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Found 49 papers, 30 papers with code
Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach
In this paper, we delve deeper into semantics-oriented multitask learning for DeepFake detection, leveraging the relationships among face semantics via joint embedding.
Learned HDR Image Compression for Perceptually Optimal Storage and Display
High dynamic range (HDR) capture and display have seen significant growth in popularity driven by the advancements in technology and increasing consumer demand for superior image quality.
Multiscale Sliced Wasserstein Distances as Perceptual Color Difference Measures
Contemporary color difference (CD) measures for photographic images typically operate by comparing co-located pixels, patches in a ``perceptually uniform'' color space, or features in a learned latent space.
Arbitrary-Scale Video Super-Resolution with Structural and Textural Priors
Arbitrary-scale video super-resolution (AVSR) aims to enhance the resolution of video frames, potentially at various scaling factors, which presents several challenges regarding spatial detail reproduction, temporal consistency, and computational complexity.
Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection Method
In recent years, deep learning has greatly streamlined the process of generating realistic fake face images.
AniClipart: Clipart Animation with Text-to-Video Priors
To generate cartoon-style and smooth motion, we first define B\'{e}zier curves over keypoints of the clipart image as a form of motion regularization.
Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition
The past years have witnessed a proliferation of large language models (LLMs).
Learned Scanpaths Aid Blind Panoramic Video Quality Assessment
Panoramic videos have the advantage of providing an immersive and interactive viewing experience.
A Comprehensive Study of Multimodal Large Language Models for Image Quality Assessment
While Multimodal Large Language Models (MLLMs) have experienced significant advancement in visual understanding and reasoning, their potential to serve as powerful, flexible, interpretable, and text-driven models for Image Quality Assessment (IQA) remains largely unexplored.
Comparison of No-Reference Image Quality Models via MAP Estimation in Diffusion Latents
Contemporary no-reference image quality assessment (NR-IQA) models can effectively quantify the perceived image quality, with high correlations between model predictions and human perceptual scores on fixed test sets.
Modular Blind Video Quality Assessment
Blind video quality assessment (BVQA) plays a pivotal role in evaluating and improving the viewing experience of end-users across a wide range of video-based platforms and services.
Hierarchical Prior-based Super Resolution for Point Cloud Geometry Compression
The content-dependent hierarchical prior is constructed at the encoder side, which enables coarse-to-fine super resolution of the point cloud geometry at the decoder side.
Perceptual Assessment and Optimization of HDR Image Rendering
Existing quality models are mostly designed for low dynamic range (LDR) images, and do not align well with human perception of HDR image quality.
Analysis of Video Quality Datasets via Design of Minimalistic Video Quality Models
By minimalistic, we restrict our family of BVQA models to build only upon basic blocks: a video preprocessor (for aggressive spatiotemporal downsampling), a spatial quality analyzer, an optional temporal quality analyzer, and a quality regressor, all with the simplest possible instantiations.
Scanpath Prediction in Panoramic Videos via Expected Code Length Minimization
Predicting human scanpaths when exploring panoramic videos is a challenging task due to the spherical geometry and the multimodality of the input, and the inherent uncertainty and diversity of the output.
IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers
More importantly, we demonstrate the flexibility of IconShop with multiple novel icon synthesis tasks, including icon editing, icon interpolation, icon semantic combination, and icon design auto-suggestion.
Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time
Moreover, on the seemingly implausible x16 interpolation task, our method outperforms existing methods by more than 1. 5 dB in terms of PSNR.
Learning a Deep Color Difference Metric for Photographic Images
Most well-established and widely used color difference (CD) metrics are handcrafted and subject-calibrated against uniformly colored patches, which do not generalize well to photographic images characterized by natural scene complexities.
Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective
We aim at advancing blind image quality assessment (BIQA), which predicts the human perception of image quality without any reference information.
Hiding Images in Deep Probabilistic Models
As an instantiation, we adopt a SinGAN, a pyramid of generative adversarial networks (GANs), to learn the patch distribution of one cover image.
Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop
No-reference image quality assessment (NR-IQA) aims to quantify how humans perceive visual distortions of digital images without access to their undistorted references.
Image Quality Assessment: Integrating Model-Centric and Data-Centric Approaches
Learning-based image quality assessment (IQA) has made remarkable progress in the past decade, but nearly all consider the two key components -- model and data -- in isolation.
