VOSR shows that competitive generative image super-resolution with faithful structures can be achieved by training a diffusion-style model from scratch on visual data alone, using a vision encoder for guidance and a restoration-oriented sampling strategy.
Maniqa: Multi-dimension attention network for no-reference image quality assessment
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ChArtist generates pictorial charts via a Diffusion Transformer using skeleton-based spatial control and reference-image subject control, supported by a new 30,000-triplet dataset and data accuracy metric.
Q-DeepSight proposes a think-with-image multimodal CoT framework trained via RL with perceptual curriculum rewards and evidence gradient filtering to achieve SOTA IQA performance and enable training-free perceptual refinement in image generation.
citing papers explorer
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VOSR: A Vision-Only Generative Model for Image Super-Resolution
VOSR shows that competitive generative image super-resolution with faithful structures can be achieved by training a diffusion-style model from scratch on visual data alone, using a vision encoder for guidance and a restoration-oriented sampling strategy.
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ChArtist: Generating Pictorial Charts with Unified Spatial and Subject Control
ChArtist generates pictorial charts via a Diffusion Transformer using skeleton-based spatial control and reference-image subject control, supported by a new 30,000-triplet dataset and data accuracy metric.
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Q-DeepSight: Incentivizing Thinking with Images for Image Quality Assessment and Refinement
Q-DeepSight proposes a think-with-image multimodal CoT framework trained via RL with perceptual curriculum rewards and evidence gradient filtering to achieve SOTA IQA performance and enable training-free perceptual refinement in image generation.