{"id":6455,"date":"2024-07-10T21:01:42","date_gmt":"2024-07-10T13:01:42","guid":{"rendered":"https:\/\/17aitech.com\/?p=6455"},"modified":"2024-08-13T03:05:30","modified_gmt":"2024-08-12T19:05:30","slug":"%e5%a4%a7%e6%a8%a1%e5%9e%8b%e6%97%b6%e4%bb%a3%e7%9a%84%e8%ae%a1%e7%ae%97%e6%9c%ba%e8%a7%86%e8%a7%89%ef%bc%81cvpr-2024%e7%ba%bf%e4%b8%8a%e5%88%86%e4%ba%ab%e4%bc%9a%e5%85%a8%e6%97%a5%e7%a8%8b%e5%85%ac","status":"publish","type":"post","link":"https:\/\/17aitech.com\/?p=6455","title":{"rendered":"\u5927\u6a21\u578b\u65f6\u4ee3\u7684\u8ba1\u7b97\u673a\u89c6\u89c9\uff01CVPR 2024\u7ebf\u4e0a\u5206\u4eab\u4f1a\u5168\u65e5\u7a0b\u516c\u5e03"},"content":{"rendered":"

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\u81ea\u4ece OpenAI \u53d1\u5e03 ChatGPT \u4ee5\u6765\uff0c\u6574\u4e2a\u6280\u672f\u793e\u533a\u5bf9\u5927\u6a21\u578b\u3001AIGC \u7684\u5173\u6ce8\u8d8a\u6765\u8d8a\u9ad8\u3002<\/p>\n

\u5927\u6a21\u578b\u65f6\u4ee3\uff0c\u8ba1\u7b97\u673a\u89c6\u89c9<\/mark>\uff08CV\uff09\u9886\u57df\u7684\u70ed\u70b9\u8bdd\u9898\u4e5f\u5728\u4e0d\u65ad\u7684\u53d1\u751f\u7740\u53d8\u5316\u3002<\/p>\n

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\u672c\u6b21\u8bba\u6587\u5206\u4eab\u4f1a\u8bbe\u7f6e Keynote\u3001 \u8bba\u6587\u5206\u4eab\u73af\u8282\uff0c\u5c31\u4e1a\u5185\u5173\u6ce8\u7684 CV \u70ed\u95e8\u4e3b\u9898\u9080\u8bf7\u9876\u7ea7\u4e13\u5bb6\u3001\u8bba\u6587\u4f5c\u8005\u4e0e\u89c2\u4f17\u505a\u5b66\u672f\u4ea4\u6d41\u3002<\/p>\n

\u4eca\u5929\uff0c\u5206\u4eab\u4f1a\u5168\u65e5\u7a0b\u3001Keynote \u5206\u4eab\u5609\u5bbe\u53ca\u6f14\u8bb2\u4e3b\u9898\u6b63\u5f0f\u516c\u5e03\u3002<\/p>\n

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

Keynote \u5609\u5bbe\u53ca\u6f14\u8bb2\u4e3b\u9898<\/strong><\/p>\n

\u4e0a\u5348 Keynote 1\uff1a\u91d1\u5c0f\u521a \u6570\u5b57\u4eba\u5efa\u6a21\u52a8\u753b\u5173\u952e\u6280\u672f<\/strong><\/p>\n

\u5206\u4eab\u4eba\u4ecb\u7ecd\uff1a\u91d1\u5c0f\u521a\uff0c\u6d59\u6c5f\u5927\u5b66\u8ba1\u7b97\u673a\u79d1\u5b66\u4e0e\u6280\u672f\u5b66\u9662\u6559\u6388\uff0c\u535a\u58eb\u751f\u5bfc\u5e08\u3002\u6d59\u6c5f\u5927\u5b66 – \u817e\u8baf<\/mark>\u6e38\u620f\u667a\u80fd\u56fe\u5f62\u521b\u65b0\u6280\u672f\u8054\u5408\u5b9e\u9a8c\u5ba4\u4e3b\u4efb\uff0c\u6d59\u6c5f\u7701\u865a\u62df\u73b0\u5b9e<\/mark>\u4ea7\u4e1a\u8054\u76df\u7406\u4e8b\u957f\u3002\u7b2c\u4e5d\u5c4a\u970d\u82f1\u4e1c\u9752\u5e74\u6559\u5e08\u57fa\u91d1\u3001\u6d59\u6c5f\u7701\u6770\u51fa\u9752\u5e74\u57fa\u91d1\u83b7\u5f97\u8005\uff0c\u5165\u9009\u6559\u80b2\u90e8\u65b0\u4e16\u7eaa\u4f18\u79c0\u4eba\u624d\u652f\u6301\u8ba1\u5212\u3002\u5728 ACM TOG (Proc. of Siggraph) \u7b49\u56fd\u9645\u91cd\u8981\u5b66\u672f\u520a\u7269\u4e0a\u53d1\u8868\u8bba\u6587 180 \u591a\u7bc7\u3002\u83b7\u7701\u90e8\u7ea7\u4e00\u7b49\u5956\u4e00\u6b21\uff0c\u4e8c\u7b49\u5956\u4e8c\u6b21\u3002<\/p>\n

