49\u3002\u7136\u800c\uff0c\u73b0\u5b9e\u4e16\u754c\u662f\u52a8\u6001\u7684\uff0c\u57fa\u4e8e\u89c6\u9891\u7684\uff0c\u800c\u975e\u9759\u6001\u7684\u56fe\u50cf\u72b6\u6001\uff0c\u56e0\u6b64\u5b66\u4e60\u8fdb\u884c\u89c6\u9891\u5206\u5272\u5bf9\u4e8e\u5b9e\u9645\u5e94\u7528\u6765\u8bf4\u66f4\u4e3a\u5408\u7406\u548c\u5b9e\u7528\u3002\u4e3a\u4e86\u63a8\u52a8\u4ece\u56fe\u50cf\u5206\u5272\u5411\u89c6\u9891\u5206\u5272\u7684\u5347\u7ea7\uff0c\u672c\u6b21 workshop \u5c06\u53d1\u5e03\u65b0<\/strong>\u7684<\/strong>\u6570\u636e\u96c6\u548c\u7ade\u8d5b<\/strong>\uff0c\u76ee\u6807\u662f\u5728\u5145\u6ee1\u6311\u6218\u4f46\u6781\u5177\u5e94\u7528\u4ef7\u503c\u7684\u5f00\u653e\u4e16\u754c\u573a\u666f\u4e0b\u5b9e\u73b0\u50cf\u7d20\u7ea7\u89c6\u9891\u7406\u89e3\u3002<\/p>\n

\u672c\u6b21\u6311\u6218\u8d5b\u4e2d\u8bbe\u7f6e\u4e86\u4e24\u4e2a\u8d5b\u9053\uff1a\u590d\u6742\u573a\u666f\u89c6\u9891\u76ee\u6807\u5206\u5272\u8d5b\u9053\uff08MOSE Track\uff09<\/strong>\u548c\u57fa\u4e8e\u52a8\u4f5c\u63cf\u8ff0\u7684\u6307\u5411\u6027\u89c6\u9891\u5206\u5272\u6311\u6218\u8d5b\uff08MeViS Track\uff09<\/strong>\u3002\u5728\u4e24\u4e2a\u6311\u6218\u8d5b\u4e2d\uff0c\u4e3b\u529e\u65b9\u91c7\u96c6\u4e86\u76ee\u524d\u6700\u5927\u89c4\u6a21\u7684\u590d\u6742\u573a\u666f\u89c6\u9891\u76ee\u6807\u5206\u5272\u6570\u636e\u96c6Complex Video Object Segmentation (MOSE) [1] \u548c\u57fa\u4e8e\u52a8\u4f5c\u63cf\u8ff0\u7684\u89c6\u9891\u5206\u5272Motion Expression Video Segmentation (MeViS) [2]\u3002\u4e24\u4e2a\u6570\u636e\u96c6\u5747\u9488\u5bf9\u4e24\u4e2a\u4efb\u52a1\u4e2d\u4e9f\u9700\u89e3\u51b3\u7684\u95ee\u9898\uff0c\u5305\u542b\u5927\u91cf\u7684\u9ad8\u96be\u5ea6\u4e14\u8d34\u8fd1\u73b0\u5b9e\u7684\u6837\u672c\uff0c\u5bf9\u89c6\u9891\u7406\u89e3\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u63d0\u51fa\u6311\u6218\u3002<\/p>\n

\u5728\u6311\u6218\u8d5b\u7684\u540c\u65f6\uff0c\u672c\u6b21workshop\u4e5f\u5c06\u6db5\u76d6\u4ee5\u4e0b\u7814\u7a76\u4e3b\u9898\uff0c\u5e76\u6b22\u8fce\u76f8\u5173\u8bba\u6587\u6295\u7a3f<\/u><\/strong>\uff1a<\/p>\n

