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Found 24 papers, 9 papers with code
AutoML for Large Capacity Modeling of Meta's Ranking Systems
The large scale of models and tight production schedule requires AutoML to outperform human baselines by only using a small number of model evaluation trials (around 100).
Perceptual Quality Assessment of 360$^\circ$ Images Based on Generative Scanpath Representation
To address these issues, we introduce a unique generative scanpath representation (GSR) for effective quality inference of 360$^\circ$ images, which aggregates varied perceptual experiences of multi-hypothesis users under a predefined viewing condition.
Blind Omnidirectional Image Quality Assessment: Integrating Local Statistics and Global Semantics
Omnidirectional image quality assessment (OIQA) aims to predict the perceptual quality of omnidirectional images that cover the whole 180$\times$360$^{\circ}$ viewing range of the visual environment.
ScanDMM: A Deep Markov Model of Scanpath Prediction for 360deg Images
Scanpath prediction for 360deg images aims to produce dynamic gaze behaviors based on the human visual perception mechanism.
Learning Acceptance Regions for Many Classes with Anomaly Detection
Set-valued classification, a new classification paradigm that aims to identify all the plausible classes that an observation belongs to, can be obtained by learning the acceptance regions for all classes.
Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical Fidelity
There has been a growing interest in developing image super-resolution (SR) algorithms that convert low-resolution (LR) to higher resolution images, but automatically evaluating the visual quality of super-resolved images remains a challenging problem.
Perceptual Quality Assessment of Colored 3D Point Clouds
In this work, we first build a large 3D point cloud database for subjective and objective quality assessment of point clouds.
Degraded Reference Image Quality Assessment
In practical media distribution systems, visual content usually undergoes multiple stages of quality degradation along the delivery chain, but the pristine source content is rarely available at most quality monitoring points along the chain to serve as a reference for quality assessment.
Deep Image Debanding
Banding or false contour is an annoying visual artifact whose impact is even more pronounced in ultra high definition, high dynamic range, and wide colour gamut visual content, which is becoming increasingly popular.
Deep Neural Networks for Blind Image Quality Assessment: Addressing the Data Challenge
This casts great challenges to deep neural network (DNN) based blind IQA (BIQA), which requires large-scale training data that is representative of the natural image distribution.
Probeable DARTS with Application to Computational Pathology
AI technology has made remarkable achievements in computational pathology (CPath), especially with the help of deep neural networks.
Image Super-Resolution Quality Assessment: Structural Fidelity Versus Statistical Naturalness
In this paper, we assess the quality of SISR generated images in a two-dimensional (2D) space of structural fidelity versus statistical naturalness.
Quantifying Visual Image Quality: A Bayesian View
Image quality assessment (IQA) models aim to establish a quantitative relationship between visual images and their perceptual quality by human observers.
Learning-Based Quality Assessment for Image Super-Resolution
Image Super-Resolution (SR) techniques improve visual quality by enhancing the spatial resolution of images.
Design and Commissioning of the PandaX-4T Cryogenic Distillation System for Krypton and Radon Removal
An online cryogenic distillation system for the removal of krypton and radon from xenon was designed and constructed for PandaX-4T, a highly sensitive dark matter detection experiment.
Instrumentation and Detectors High Energy Physics - Experiment
FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology
Out-of-focus microscopy lens in digital pathology is a critical bottleneck in high-throughput Whole Slide Image (WSI) scanning platforms, for which pixel-level automated Focus Quality Assessment (FQA) methods are highly desirable to help significantly accelerate the clinical workflows.
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network
We propose a deep bilinear model for blind image quality assessment (BIQA) that handles both synthetic and authentic distortions.
Ranked #2 on
Video Quality Assessment
on MSU NR VQA Database
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs
One of the biggest challenges in learning BIQA models is the conflict between the gigantic image space (which is in the dimension of the number of image pixels) and the extremely limited reliable ground truth data for training.
PEA265: Perceptual Assessment of Video Compression Artifacts
To monitor and improve visual QoE, it is crucial to develop subjective and objective measures that can identify and quantify various types of PEAs.
Limits on Axion Couplings from the first 80-day data of PandaX-II Experiment
We report new searches for the solar axions and galactic axion-like dark matter particles, using the first low-background data from PandaX-II experiment at China Jinping Underground Laboratory, corresponding to a total exposure of about $2. 7\times 10^4$ kg$\cdot$day.
High Energy Physics - Experiment Solar and Stellar Astrophysics High Energy Physics - Phenomenology
Deep Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks
The human visual system excels at detecting local blur of visual images, but the underlying mechanism is not well understood.
Group MAD Competition - A New Methodology to Compare Objective Image Quality Models
We first build a database that contains 4, 744 source natural images, together with 94, 880 distorted images created from them.
Image Super-Resolution Based on Sparsity Prior via Smoothed $l_0$ Norm
However, sparse representation of a signal over a known dictionary is an ill-posed, combinatorial optimization problem.
Image quality assessment: from error visibility to structural similarity
Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system.
Ranked #11 on
Video Quality Assessment
on MSU FR VQA Database
