5 papers
High-Fidelity Video Quality Assessment with VQA-Specific Saliency
Hakan Emre Gedik, Shashank Gupta, Alan Bovik
No-reference video quality assessment (NR VQA) has recently seen promising progress with deep learning. However, video data is inherently large, and processing them with deep model…
Learning Where to Look and How to Judge: Resolution-agnostic Image Quality Assessment with Quality-aware Saliency
Hakan Emre Gedik, Shashank Gupta, Alan Bovik
No-reference image quality assessment (NR IQA) has recently benefited from deep and multimodal models, yet many SOTA systems still violate at least one basic requirement: they eith…
GenSP: Consistent Spherical Parameterization via Learning Shape Generative Models
Sai Karthikey Pentapati, Shashank Gupta, Rajesh Sureddi +3
We introduce GenSP, a data-driven framework that learns consistent spherical parameterizations across a collection of genus-0 shapes. Instead of optimizing the parameterization of…
PIT-QMM: A Large Multimodal Model For No-Reference Point Cloud Quality Assessment
Shashank Gupta, Gregoire Phillips, Alan C. Bovik
Large Multimodal Models (LMMs) have recently enabled considerable advances in the realm of image and video quality assessment, but this progress has yet to be fully explored in the…
Rectified-CFG++ for Flow Based Models
Shreshth Saini, Shashank Gupta, Alan C. Bovik
Classifier-free guidance (CFG) is the workhorse for steering large diffusion models toward text-conditioned targets, yet its native application to rectified flow (RF) based models…