\u00b7\u56fe\u50cf\/\u89c6\u9891\u7684\u8bed\u4e49\u4e0e\u5168\u666f\u5206\u5272<\/p>\n

\u00b7\u4ea4\u4e92\u5f0f\u56fe\u50cf\/\u89c6\u9891\u7406\u89e3\u4e0e\u5206\u5272\uff08\u5982\u57fa\u4e8e\u6587\u672c\u3001\u70b9\u51fb\u7b49\uff09<\/p>\n

\u00b7\u89c6\u9891\u5bf9\u8c61\/\u5b9e\u4f8b\u5206\u5272<\/p>\n

\u00b7\u590d\u6742\u73af\u5883\u4e0b\u7684\u89c6\u9891\u7406\u89e3<\/p>\n

\u00b7\u8bed\u8a00\u5f15\u5bfc\u7684\u89c6\u9891\u7406\u89e3<\/p>\n

\u00b7\u97f3\u9891\u5f15\u5bfc\u7684\u89c6\u9891\u5206\u5272<\/p>\n

\u00b7\u89c6\u9891\u573a\u666f\u89e3\u6790\u7684\u9ad8\u6548\u8ba1\u7b97\u65b9\u6cd5<\/p>\n

\u00b7\u89c6\u9891\u4e2d\u7684\u534a\u76d1\u7763\u8bc6\u522b<\/p>\n

\u00b7\u8bc4\u4f30\u89c6\u9891\u573a\u666f\u89e3\u6790\u8d28\u91cf\u7684\u65b0\u6307\u6807<\/p>\n

\u00b7\u771f\u5b9e\u4e16\u754c\u7684\u89c6\u9891\u5e94\u7528\uff08\u5982\u81ea\u52a8\u9a7e\u9a76\u3001\u5ba4\u5185\u673a\u5668\u4eba\u3001\u89c6\u89c9\u5bfc\u822a\u7b49\uff09<\/p>\n

\u76ee\u524d\uff0c\u4e24\u4e2a\u6311\u6218\u8d5b\u7684\u6570\u636e\u96c6\u5747\u5df2\u5f00\u653e\u4e0b\u8f7d\uff0c\u6d4b\u8bd5\u63d0\u4ea4\u5165\u53e3\u4e5f\u5df2\u5f00\u653e\uff0c\u6b22\u8fce\u53c2\u8d5b\u8005\u81ea\u7531\u6d4b\u8bd5\u5404\u79cd\u65b9\u6cd5\uff01<\/u><\/strong><\/p>\n

\u8d5b\u90531\uff1a\u590d\u6742\u573a\u666f\u89c6\u9891\u76ee\u6807\u5206\u5272\uff08MOSE\uff09<\/strong><\/p>\n

