{"id":1180,"date":"2024-05-09T00:00:48","date_gmt":"2024-05-09T00:00:48","guid":{"rendered":"https:\/\/www.gdsig.cn\/?gdsig=283"},"modified":"2024-05-09T00:00:48","modified_gmt":"2024-05-09T00:00:48","slug":"post-283","status":"publish","type":"post","link":"https:\/\/test.gdsig.cn\/index.php\/2024\/05\/09\/post-283\/","title":{"rendered":"CSIG-\u5e7f\u4e1c\u7701CVPR2024 \u8bba\u6587\u5206\u4eab\u5b66\u672f\u62a5\u544a\u4f1a\u5706\u6ee1\u843d\u5e55"},"content":{"rendered":"

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<\/span>CSIG-<\/span>\u5e7f\u4e1c\u7701<\/span>CVPR2024<\/span>\u8bba\u6587\u5206\u4eab\u5b66\u672f\u62a5\u544a\u4f1a\u4e8e<\/span>2024<\/span>\u5e74<\/span>4<\/span>\u6708<\/span>27<\/span>\u65e5\u81f3<\/span>28<\/span>\u65e5\u6210\u529f\u53ec\u5f00\uff0c\u672c\u6b21\u5b66\u672f\u62a5\u544a\u4f1a\u7531\u5e7f\u4e1c\u7701\u56fe\u8c61\u56fe\u5f62\u5b66\u4f1a\uff08<\/span>GDSIG<\/span>\uff09\u4e3b\u529e\uff0c\u7531\u534e\u5357\u7406\u5de5\u5927\u5b66\u7535\u5b50\u4e0e\u4fe1\u606f\u5b66\u9662\u3001\u4e2d\u5c71\u5927\u5b66\u8ba1\u7b97\u673a\u5b66\u9662\u3001\u6df1\u5733\u5927\u5b66\u7535\u5b50\u4e0e\u4fe1\u606f\u5de5\u7a0b\u5b66\u9662\u3001\u5357\u65b9\u79d1\u6280\u5927\u5b66\u8ba1\u7b97\u673a\u79d1\u5b66\u4e0e\u5de5\u7a0b\u7cfb\u3001<\/span>GDSIG<\/span>\u8ba1\u7b97\u673a\u89c6\u89c9\u4e13\u59d4\u4f1a\u627f\u529e\uff0c\u7531<\/span>CSIG<\/span>\u5e7f\u5dde\u4f1a\u5458\u6d3b\u52a8\u4e2d\u5fc3\u3001<\/span>CSIG-<\/span>\u6587\u6863\u56fe\u50cf\u5206\u6790\u4e0e\u8bc6\u522b\u4e13\u59d4\u4f1a\u3001<\/span>CSIG-<\/span>\u673a\u5668\u89c6\u89c9\u4e13\u59d4\u4f1a\u534f\u529e\u3002<\/span>29<\/span>\u4f4d\u56fe\u50cf\u56fe\u5f62\u9886\u57df\u7684\u4f18\u79c0\u9752\u5e74\u5b66\u5b50\u4ecb\u7ecd\u4e86\u4ed6\u4eec<\/span>CVPR 2024<\/span>\u5f55\u7528\u8bba\u6587\u7684\u6700\u65b0\u7814\u7a76\u6210\u679c\uff0c\u62a5\u544a\u4f1a\u5728<\/span>B<\/span>\u7ad9\u3001\u853b\u4eab\u5b66\u672f\u548c\u4e2d\u56fd\u56fe\u8c61\u56fe\u5f62\u5b66\u4f1a\u89c6\u9891\u53f7\u4e09\u4e2a\u5e73\u53f0\u8fdb\u884c\u4e86\u540c\u6b65\u76f4\u64ad\u3002\u6765\u81ea\u5168\u56fd\u5404\u5730\u7684\u542c\u4f17\u8d85\u8fc7<\/span>9000<\/span>\u4eba\u6b21\u5728\u7ebf\u53c2\u52a0\u4e86\u672c\u6b21\u5b66\u672f\u62a5\u544a\u4f1a\u3002<\/span><\/p>\n


