Tag: image editing

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Emu Edit
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Emu Edit

Emu Edit is a multi-task image editing model that completes precise image editing through recognition and generation tasks, and has achieved the latest technological breakthroughs in this field. Emu Edit’s architecture is optimized for multi-task learning and trained on a wide range of tasks, including region-based editing, free-form editing, and computer vision tasks such as detection and segmentation. In addition, to handle these diverse tasks more efficiently, we introduce the concept of learned task embeddings to guide the generation process to correctly execute editing instructions. Our model can significantly improve its ability to accurately execute editing instructions through multi-task training and the use of learned task embeddings. Emu Edit also supports rapid adaptation to unseen tasks and achieves few-shot learning through task inversion. During this process, we keep the model weights unchanged and only update the task embeddings to adapt to the new tasks. Our experiments prove that Emu Edit can quickly adapt to new tasks, such as super-resolution, contour detection, etc. This makes using Emu Edit for task reversal particularly advantageous when annotated samples are limited or computational budgets are limited. To support rigorous and evidence-based evaluation of instruction-based image editing models, we also collected and publicly released a new benchmark dataset containing seven different image editing tasks: background modification (background), comprehensive image changes (global), style modification (style), object removal (remove), object addition (add), local modification (local), and color/texture modification (texture). In addition, for proper comparison with Emu Edit, we also share the generated results of Emu Edit on the dataset. Emu Edit 2023 Meta All Rights Reserved

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image editing multi-task learning Accurate Benchmark dataset +1
#image editing
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