MetaEarth-MM unifies multi-modal remote sensing image generation and any-to-any translation across five modalities via scene-centered joint modeling on the new EarthMM dataset.
Gans trained by a two time-scale update rule converge to a local nash equilibrium,
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
representative citing papers
PROVE proposes RC metrics for perceptual removal coherence and releases PROVE-Bench to better align automatic scores with human judgments on object removal tasks.
The paper proposes the Degradation Frequency Curve (DFC) as an explicit spectral representation for quantifying degradations and develops a DFC-guided multi-scale restorer that achieves state-of-the-art performance on composite and real-world benchmarks.
D2-CDIG conditions diffusion models on DEM and cloud-fog priors to generate controlled remote sensing images with decoupled terrain and atmospheric control.
PDA-GAN with pixel discriminator bridges domain gap from inpainted posters to generate SOTA image-aware layouts on a new 60k-pair CGL-Dataset.
NC-Diffusion matches quantization noise to the diffusion forward process, adds an adaptive frequency filter and zero-shot enhancement, and reports superior fidelity on benchmarks.
PCMECL improves speech-preserving facial expression manipulation by learning personalized prompts from individual visuals and using feature differencing to align visual and semantic changes from VLMs.
Beta Sampling uses spectral analysis to select critical denoising steps in diffusion models, outperforming uniform sampling on FID and IS metrics.
citing papers explorer
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MetaEarth-MM: Unified Multimodal Remote Sensing Image Generation with Scene-centered Joint Modeling
MetaEarth-MM unifies multi-modal remote sensing image generation and any-to-any translation across five modalities via scene-centered joint modeling on the new EarthMM dataset.
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PROVE: A Perceptual RemOVal cohErence Benchmark for Visual Media
PROVE proposes RC metrics for perceptual removal coherence and releases PROVE-Bench to better align automatic scores with human judgments on object removal tasks.
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Degradation Frequency Curve: An Explicit Frequency-Quantified Representation for All-in-One Image Restoration
The paper proposes the Degradation Frequency Curve (DFC) as an explicit spectral representation for quantifying degradations and develops a DFC-guided multi-scale restorer that achieves state-of-the-art performance on composite and real-world benchmarks.
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D2-CDIG: Controlled Diffusion Remote Sensing Image Generation with Dual Priors of DEM and Cloud-Fog
D2-CDIG conditions diffusion models on DEM and cloud-fog priors to generate controlled remote sensing images with decoupled terrain and atmospheric control.
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GAN-based Domain Adaptation for Image-aware Layout Generation in Advertising Poster Design
PDA-GAN with pixel discriminator bridges domain gap from inpainted posters to generate SOTA image-aware layouts on a new 60k-pair CGL-Dataset.
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A Noise Constrained Diffusion (NC-Diffusion) Framework for High Fidelity Image Compression
NC-Diffusion matches quantization noise to the diffusion forward process, adds an adaptive frequency filter and zero-shot enhancement, and reports superior fidelity on benchmarks.
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Personalized Cross-Modal Emotional Correlation Learning for Speech-Preserving Facial Expression Manipulation
PCMECL improves speech-preserving facial expression manipulation by learning personalized prompts from individual visuals and using feature differencing to align visual and semantic changes from VLMs.
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Beta Sampling is All You Need: Efficient Image Generation Strategy for Diffusion Models using Stepwise Spectral Analysis
Beta Sampling uses spectral analysis to select critical denoising steps in diffusion models, outperforming uniform sampling on FID and IS metrics.