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CSIG\u526f\u7406\u4e8b\u957f\u3001CSIG\u5e7f\u5dde\u4f1a\u5458\u6d3b\u52a8\u4e2d\u5fc3\u4e3b\u4efb\u8d56\u5251\u714c\u6559\u6388\u81f4\u5f00\u5e55\u8f9e<\/span><\/p>\n

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\u4f1a\u8bae\u7531\u4e2d\u56fd\u56fe\u8c61\u56fe\u5f62\u5b66\u4f1a<\/span>(<\/span>CSIG)<\/span>\u526f\u7406\u4e8b\u957f\u3001<\/span>CSIG\u5e7f\u5dde\u4f1a\u5458\u6d3b\u52a8\u4e2d\u5fc3\u7684\u4e3b\u5e2d\u3001\u4e2d\u5c71\u5927\u5b66\u8d56\u5251\u714c\u6559\u6388\u81f4\u5f00\u5e55\u8bcd\u3002\u8d56\u6559\u6388\u6307\u51fa\uff0cCVPR\u662f\u8ba1\u7b97\u673a\u89c6\u89c9\u548c\u6a21\u5f0f\u8bc6\u522b\u7684\u9876\u7ea7\u5b66\u672f\u4f1a\u8bae\uff0c\u591a\u9886\u57df\u5185\u5bcc\u6709\u5f71\u54cd\u7684\u539f\u521b\u65b9\u6cd5\u90fd\u662f\u6765\u81ea\u4e8e\u8fd9\u4e2a\u4f1a\u8bae\uff0c\u4eca\u5e74\u4f1a\u8bae\u7684\u5f55\u7528\u7387\u4e3a23.6%\uff0c<\/span>11532\u4efd\u6709\u6548\u8bba\u6587\uff0c2719\u7bc7\u8bba\u6587\u88ab\u63a5\u6536<\/span>\uff0c\u76f8\u8f83\u4e8e\u53bb\u5e74\u5f55\u7528\u96be\u5ea6\u66f4\u5927\uff0c\u80fd\u591f\u5f55\u7528\u975e\u5e38\u4e0d\u5bb9\u6613\u3002\u672c\u6b21\u4f1a\u8bae\u9080\u8bf7\u4e86<\/span>29\u4f4d\u5e7f\u4e1c\u7701\u6b64\u9886\u57df\u90e8\u5206\u4f18\u79c0\u56e2\u961f\u7684\u9752\u5e74\u5b66\u5b50\uff0c\u4ecb\u7ecd\u4ed6\u4eec\u4eca\u5e74CVPR2024\u5f55\u7528\u8bba\u6587\u7684\u6700\u65b0\u7814\u7a76\u6210\u679c\u3002<\/span><\/span><\/p>\n


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29\u4f4d\u9752\u5e74\u5b66\u5b50\u5728\u4f1a\u8bae\u671f\u95f4\u5206\u4eab\u4e86\u5404\u81ea\u7684\u7814\u7a76\u6210\u679c\uff0c\u8fd9\u4e9b\u7814\u7a76\u6210\u679c\u4ee3\u8868\u6027\u5730\u5c55\u793a\u4e86\u5e7f\u4e1c\u5b66\u8005\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u6a21\u5f0f\u8bc6\u522b\u9886\u57df\u7684\u5b66\u672f\u98ce\u91c7\u548c\u7814\u7a76\u6c34\u5e73\u3002<\/span><\/span><\/p>\n

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\u5e7f\u4e1c\u7701\u56fe\u8c61\u56fe\u5f62\u5b66\u4f1a\u7406\u4e8b\u957f\u3001CSIG\u6587\u6863\u56fe\u50cf\u5206\u6790\u4e0e\u8bc6\u522b\u4e13\u59d4\u4f1a\u91d1\u8fde\u6587\u6559\u6388\u81f4\u95ed\u5e55\u8f9e<\/span><\/p>\n

