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Measured Sliders: Learning Continuous Controls from Differentiable Image Measurements

Paper recorded by Signals 4 on 2026-09-04 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-04 on arXiv · recorded by Signals 4 on 2026-09-07

Category: cs.CV · 计算机视觉 · first seen 2026-09-07

Abstract

Continuous sliders are useful only when coefficient changes produce predictable image changes. Yet most diffusion sliders derive their axes from text or learned representations, leaving their scales disconnected from observable image properties. Consequently, we cannot tell in advance which attributes are learnable, compare control strengths directly, or anticipate interference when multiple contr

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#140 most recent of 237 cs.CV papers we have recorded · ↑ newer: Cross-Domain Tracker Adaptation Without Target-Domain Labels via Visio · ↓ older: First Things First: Teaching LLM-Based Agents to Prioritize Must-Haves
Cite this page: Measured Sliders: Learning Continuous Controls from Differentiable Image Measurements: the #140 most recent of 237 cs.CV papers we have recorded (as of 2026-09-04). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/measured-sliders-learning-continuous-controls-from-differentiable-image-measurem.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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