Title Dossierآخر

Machine Vision for Cultural Heritage & Natural Science Collections

Name: Machine Vision for Cultural Heritage & Natural Science Collections
Genre: آخر
Released: 2019-12-02
Rating: 0

آخر

The mass digitization of visual collections, on the order of hundreds of thousands or millions of images, creates new challenges for curators and researchers alike. The Yale-Smithsonian Partnership brings together scholars and curators from both institutions for conversations, demonstrations, and future partnerships.

السنة

2019

المدة

3h 15m

IMDb

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اختيارات مميزة

هل تريد المزيد من الترفيه؟ استكشف منصة الألعاب المقترحة ووجهة الدراما القصيرة التي نوصي بها.

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دراما قصيرة

VSKIT Drama

شاهد المسلسلات القصيرة الرائجة وقصص الرومانسية والدراما الجديدة مع تحديثات يومية سريعة.

زر الآن

Susan Gibbons (University Librarian & Deputy Provost, Yale) The Yale-Smithsonian Partnership presents: Machine Vision for Cultural Heritage & Natural Science Collections The mass digitization of visual collections, on the order of hundreds of thousands or millions of images, creates new challenges for curators and researchers alike. Simultaneously, the rapid pace of industry innovation in deep learning (from guiding self-driving cars to captioning smartphone images) demands the attention of library, museum, and academic professionals. Existing practices of cataloging and description can be augmented by recent advancements in machine vision – and human expertise can likewise guide the development of future algorithms for the humanities and sciences alike. This event, held at the Franke Family Digital Humanities Laboratory in Yale’s Sterling Memorial Library, brings together scholars and curators from both institutions for conversations, demonstrations, and future partnerships. dhlab.yal

KobbyNov 24, 2025

Jacob Kim (Hirshhorn, Smithsonian Institution) & Dan Michaelson (School of Art, Yale) The Yale-Smithsonian Partnership presents: Machine Vision for Cultural Heritage & Natural Science Collections The mass digitization of visual collections, on the order of hundreds of thousands or millions of images, creates new challenges for curators and researchers alike. Simultaneously, the rapid pace of industry innovation in deep learning (from guiding self-driving cars to captioning smartphone images) demands the attention of library, museum, and academic professionals. Existing practices of cataloging and description can be augmented by recent advancements in machine vision – and human expertise can likewise guide the development of future algorithms for the humanities and sciences alike. This event, held at the Franke Family Digital Humanities Laboratory in Yale’s Sterling Memorial Library, brings together scholars and curators from both institutions for conversations, demonstrations, and futu

RosaNov 24, 2025

Stephen Krewson (English, Yale) The Yale-Smithsonian Partnership presents: Machine Vision for Cultural Heritage & Natural Science Collections The mass digitization of visual collections, on the order of hundreds of thousands or millions of images, creates new challenges for curators and researchers alike. Simultaneously, the rapid pace of industry innovation in deep learning (from guiding self-driving cars to captioning smartphone images) demands the attention of library, museum, and academic professionals. Existing practices of cataloging and description can be augmented by recent advancements in machine vision – and human expertise can likewise guide the development of future algorithms for the humanities and sciences alike. This event, held at the Franke Family Digital Humanities Laboratory in Yale’s Sterling Memorial Library, brings together scholars and curators from both institutions for conversations, demonstrations, and future partnerships. dhlab.yale.edu/machine-vision

Yaka mwanaNov 24, 2025

Adam Metallo (Digitization Program Office, Smithsonian Institution) The Yale-Smithsonian Partnership presents: Machine Vision for Cultural Heritage & Natural Science Collections The mass digitization of visual collections, on the order of hundreds of thousands or millions of images, creates new challenges for curators and researchers alike. Simultaneously, the rapid pace of industry innovation in deep learning (from guiding self-driving cars to captioning smartphone images) demands the attention of library, museum, and academic professionals. Existing practices of cataloging and description can be augmented by recent advancements in machine vision – and human expertise can likewise guide the development of future algorithms for the humanities and sciences alike. This event, held at the Franke Family Digital Humanities Laboratory in Yale’s Sterling Memorial Library, brings together scholars and curators from both institutions for conversations, demonstrations, and future partnerships. d