\u00a0<\/span><\/p>\n

\u6700\u540e\uff0c\u5927\u4f1a\u5728\u4e2d\u56fd\u56fe\u8c61\u56fe\u5f62\u5b66\u5b66\u4f1a\u5e38\u52a1\u7406\u4e8b\u3001\u5e7f\u4e1c\u7701\u56fe\u8c61\u56fe\u5f62\u5b66\u4f1a\u7406\u4e8b\u957f\u3001<\/span>CSIG\u6587\u6863\u56fe\u50cf\u5206\u6790\u4e0e\u8bc6\u522b\u4e13\u59d4\u4f1a\u4e3b\u4efb\u3001\u534e\u5357\u7406\u5de5\u5927\u5b66\u91d1\u8fde\u6587\u6559\u6388\u7684\u81f4\u8f9e\u4e2d\u95ed\u5e55\u3002\u91d1\u8001\u5e08\u9996\u5148\u611f\u8c22\u4e86\u6240\u6709\u7ebf\u4e0a\u53c2\u52a0\u672c\u6b21\u5e7f\u4e1c\u7701CVPR\u8bba\u6587\u4ea4\u6d41\u7684\u62a5\u544a\u4f1a\u7684\u8001\u5e08\u548c\u540c\u5b66\uff0c\u611f\u8c22\u5927\u5bb6\u5173\u6ce8CSIG\u548c<\/span>\u5e7f\u4e1c\u7701\u56fe\u8c61\u56fe\u5f62\u5b66\u4f1a<\/span>\u8054\u5408\u4e3b\u529e\u7684\u8fd9\u4e00\u6b21\u5b66\u672f\u4ea4\u6d41\u6d3b\u52a8\u3002\u91d1\u8001\u5e08\u603b\u7ed3\u5230\uff0c\u4eca\u5e74\u7684\u5206\u4eab\u7684\u8bba\u6587\u6210\u679c\u5206\u4e3a\u4e09\u4e2a\u5927\u7684\u7c7b\u522b\uff0c\u6db5\u76d6\u4e86\u50cf\u8bc6\u522b\u4e0e\u5206\u5272\u5185\u5bb9\u751f\u6210\u56fe\u50cf\uff0c\u6062\u590d\u8868\u5f81\u5b66\u4e60\u7b49\u5f88\u591a\u9886\u57df\uff0c\u5e76\u4f53\u73b0\u4e86\u8fd9\u4e9b\u9886\u57df\u7684\u6700\u65b0\u8fdb\u5c55\u3002\u8fdb\u4e00\u6b65\uff0c\u91d1\u8001\u5e08\u4ecb\u7ecd\u4e86\u5e7f\u4e1c\u7701<\/span>CVPR\u8bba\u6587\u4ea4\u6d41\u6d3b\u52a8\u7684\u5f80\u5e74\u60c5\u51b5\uff0c\u4ece2020\u5e74\u7b2c\u4e00\u5c4a\u5c0f\u89c4\u6a2112\u7bc7\u8bba\u6587\u4ea4\u6d41\uff0c\u5230\u4eca\u5e742024\u5e74\u662f\u7b2c\u4e94\u5c4a\u8fbe\u5230\u4e8629\u7bc7\uff0c\u4ecd\u7136\u6709\u5f88\u591a\u62a5\u540d\u53c2\u52a0\u4ea4\u6d41\u7684\u540c\u5b66\u65e0\u6cd5\u5b89\u6392\uff0c\u663e\u793a\u5e7f\u4e1c\u7701\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\u7684\u6301\u7eed\u6d3b\u8dc3\u5ea6\u3002\u91d1\u6559\u6388\u5bf9\u672c\u6b21\u5e26\u6765\u7cbe\u5f69\u8bba\u6587\u5206\u4eab\u768429\u4f4d\u540c\u5b66\u3001\u80cc\u540e\u7684\u6307\u5bfc\u8001\u5e08\u548c\u56e2\u961f\u3001\u672c\u6b21\u4f1a\u8bae\u4e3b\u6301\u4eba\u3001\u4ee5\u53ca\u542c\u4f17\u8868\u8fbe\u4e86\u611f\u8c22\u3002<\/span><\/p>\n