MOSE\uff08\u590d\u6742\u573a\u666f\u89c6\u9891\u76ee\u6807\u5206\u5272\uff09\u805a\u7126\u4e8e\u590d\u6742\u73af\u5883\u4e0b\u7684\u89c6\u9891\u76ee\u6807\u5206\u5272\u95ee\u9898\uff0c\u65e8\u5728\u63a8\u52a8\u8fd9\u4e00\u9886\u57df\u7684\u7814\u7a76\u4e0e\u5e94\u7528\u3002\u672c\u6b21\u5927\u805a\u7126\u4e8e\u914d\u5957\u7684MOSE\u6570\u636e\u96c6\u00a0[1]\u00a0\u3002\u8be5\u5927\u89c4\u6a21\u6570\u636e\u96c6\u5305\u542b2149\u4e2a\u89c6\u9891\u7247\u6bb5\u548c5200\u4e2a\u76ee\u6807\uff0c\u5171\u6709431,725\u4e2a\u9ad8\u8d28\u91cf\u7684\u76ee\u6807\u5206\u5272\u63a9\u7801\u3002\u89c6\u9891\u5206\u8fa8\u7387\u4e3a1920\u00d71080\uff0c\u89c6\u9891\u957f\u5ea6\u4ece5\u79d2\u523060\u79d2\u4e0d\u7b49\u3002MOSE\u6570\u636e\u96c6\u7684\u663e\u8457\u7279\u70b9\u662f\u5176\u590d\u6742\u7684\u573a\u666f\uff0c\u5305\u62ec\u76ee\u6807\u7684\u6d88\u5931\u548c\u91cd\u73b0\u3001\u4e0d\u663e\u773c\u7684\u5c0f\u7269\u4f53\u3001\u4e25\u91cd\u7684\u906e\u6321\u4ee5\u53ca\u62e5\u6324\u7684\u73af\u5883\u7b49\u3002\u76f8\u6bd4\u4e8e\u4e0a\u4e00\u5c4a\u6bd4\u8d5b\uff0c\u672c\u5c4a\u6bd4\u8d5b\u7684\u6d4b\u8bd5\u6570\u636e\u4e2d\u5c06\u4f1a\u6709\u5168\u65b0\u573a\u666f\u52a0\u5165\uff0c\u63d0\u4f9b\u66f4\u52a0\u4e30\u5bcc\u3001\u66f4\u52a0\u6709\u6311\u6218\u6027\u7684\u6570\u636e<\/strong>\u3002<\/p>\n

\u6570\u636e\u96c6\u4e3b\u9875\uff1ahttps:\/\/henghuiding.github.io\/MOSE\/<\/u><\/a><\/p>\n

\u53c2\u8d5b\u7f51\u7ad9\uff1ahttps:\/\/codalab.lisn.upsaclay.fr\/competitions\/21948<\/u><\/a><\/p>\n

\u6570\u636e\u96c6\u4e0b\u8f7d\uff1ahttps:\/\/codalab.lisn.upsaclay.fr\/competitions\/21948#participate<\/u><\/a><\/p>\n

\u6570\u636e\u96c6api\u4ee3\u7801\uff1ahttps:\/\/github.com\/henghuiding\/MOSE-api<\/u><\/a><\/p>\n

\u8d5b\u90532\uff1a\u57fa\u4e8e\u52a8\u4f5c\u63cf\u8ff0\u7684\u6307\u5411\u6027\u89c6\u9891\u5206\u5272\uff08MeViS\uff09<\/strong><\/p>\n

MeViS\uff08\u57fa\u4e8e\u52a8\u4f5c\u63cf\u8ff0\u7684\u6307\u5411\u6027\u89c6\u9891\u5206\u5272\uff09\u8d5b\u9053\u4e5f\u5728CVPR2025\u4e0a\u540c\u671f\u4e3e\u884c\u3002\u672c\u6b21\u6311\u6218\u8d5b\u7684\u91cd\u70b9\u662f\u57fa\u4e8e\u52a8\u4f5c\u63cf\u8ff0\u7684\u6307\u5411\u6027\u89c6\u9891\u5206\u5272\uff0c\u5373\u6839\u636e\u63cf\u8ff0\u7269\u4f53\u8fd0\u52a8\u7684\u53e5\u5b50\u6765\u8bc6\u522b\u5e76\u5206\u5272\u89c6\u9891\u5185\u5bb9\u4e2d\u7684\u76ee\u6807\u7269\u4f53\u3002MeViS\u6570\u636e\u96c6 [2] \u662f\u6b64\u6b21\u6bd4\u8d5b\u7684\u91cd\u8981\u7ec4\u6210\u90e8\u5206\uff0c\u5305\u542b2006\u4e2a\u89c6\u9891\u7247\u6bb5\u548c443,000\u4e2a\u9ad8\u8d28\u91cf\u7684\u76ee\u6807\u5206\u5272\u63a9\u7801\uff0c\u5171\u670928,570\u53e5\u63cf\u8ff08,171\u4e2a\u590d\u6742\u73af\u5883\u4e0b\u7269\u4f53\u8fd0\u52a8\u7684\u53e5\u5b50\u3002\u5728\u672c\u5c4a\u8d5b\u4e8b\u4e2d\uff0cMeViS\u8d5b\u9053\u4e5f\u4f1a\u52a0\u5165\u66f4\u52a0\u6709\u6311\u6218\u6027\u7684\u5168\u65b0\u7684\u573a\u666f\u548c\u89c6\u9891\u7c7b\u522b\u3002<\/strong><\/p>\n

