Stream-DiffVSR enables practical low-latency video super-resolution by combining a four-step distilled denoiser, auto-regressive temporal guidance, and a temporal processor in a strictly causal pipeline.
Vrt: A video restoration transformer
4 Pith papers cite this work. Polarity classification is still indexing.
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Q-Align trains LMMs on discrete text-defined levels for visual scoring, achieving SOTA on IQA, IAA, and VQA while unifying the tasks in OneAlign.
TIGER is a tri-prior fusion method for face video restoration using identity, geometry, and generative priors with progressive training to achieve SOTA identity fidelity and temporal stability on a new large-scale dataset.
A hybrid AI system combines super-resolution, YOLO-based detection, and vision-language models to semantically classify building damage severity in pre- and post-disaster satellite images.
citing papers explorer
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Stream-DiffVSR: Low-Latency Streamable Video Super-Resolution via Auto-Regressive Diffusion
Stream-DiffVSR enables practical low-latency video super-resolution by combining a four-step distilled denoiser, auto-regressive temporal guidance, and a temporal processor in a strictly causal pipeline.
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Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
Q-Align trains LMMs on discrete text-defined levels for visual scoring, achieving SOTA on IQA, IAA, and VQA while unifying the tasks in OneAlign.
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TIGER: Taming Identity, Geometry, and Generative Priors for High-Quality Face Video Restoration
TIGER is a tri-prior fusion method for face video restoration using identity, geometry, and generative priors with progressive training to achieve SOTA identity fidelity and temporal stability on a new large-scale dataset.
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From Pixels to Semantics: A Multi-Stage AI Framework for Structural Damage Detection in Satellite Imagery
A hybrid AI system combines super-resolution, YOLO-based detection, and vision-language models to semantically classify building damage severity in pre- and post-disaster satellite images.