\u6211\u4eec\u671f\u5f85\u66f4\u591a\u4f18\u79c0\u9752\u5e74\u5b66\u5b50\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\u53d6\u5f97\u7814\u7a76\u8fdb\u5c55\uff0c\u5e76\u5728\u672a\u6765\u7684\u62a5\u544a\u4f1a\u4e2d\u8e0a\u8dc3\u53c2\u4e0e\u3001\u5171\u540c\u63a8\u52a8\u8ba1\u7b97\u673a\u89c6\u89c9\u9886\u57df\u7684\u5b66\u672f\u4ea4\u6d41\u548c\u6280\u672f\u8fdb\u6b65\uff01<\/span><\/p>\n

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\u9644<\/span>1\uff1a\u62a5\u544a\u8bba\u6587\u5217\u8868<\/span><\/span><\/strong><\/p>\n


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1.\u6807\u9898\uff1aView-decoupled Transformer for Person Re-identification under Aerial-ground Camera Network<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u5f20\u6743\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u8d56\u5251\u714c<\/span><\/p>\n


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2.\u6807\u9898\uff1aMulti-scale Dynamic and Hierarchical Relationship Modeling for Facial Action Units Recognition<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u6c6a\u5b50\u6657\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u6c88\u7433\u7433<\/span><\/p>\n


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3.\u6807\u9898\uff1aBigGait: Learning Gait Representation You Want by Large Vision Models<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u53f6\u9876\u5f3a\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u4e8e\u4ed5\u742a\/\u5218\u6653\u660e<\/span><\/p>\n


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4.\u6807\u9898\uff1aAlignSAM: Aligning Segment Anything Model to Open Context via Reinforcement Learning<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u9ec4\u94ce\u5cfb\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u674e\u51a0\u5f6c<\/span><\/p>\n


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5.\u6807\u9898\uff1aBridging the Gap Between End-to-End and Two-Step Text Spotting<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u9ec4\u660e\u946b\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u91d1\u8fde\u6587<\/span><\/p>\n


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6.\u6807\u9898\uff1aAPSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u4f55\u4f1f\u948a\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u5f20\u6d0b\/\u5353\u709c<\/span><\/p>\n

\u00a0<\/span><\/p>\n

7.\u6807\u9898\uff1aDiffForensics: Leveraging Diffusion Prior to Image Forgery Detection and Localization<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u4f59\u6cfd\u94a6\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u502a\u6c5f\u7fa4\/\u674e\u658c<\/span><\/p>\n


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8.\u6807\u9898\uff1aCMA: A Chromaticity Map Adapter for Robust Detection of Screen-Recapture Document Images<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u6797\u826f\u5a01\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u9648\u660c\u76db<\/span><\/p>\n


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9.\u6807\u9898\uff1aTowards Modern Image Manipulation Localization: A Large-Scale Dataset and Novel Methods<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u66f2\u6668\u5e06\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u91d1\u8fde\u6587\/\u949f\u4ea6\u6b66<\/span><\/p>\n


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10.\u6807\u9898\uff1aLearning Background Prompts to Discover Implicit Knowledge for Open Vocabulary Object Detection<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u674e\u4f73\u94ed\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u674e\u51a0\u5f6c<\/span><\/p>\n


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11.\u6807\u9898\uff1aSingle-View Scene Point Cloud Human Grasp Generation<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u738b\u708e\u5eb7\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u90d1\u4f1f\u8bd7<\/span><\/p>\n


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12.\u6807\u9898\uff1aCoarse-to-Fine Latent Diffusion for Pose-Guided Person Image Synthesis<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u5362\u5f66\u4f5c\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u9a6c\u9526\u534e\/\u8c22\u6653\u534e<\/span><\/p>\n


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13.\u6807\u9898\uff1aTexture-Preserving Diffusion Models for High-Fidelity Virtual Try-On<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u6768\u7166\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u4e01\u957f\u5174<\/span><\/p>\n


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14.\u6807\u9898\uff1a360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u738b\u831c\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u5f20\u5065<\/span><\/p>\n


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15.\u6807\u9898\uff1aText-conditional Attribute Alignment across Latent Spaces for 3D Controllable Face Image Synthesis<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u5f90\u975e\u51e1\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u5434\u65af\/\u674e\u745e<\/span><\/p>\n


