![]() ![]() On numerous benchmarks, Flamingo outperforms models fine-tuned on thousands of times more task-specific data. For tasks lying anywhere on this spectrum, a single Flamingo model can achieve a new state of the art with few-shot learning, simply by prompting the model with task-specific examples. Gratis para uso comercial Imágenes de gran calidad. +12.000 Vectores, fotos de stock y archivos PSD. These include open-ended tasks such as visual question-answering, where the model is prompted with a question which it has to answer, captioning tasks, which evaluate the ability to describe a scene or an event, and close-ended tasks such as multiple-choice visual question-answering. Encuentra y descarga recursos gráficos gratuitos de Flamingos Flamingo. We perform a thorough evaluation of our models, exploring and measuring their ability to rapidly adapt to a variety of image and video tasks. Thanks to their flexibility, Flamingo models can be trained on large-scale multimodal web corpora containing arbitrarily interleaved text and images, which is key to endow them with in-context few-shot learning capabilities. We propose key architectural innovations to: (i) bridge powerful pretrained vision-only and language-only models, (ii) handle sequences of arbitrarily interleaved visual and textual data, and (iii) seamlessly ingest images or videos as inputs. We introduce Flamingo, a family of Visual Language Models (VLM) with this ability. Models fine-tuned on thousands of times more task-specific data.Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, Antoine Miech, Iain Barr, Yana Hasson, Karel Lenc, Arthur Mensch, Katherine Millican, Malcolm Reynolds, Roman Ring, Eliza Rutherford, Serkan Cabi, Tengda Han, Zhitao Gong, Sina Samangooei, Marianne Monteiro, Jacob L Menick, Sebastian Borgeaud, Andy Brock, Aida Nematzadeh, Sahand Sharifzadeh, Mikołaj Bińkowski, Ricardo Barreira, Oriol Vinyals, Andrew Zisserman, Karén Simonyan Abstractīuilding models that can be rapidly adapted to novel tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. Freepik’s Choice See high-quality images selected by our team daily. On numerous benchmarks, Flamingo outperforms Watercolor Flat Cartoon Geometric Gradient Isometric 3D Hand-drawn. Question-answering, where the model is prompted with a question which it has toĪnswer captioning tasks, which evaluate the ability to describe a scene or anĮvent and close-ended tasks such as multiple-choice visual question-answering.įor tasks lying anywhere on this spectrum, a single Flamingo model can achieveĪ new state of the art with few-shot learning, simply by prompting the model And, of course, not only did I get this picture AND the two previous pictures, but I got six OTHER pictures of flamingos as well. It is quite evident where this is all headed if you think beyond just images. These include open-ended tasks such as visual Implementation of Flamingo, state-of-the-art few-shot visual question. Our models, exploring and measuring their ability to rapidly adapt to a variety ![]() In-context few-shot learning capabilities. Thanks to their flexibility,įlamingo models can be trained on large-scale multimodal web corpora containingĪrbitrarily interleaved text and images, which is key to endow them with ![]() Seamlessly ingest images or videos as inputs. Sequences of arbitrarily interleaved visual and textual data, and (iii) We propose key architectural innovations to: (i)īridge powerful pretrained vision-only and language-only models, (ii) handle We introduce Flamingo, a family of Visual Language Models Handful of annotated examples is an open challenge for multimodal machine Download a PDF of the paper titled Flamingo: a Visual Language Model for Few-Shot Learning, by Jean-Baptiste Alayrac and 26 other authors Download PDF Abstract: Building models that can be rapidly adapted to novel tasks using only a ![]()
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