\u6570\u636e\u96c6\u4e3b\u9875\uff1ahttps:\/\/henghuiding.github.io\/M<\/u>eViS<\/u>\/<\/u><\/a><\/p>\n

\u53c2\u8d5b\u7f51\u7ad9\uff1ahttps:\/\/codalab.lisn.upsaclay.fr\/competitions\/21944<\/u><\/a><\/p>\n

\u6570\u636e\u96c6\u4e0b\u8f7d\uff1ahttps:\/\/codalab.lisn.upsaclay.fr\/competitions\/21944#participate<\/u><\/a><\/p>\n

Baseline \u4ee3\u7801\uff1ahttps:\/\/github.com\/henghuiding\/MeViS<\/u><\/a><\/p>\n

\u53c2\u8d5b\u4fe1\u606f<\/strong><\/p>\n

\u4e24\u4e2a\u6311\u6218\u8d5b\u5747\u5728\u5e73\u53f0CodaLab\u4e0a\u4e3e\u529e\u3002\u53c2\u8d5b\u8005\u53ef\u4ee5\u4e0b\u8f7d\u6570\u636e\u96c6\u81f3\u672c\u5730\uff0c\u8bad\u7ec3\u6d4b\u8bd5\u540e\u5c06\u7ed3\u679c\u63d0\u4ea4\u81f3\u8d5b\u4e8b\u5e73\u53f0\u8fdb\u884c\u8bc4\u4f30\u3002\u76ee\u524d\uff0c\u4e24\u4e2a\u6311\u6218\u8d5b\u7684\u6570\u636e\u96c6\u5747\u5df2\u5f00\u653e\u4e0b\u8f7d\uff0c\u9a8c\u8bc1\u96c6\u63d0\u4ea4\u5165\u53e3\u4e5f\u5df2\u5f00\u653e\uff0c\u6b22\u8fce\u4e2a\u4eba\u548c\u56e2\u961f\u81ea\u7531\u6d4b\u8bd5\u5404\u79cd\u65b9\u6cd5\u3002<\/p>\n

\u6d4b\u8bd5\u96c6\u63d0\u4ea4\u5165\u53e3\u5f00\u653e\u65f6\u95f4\u4e3a3\u670815\u65e5\u81f33\u670825\u65e5\u3002\u6700\u7ec8\u6bd4\u8d5b\u7ed3\u679c\u5c06\u4e8e3\u670827\u65e5\u516c\u5e03\uff0c\u4e24\u4e2a\u6311\u6218\u8d5b\u7684\u51a0\u519b\u90fd\u5c06\u4f1a\u88ab\u9080\u8bf7\u5728CVPR 2025Workshop\u4e0a\u5206\u4eab\u5c55\u793a\u3002<\/p>\n

Workshop\u8bba\u6587\u63d0\u4ea4\u5165\u53e3\u5373\u5c06\u5f00\u653e\uff0c\u8bf7\u5173\u6ce8\u5b98\u65b9\u7f51\u7ad9\u83b7\u53d6\u5730\u5740https:\/\/pvuw.github.io\/<\/u><\/a>\u3002<\/p>\n

[1] MOSE: A New Dataset for Video Object Segmentation in Complex Scenes. ICCV 2023<\/p>\n

[2] MeViS: A Large-scale Benchmark for Video Segmentation with Motion Expressions. ICCV 2023<\/p>\n

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