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Delta-Adapter enables image editing with single-pair supervision

By PulseAugur Editorial · [1 sources] ·

Researchers have developed Delta-Adapter, a novel approach for image editing that requires only a single source-target image pair for supervision, bypassing the need for multiple pairs or textual guidance. The method extracts a "semantic delta" from the exemplar pair using a pre-trained vision encoder and injects this into an editing model via an adapter. This technique allows for more scalable training data curation and improved generalization to diverse editing tasks, outperforming existing methods in accuracy and consistency. AI

IMPACT Enables more scalable and generalizable image editing models by reducing data supervision requirements.

RANK_REASON Academic paper detailing a new method for image editing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Delta-Adapter enables image editing with single-pair supervision

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Academic paper detailing a new method for image editing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Xudong Mao ·

    Delta-Adapter: Scalable Exemplar-Based Image Editing with Single-Pair Supervision

    Exemplar-based image editing applies a transformation defined by a source-target image pair to a new query image. Existing methods rely on a pair-of-pairs supervision paradigm, requiring two image pairs sharing the same edit semantics to learn the target transformation. This cons…

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