mlsanityData to 14 Oct 2026

VINS-120K: Ultra High-Resolution Image Editing with A Large-Scale Dataset

arXiv 2605.23518 ↗First posted 22 May 2026cs.CVcited by 1

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Abstract (arXiv)

Directly editing ultra-high-resolution (UHR) images is valuable but underexplored, primarily due to the lack of high-quality data and the challenge in modeling high-frequency texture details. We introduce VINS-120K, the first large-scale dataset for instruction-based UHR image editing, comprising 120K carefully curated triplets of instruction, input image, and edited image. Each image exceeds 4K resolution (≥4096 × 4096) and is filtered through a rigorous multi-stage pipeline to ensure visual quality, instruction alignment, and aesthetic fidelity. Built on VINS-120K, we further develop a high-frequency-aware post-adaptation strategy to extend pretrained non-high-resolution models to the UHR regime. We also present VINS-4KEval, a benchmark covering diverse editing types, to facilitate consistent evaluation in UHR settings. Experiments confirm that our work improves fine-grained detail synthesis and texture realism in UHR image editing.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 45 of its 66 references that are in the map. First posted on arXiv 22 May 2026.

Builds on

45 of 66 references.

References · 45

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33 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026BluePixel: Toward Native 4K Agentic Image Editingcited by 0