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\u4e0a\u5348 Keynote 2\uff1a\u6731\u4fca\u5f66<\/mark> Enabling Collaboration between Creator<\/mark>s and Generative Models<\/strong><\/p>\n

\u5206\u4eab\u4eba\u4ecb\u7ecd\uff1aJun-Yan Zhu is an Assistant Professor at CMU\u2019s School of Computer Science. Prior to joining CMU, he was a Research Scientist at Adobe Research and a postdoc at MIT CSAIL. He obtained his Ph.D. from UC Berkeley and B.E. from Tsinghua University. He studies computer vision, computer graphics, and computational photography. His current research focuses on generative models for visual storytelling. He has received the Packard Fellowship, the NSF CAREER Award, the ACM SIGGRAPH Outstanding Doctoral Dissertation Award, and the UC Berkeley EECS David J. Sakrison Memorial Prize for outstanding doctoral research, among other awards.<\/p>\n

\u5206\u4eab\u6458\u8981\uff1aLarge-scale generative visual models, such as DALL\u30fbE and Stable Diffusion, have made content creation as little effort as writing a short text description. Meanwhile, these models also spark concerns among artists, designers, and photographers about job security and proper credit for their contributions to the training data. This leads to many questions: Will generative models make creators\u2019 jobs obsolete? Should creators stop publicly sharing their work? Should we ban generative models altogether?<\/p>\n

In this talk, I argue that human creators and generative models can coexist. To achieve it, we need to involve creators in the loop of both model inference and model training while crediting their efforts for their involvement. I will first explore our recent efforts in model customization, which allows creators to freely control the model\u2019s behavior by adding, altering, or removing concepts and rules. I will demonstrate several applications, including customizing models with multiple personal concepts and removing copyrighted content. I will then discuss our data attribution algorithm for assessing the influence of each training image for a generated sample. Collectively, we aim to allow creators to leverage the models while retaining control over the creation process and data ownership.\u00a0<\/p>\n

\u4e0b\u5348 Keynote 1\uff1a\u82a6\u6e05\u6797 \u817e\u8baf<\/mark>\u6df7\u5143\u6587\u751f\u56fe\u7684\u7b97\u6cd5\u6838\u5fc3 \u2014\u2014DIT \u67b6\u6784\u89e3\u8bfb<\/strong><\/p>\n

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\u5206\u4eab\u6458\u8981\uff1a<\/p>\n

1\u3001\u8d8b\u52bf\uff1a\u89c6\u89c9\u751f\u6210\u6280\u672f\u7684\u65b0\u7bc7\u7ae0 \u2014\u2014DIT<\/p>\n

2\u3001\u6df7\u5143 DIT \u4e09\u5927\u5347\u7ea7<\/p>\n

3\u3001\u6548\u679c\u5c55\u793a & \u5b9a\u91cf\u5206\u6790 \u2014\u2014demo<\/p>\n

4\u3001\u5168\u9762\u5f00\u6e90<\/p>\n

\u4e0b\u5348 Keynote 2\uff1a\u76db\u5f8b Multimodal Large Language Models Meets Embodied Agents<\/strong><\/p>\n

\u5206\u4eab\u4eba\u4ecb\u7ecd\uff1a\u76db\u5f8b\uff0c\u5317\u4eac\u822a\u7a7a\u822a\u5929\u5927\u5b66 \u201c\u5353\u8d8a\u767e\u4eba\u201d \u526f\u6559\u6388\uff0c\u5165\u9009\u5317\u822a\u9752\u5e74\u62d4\u5c16\u8ba1\u5212\u3002\u7814\u7a76\u65b9\u5411\u662f\u4e09\u7ef4\u89c6\u89c9\u3001\u591a\u6a21\u6001\u5927\u6a21\u578b\u548c\u5177\u8eab\u667a\u80fd\u3002\u5728 TPAMI\/IJCV \u4ee5\u53ca CVPR\/ICCV\/ECCV \u7b49\u91cd\u8981\u56fd\u9645\u671f\u520a\u548c\u4f1a\u8bae\u53d1\u8868\u8bba\u6587 50 \u4f59\u7bc7\uff0c\u542b\u591a\u7bc7\u4f1a\u8bae\u53e3\u5934\u62a5\u544a\u6216\u4eae\u70b9\u8bba\u6587\u3002\u8c37\u6b4c\u5f15\u7528\u8d85 4900 \u6b21\u3002\u62c5\u4efb ACM Computer Surveys \u526f\u7f16\uff0cCVPR 2024\/ECCV 2024\/ACM MM 2024 \u9886\u57df\u4e3b\u5e2d\u7b49\u3002\u4e3b\u6301\u6216\u53c2\u4e0e\u591a\u9879\u56fd\u5bb6\u81ea\u7136\u79d1\u5b66\u57fa\u91d1\u3001\u79d1\u6280\u90e8\u91cd\u70b9\u7814\u53d1\u8ba1\u5212\u7b49\u9879\u76ee\u3002<\/p>\n

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\u53c2\u4e0e\u65b9\u5f0f<\/strong><\/p>\n

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

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