MUSE shows that the native timestep embedding in diffusion models acts as a parameter-free steering signal for multi-task monocular depth and normal estimation via manifold decoupling in latent space.
Steering one-step diffusion model with fidelity-rich decoder for fast image compression,
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
SPRDiff is a diffusion model for ultra-low bitrate image compression that fuses features from distortion-oriented, semantic-oriented, and VAE encoders plus a dual-feature reconstruction module to outperform prior methods on rate-distortion-perception trade-offs.
citing papers explorer
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MUSE: Unlocking Timestep as Native Task Steering for One-Step Dense Prediction
MUSE shows that the native timestep embedding in diffusion models acts as a parameter-free steering signal for multi-task monocular depth and normal estimation via manifold decoupling in latent space.
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Exploiting Semantic and Pixel Representations for Ultra-Low Bitrate Image Compression
SPRDiff is a diffusion model for ultra-low bitrate image compression that fuses features from distortion-oriented, semantic-oriented, and VAE encoders plus a dual-feature reconstruction module to outperform prior methods on rate-distortion-perception trade-offs.