Sarah ElizabethNov 24, 2025

Catherine DeRose (DHLab, Yale) The Yale-Smithsonian Partnership presents: Machine Vision for Cultural Heritage & Natural Science Collections The mass digitization of visual collections, on the order of hundreds of thousands or millions of images, creates new challenges for curators and researchers alike. Simultaneously, the rapid pace of industry innovation in deep learning (from guiding self-driving cars to captioning smartphone images) demands the attention of library, museum, and academic professionals. Existing practices of cataloging and description can be augmented by recent advancements in machine vision – and human expertise can likewise guide the development of future algorithms for the humanities and sciences alike. This event, held at the Franke Family Digital Humanities Laboratory in Yale’s Sterling Memorial Library, brings together scholars and curators from both institutions for conversations, demonstrations, and future partnerships. dhlab.yale.edu/machine-vision

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Alex White (NMNH & Data Science Lab, Smithsonian Institution) The Yale-Smithsonian Partnership presents: Machine Vision for Cultural Heritage & Natural Science Collections The mass digitization of visual collections, on the order of hundreds of thousands or millions of images, creates new challenges for curators and researchers alike. Simultaneously, the rapid pace of industry innovation in deep learning (from guiding self-driving cars to captioning smartphone images) demands the attention of library, museum, and academic professionals. Existing practices of cataloging and description can be augmented by recent advancements in machine vision – and human expertise can likewise guide the development of future algorithms for the humanities and sciences alike. This event, held at the Franke Family Digital Humanities Laboratory in Yale’s Sterling Memorial Library, brings together scholars and curators from both institutions for conversations, demonstrations, and future partnerships. dhlab.y

AhmedFathyActorNov 24, 2025

Douglas Duhaime (DHLab, Yale) The Yale-Smithsonian Partnership presents: Machine Vision for Cultural Heritage & Natural Science Collections The mass digitization of visual collections, on the order of hundreds of thousands or millions of images, creates new challenges for curators and researchers alike. Simultaneously, the rapid pace of industry innovation in deep learning (from guiding self-driving cars to captioning smartphone images) demands the attention of library, museum, and academic professionals. Existing practices of cataloging and description can be augmented by recent advancements in machine vision – and human expertise can likewise guide the development of future algorithms for the humanities and sciences alike. This event, held at the Franke Family Digital Humanities Laboratory in Yale’s Sterling Memorial Library, brings together scholars and curators from both institutions for conversations, demonstrations, and future partnerships. dhlab.yale.edu/machine-vision

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Peter Leonard (DHLab, Yale) The Yale-Smithsonian Partnership presents: Machine Vision for Cultural Heritage & Natural Science Collections The mass digitization of visual collections, on the order of hundreds of thousands or millions of images, creates new challenges for curators and researchers alike. Simultaneously, the rapid pace of industry innovation in deep learning (from guiding self-driving cars to captioning smartphone images) demands the attention of library, museum, and academic professionals. Existing practices of cataloging and description can be augmented by recent advancements in machine vision – and human expertise can likewise guide the development of future algorithms for the humanities and sciences alike. This event, held at the Franke Family Digital Humanities Laboratory in Yale’s Sterling Memorial Library, brings together scholars and curators from both institutions for conversations, demonstrations, and future partnerships. dhlab.yale.edu/machine-vision

Depi😍😍Nov 24, 2025

Holly Rushmeier (Computer Science, Yale) The Yale-Smithsonian Partnership presents: Machine Vision for Cultural Heritage & Natural Science Collections The mass digitization of visual collections, on the order of hundreds of thousands or millions of images, creates new challenges for curators and researchers alike. Simultaneously, the rapid pace of industry innovation in deep learning (from guiding self-driving cars to captioning smartphone images) demands the attention of library, museum, and academic professionals. Existing practices of cataloging and description can be augmented by recent advancements in machine vision – and human expertise can likewise guide the development of future algorithms for the humanities and sciences alike. This event, held at the Franke Family Digital Humanities Laboratory in Yale’s Sterling Memorial Library, brings together scholars and curators from both institutions for conversations, demonstrations, and future partnerships. dhlab.yale.edu/machine-visio

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Rebecca Dikow (Data Science Lab, Smithsonian Institution) The Yale-Smithsonian Partnership presents: Machine Vision for Cultural Heritage & Natural Science Collections The mass digitization of visual collections, on the order of hundreds of thousands or millions of images, creates new challenges for curators and researchers alike. Simultaneously, the rapid pace of industry innovation in deep learning (from guiding self-driving cars to captioning smartphone images) demands the attention of library, museum, and academic professionals. Existing practices of cataloging and description can be augmented by recent advancements in machine vision – and human expertise can likewise guide the development of future algorithms for the humanities and sciences alike. This event, held at the Franke Family Digital Humanities Laboratory in Yale’s Sterling Memorial Library, brings together scholars and curators from both institutions for conversations, demonstrations, and future partnerships. dhlab.yale.

Machine Vision for Cultural Heritage & Natural Science Collections

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