Physics-IQ benchmark reveals that generative video models exhibit limited physical understanding unrelated to their visual quality.
Image quality assessment: from error visibility to structural similarity
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
citation-role summary
dataset 1
citation-polarity summary
roles
dataset 1polarities
background 1representative citing papers
GeoSR-Bench is the first SR benchmark that directly measures how super-resolved remote sensing imagery improves performance on land cover segmentation, infrastructure mapping, and biophysical variable estimation rather than relying on fidelity metrics.
VFMTok builds a generalist image tokenizer on frozen VFMs using adaptive quantization and semantic alignment, delivering gFID 1.36 for autoregressive and 1.25 for continuous generation on ImageNet with 3x faster convergence.
A self-supervised framework using SURE and equivariant constraints produces super-resolved Sentinel-5P images comparable to supervised baselines without HR references and with physically plausible structures validated against EMIT data.
A Bayesian auxiliary signal derived from noisy data enables learning-based refinement that improves statistical consistency and quality of any denoiser without clean images or precise noise distribution knowledge.
The paper introduces World Action Models as a new paradigm unifying predictive world modeling with action generation in embodied foundation models and provides a taxonomy of existing approaches.
citing papers explorer
-
Do generative video models understand physical principles?
Physics-IQ benchmark reveals that generative video models exhibit limited physical understanding unrelated to their visual quality.
-
Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration
GeoSR-Bench is the first SR benchmark that directly measures how super-resolved remote sensing imagery improves performance on land cover segmentation, infrastructure mapping, and biophysical variable estimation rather than relying on fidelity metrics.
-
Vision Foundation Models as Generalist Tokenizers for Image Generation
VFMTok builds a generalist image tokenizer on frozen VFMs using adaptive quantization and semantic alignment, delivering gFID 1.36 for autoregressive and 1.25 for continuous generation on ImageNet with 3x faster convergence.
-
Self-Supervised Super-Resolution for Sentinel-5P Hyperspectral Images
A self-supervised framework using SURE and equivariant constraints produces super-resolved Sentinel-5P images comparable to supervised baselines without HR references and with physically plausible structures validated against EMIT data.
-
Learning-based Statistical Refinement for Denoising
A Bayesian auxiliary signal derived from noisy data enables learning-based refinement that improves statistical consistency and quality of any denoiser without clean images or precise noise distribution knowledge.
-
World Action Models: The Next Frontier in Embodied AI
The paper introduces World Action Models as a new paradigm unifying predictive world modeling with action generation in embodied foundation models and provides a taxonomy of existing approaches.
- Training-Free Inference for High-Resolution Sinogram Completion