A Perceptually Optimized and Self-Calibrated Tone Mapping Operator
In Stage two, the input HDR image is self-calibrated to compute the final LDR image.
Perceptual Quality Assessment of Virtual Reality Videos in the Wild
Investigating how people perceive virtual reality (VR) videos in the wild (i. e., those captured by everyday users) is a crucial and challenging task in VR-related applications due to complex authentic distortions localized in space and time.
Measuring Perceptual Color Differences of Smartphone Photographs
Measuring perceptual color differences (CDs) is of great importance in modern smartphone photography.
Steerable Pyramid Transform Enables Robust Left Ventricle Quantification
Predicting cardiac indices has long been a focal point in the medical imaging community.
Pseudocylindrical Convolutions for Learned Omnidirectional Image Compression
We first describe parametric pseudocylindrical representation as a generalization of common pseudocylindrical map projections.
Image Quality Assessment in the Modern Age
This tutorial provides the audience with the basic theories, methodologies, and current progresses of image quality assessment (IQA).
Locally Adaptive Structure and Texture Similarity for Image Quality Assessment
The latest advances in full-reference image quality assessment (IQA) involve unifying structure and texture similarity based on deep representations.
Ranked #34 on
Video Quality Assessment
on MSU SR-QA Dataset
Perceptually Optimized Deep High-Dynamic-Range Image Tone Mapping
We describe a deep high-dynamic-range (HDR) image tone mapping operator that is computationally efficient and perceptually optimized.
Task-Specific Normalization for Continual Learning of Blind Image Quality Models
In this paper, we present a simple yet effective continual learning method for blind image quality assessment (BIQA) with improved quality prediction accuracy, plasticity-stability trade-off, and task-order/-length robustness.
Semi-Supervised Deep Ensembles for Blind Image Quality Assessment
Ensemble methods are generally regarded to be better than a single model if the base learners are deemed to be "accurate" and "diverse."
Troubleshooting Blind Image Quality Models in the Wild
Recently, the group maximum differentiation competition (gMAD) has been used to improve blind image quality assessment (BIQA) models, with the help of full-reference metrics.
Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition
A natural question then arises: Does the superior performance on the closed (and frequently re-used) test sets transfer to the open visual world with unconstrained variations?
Continual Learning for Blind Image Quality Assessment
In this paper, we formulate continual learning for BIQA, where a model learns continually from a stream of IQA datasets, building on what was learned from previously seen data.
Efficiently Troubleshooting Image Segmentation Models with Human-In-The-Loop
Image segmentation lays the foundation for many high-stakes vision applications such as autonomous driving and medical image analysis.
Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild
Nevertheless, due to the distributional shift between images simulated in the laboratory and captured in the wild, models trained on databases with synthetic distortions remain particularly weak at handling realistic distortions (and vice versa).
Perceptual Quality Assessment of Omnidirectional Images as Moving Camera Videos
We first carry out a psychophysical experiment to investigate the interplay among the VR viewing conditions, the user viewing behaviors, and the perceived quality of 360{\deg} images.
Comparison of Image Quality Models for Optimization of Image Processing Systems
The performance of objective image quality assessment (IQA) models has been evaluated primarily by comparing model predictions to human quality judgments.
Image Quality Assessment: Unifying Structure and Texture Similarity
Objective measures of image quality generally operate by comparing pixels of a "degraded" image to those of the original.
Ranked #31 on
Video Quality Assessment
on MSU SR-QA Dataset
Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment
We then seek pairs of images by comparing the baseline model with a set of full-reference IQA methods in gMAD.
I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively
On the other hand, the trained classifiers have traditionally been evaluated on small and fixed sets of test images, which are deemed to be extremely sparsely distributed in the space of all natural images.
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
Intrinsic Image Popularity Assessment
The goal of research in automatic image popularity assessment (IPA) is to develop computational models that can accurately predict the potential of a social image to go viral on the Internet.
Learning to Blindly Assess Image Quality in the Laboratory and Wild
Computational models for blind image quality assessment (BIQA) are typically trained in well-controlled laboratory environments with limited generalizability to realistically distorted images.
Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compression
For the former, we directly apply a CCN to the binarized representation of an image to compute the Bernoulli distribution of each code for entropy estimation.
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.
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.
