Add to ORKGWe introduce UViM, a unified approach capable of modeling a wide range of computer vision tasks. In contrast to previous models, UViM has the same functional form for all tasks; it requires no task-specific modifications which require extensive human expertise. The approach involves two components: (I) a base model (feed-forward) which is trained to directly predict raw vision outputs, guided by a learned discrete code and (II) a language model (autoregressive) that is trained to generate the guiding code. These components complement each other: the language model is well-suited to modeling structured interdependent data, while the base model is efficient at dealing with high-dimensional outputs. We demonstrate the effectiveness of UViM on ...
In the last years, all the computer vision dramatically changed because of the deep learning systems...
Building models that can be rapidly adapted to novel tasks using only a handful of annotated example...
A visual system has to learn both which features to extract from images and how to group locations i...
154 pagesOver the course of the last decades, we have witnessed the significant progress of machine ...
With recent progress in joint modeling of visual and textual representations, Vision-Language Pretra...
Large pre-trained vision-language models like CLIP have shown great potential in learning representa...
The power of deep neural networks comes mainly from huge labeled datasets. Even though it shines on ...
A key requirement for any agent that wishes to interact with the visual world is the ability to unde...
We propose Unified-IO, a model that performs a large variety of AI tasks spanning classical computer...
The field of computer vision is currently dominated by deep learning advances. Convolutional Neural ...
As transformer evolves, pre-trained models have advanced at a breakneck pace in recent years. They h...
Computer vision has been profoundly influenced by machine learning in the past two decades. Canonica...
The human brain is adept at solving difficult high-level visual processing prob-lems such as image i...
Modern computer vision models mostly rely on massive human annotated datasets for supervised trainin...
We propose a unified look at jointly learning multiple vision tasks and visual domains through unive...
In the last years, all the computer vision dramatically changed because of the deep learning systems...
Building models that can be rapidly adapted to novel tasks using only a handful of annotated example...
A visual system has to learn both which features to extract from images and how to group locations i...
154 pagesOver the course of the last decades, we have witnessed the significant progress of machine ...
With recent progress in joint modeling of visual and textual representations, Vision-Language Pretra...
Large pre-trained vision-language models like CLIP have shown great potential in learning representa...
The power of deep neural networks comes mainly from huge labeled datasets. Even though it shines on ...
A key requirement for any agent that wishes to interact with the visual world is the ability to unde...
We propose Unified-IO, a model that performs a large variety of AI tasks spanning classical computer...
The field of computer vision is currently dominated by deep learning advances. Convolutional Neural ...
As transformer evolves, pre-trained models have advanced at a breakneck pace in recent years. They h...
Computer vision has been profoundly influenced by machine learning in the past two decades. Canonica...
The human brain is adept at solving difficult high-level visual processing prob-lems such as image i...
Modern computer vision models mostly rely on massive human annotated datasets for supervised trainin...
We propose a unified look at jointly learning multiple vision tasks and visual domains through unive...
In the last years, all the computer vision dramatically changed because of the deep learning systems...
Building models that can be rapidly adapted to novel tasks using only a handful of annotated example...
A visual system has to learn both which features to extract from images and how to group locations i...