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16.\u6807\u9898\uff1aTowards Variable and Coordinated Holistic Co-Speech Motion Generation<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u5218\u7ffc\u98de\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u4e01\u957f\u5174\/\u66f9\u743c<\/span><\/p>\n


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17.\u6807\u9898\uff1aTackling the Singularities at the Endpoints of Time Intervals in Diffusion Models<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u5f20\u9e4f\u6cfd\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u8c22\u6653\u534e<\/span><\/p>\n


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18.\u6807\u9898\uff1aNeRF-HuGS: Improved Neural Radiance Fields in Non-static Scenes Using Heuristics-Guided Segmentation<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u9648\u5bb6\u8c6a\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u674e\u51a0\u5f6c<\/span><\/p>\n


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19.\u6807\u9898\uff1aAlignMiF: Geometry-Aligned Multimodal Implicit Field for LiDAR-Camera Joint Synthesis<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u5510\u6d9b\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u6881\u5c0f\u4e39\/\u4e8e\u5f00\u4e1e<\/span><\/p>\n


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20.\u6807\u9898\uff1aNeural Customizable Human Avatar<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u8096\u4fca\u9526\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u5f20\u9752<\/span><\/p>\n


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21.\u6807\u9898\uff1aVRetouchEr: Learning Cross-frame Feature Interdependence with Imperfection Flow for Face Retouching in Videos<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u859b\u6587\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u5434\u65af\/\u8bb8\u52c7<\/span><\/p>\n


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22.\u6807\u9898\uff1aCoSeR: Bridging Image and Language for Cognitive Super-Resolution<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u5b59\u6d69\u6cfd\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1aYujiu Yang<\/span><\/p>\n


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23.\u6807\u9898\uff1aLearning to Remove Wrinkled Transparent Film with Polarized Prior<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u5510\u5bb6\u797a\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1aYing-Cong Chen<\/span><\/p>\n


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24.\u6807\u9898\uff1aLearning Degradation-unaware Representation with Prior-based Latent Transformations for Blind Face Restoration<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u8c22\u70bc\u65b0\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u5434\u65af<\/span><\/p>\n


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25.\u6807\u9898\uff1aIncremental Residual Concept Bottleneck Models<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u5c1a\u8fb0\u94ed\uff0c\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u6768\u4f59\u4e45\/\u738b\u7389\u65fa<\/span><\/p>\n


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26.\u6807\u9898\uff1aEditGuard: Versatile Image Watermarking for Tamper Localization and Copyright Protection<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u5f20\u8f69\u5b87\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u5f20\u5065<\/span><\/p>\n


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27.\u6807\u9898\uff1aDeep Unsupervised Unrolling Networks for Phase Unwrapping<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u9648\u5fd7\u4e50\u00a0 \u00a0 \u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u5168\u5b87\u6656<\/span><\/p>\n


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28.\u6807\u9898\uff1aLatent Modulated Function for Computational Optimal Continuous Image Representation<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u4f55\u5b97\u8000\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u91d1\u679d<\/span><\/p>\n


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29.\u6807\u9898\uff1aLearning Adaptive Spatial Coherent Correlations for Speech-Preserving Facial Expression Manipulation<\/span><\/p>\n

\u8bba\u6587\u7b2c\u4e00\u4f5c\u8005\uff1a\u6797\u575a\u6ee1\u00a0\u00a0\u00a0\u00a0\u5bfc\u5e08\/\u901a\u4fe1\u4f5c\u8005\uff1a\u9648\u6dfb\u6c34<\/span><\/p>\n

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\u96442\uff1a\u4f1a\u8bae\u56de\u653e\u94fe\u63a5<\/span><\/strong><\/p>\n


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27\u65e5\u4e0a\u5348\u573a\uff1a<\/span>\u00a0<\/span><\/p>\n

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27\u65e5\u4e0b\u5348\u573a<\/span>\uff1a<\/span>\u00a0<\/span><\/p>\n

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28\u65e5\u4e0a\u5348\u573a\uff1a<\/span>\u00a0<\/span><\/p>\n

\"\u56fe\u724714.png\"<\/p>\n

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