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Found 33 papers, 24 papers with code
TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment
Our approach to IQA involves the design of a heuristic coarse-to-fine network (CFANet) that leverages multi-scale features and progressively propagates multi-level semantic information to low-level representations in a top-down manner.
Ranked #11 on
Video Quality Assessment
on MSU SR-QA Dataset
Blind Face Restoration via Deep Multi-scale Component Dictionaries
Next, with the degraded input, we match and select the most similar component features from their corresponding dictionaries and transfer the high-quality details to the input via the proposed dictionary feature transfer (DFT) block.
Progressive Semantic-Aware Style Transformation for Blind Face Restoration
Compared with previous networks, the proposed PSFR-GAN makes full use of the semantic (parsing maps) and pixel (LQ images) space information from different scales of input pairs.
Ranked #4 on
Blind Face Restoration
on CelebA-Test
FAST-VQA: Efficient End-to-end Video Quality Assessment with Fragment Sampling
Consisting of fragments and FANet, the proposed FrAgment Sample Transformer for VQA (FAST-VQA) enables efficient end-to-end deep VQA and learns effective video-quality-related representations.
Ranked #3 on
Video Quality Assessment
on LIVE-VQC
(using extra training data)
Neighbourhood Representative Sampling for Efficient End-to-end Video Quality Assessment
On the other hand, existing practices, such as resizing and cropping, will change the quality of original videos due to the loss of details and contents, and are therefore harmful to quality assessment.
Ranked #2 on
Video Quality Assessment
on KoNViD-1k
(using extra training data)
Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives
In light of this, we propose the Disentangled Objective Video Quality Evaluator (DOVER) to learn the quality of UGC videos based on the two perspectives.
Ranked #1 on
Video Quality Assessment
on LIVE-VQC
Learning Spatial Attention for Face Super-Resolution
Visualization of the attention maps shows that our spatial attention network can capture the key face structures well even for very low resolution faces (e. g., $16\times16$).
Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors
Unlike image-space methods, our FeMaSR restores HR images by matching distorted LR image {\it features} to their distortion-free HR counterparts in our pretrained HR priors, and decoding the matched features to obtain realistic HR images.
Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision
To address this gap, we present Q-Bench, a holistic benchmark crafted to systematically evaluate potential abilities of MLLMs on three realms: low-level visual perception, low-level visual description, and overall visual quality assessment.
Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models
Multi-modality foundation models, as represented by GPT-4V, have brought a new paradigm for low-level visual perception and understanding tasks, that can respond to a broad range of natural human instructions in a model.
Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
The explosion of visual content available online underscores the requirement for an accurate machine assessor to robustly evaluate scores across diverse types of visual contents.
Ranked #1 on
Video Quality Assessment
on LIVE-FB LSVQ
From Face to Natural Image: Learning Real Degradation for Blind Image Super-Resolution
Notably, LQ face images, which may have the same degradation process as natural images, can be robustly restored with photo-realistic textures by exploiting their strong structural priors.
HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset
Secondly, we conduct more sophisticated alignment and temporal fusion in the feature space of the coarse HDR video to produce better reconstruction.
Semi-Supervised Learning for Face Sketch Synthesis in the Wild
Instead of supervising the network with ground truth sketches, we first perform patch matching in feature space between the input photo and photos in a small reference set of photo-sketch pairs.
Ranked #1 on
Face Sketch Synthesis
on CUHK
Face Sketch Synthesis with Style Transfer using Pyramid Column Feature
We utilize a fully convolutional neural network (FCNN) to create the content image, and propose a style transfer approach to introduce textures and shadings based on a newly proposed pyramid column feature.
Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach
Though subjective studies have collected overall quality scores for these videos, how the abstract quality scores relate with specific factors is still obscure, hindering VQA methods from more concrete quality evaluations (e. g. sharpness of a video).
Iterative Token Evaluation and Refinement for Real-World Super-Resolution
Distortion removal involves simple HQ token prediction with LQ images, while texture generation uses a discrete diffusion model to iteratively refine the distortion removal output with a token refinement network.
Enhancing Diffusion Models with Text-Encoder Reinforcement Learning
While fine-tuning the U-Net can partially improve performance, it remains suffering from the suboptimal text encoder.
A Unified Framework for Masked and Mask-Free Face Recognition via Feature Rectification
We introduce rectification blocks to rectify features extracted by a state-of-the-art recognition model, in both spatial and channel dimensions, to minimize the distance between a masked face and its mask-free counterpart in the rectified feature space.
Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion
Recent learning-based video quality assessment (VQA) algorithms are expensive to implement due to the cost of data collection of human quality opinions, and are less robust across various scenarios due to the biases of these opinions.
Towards Robust Text-Prompted Semantic Criterion for In-the-Wild Video Quality Assessment
The proliferation of videos collected during in-the-wild natural settings has pushed the development of effective Video Quality Assessment (VQA) methodologies.
MIMO Is All You Need : A Strong Multi-In-Multi-Out Baseline for Video Prediction
The mainstream of the existing approaches for video prediction builds up their models based on a Single-In-Single-Out (SISO) architecture, which takes the current frame as input to predict the next frame in a recursive manner.
DisCoVQA: Temporal Distortion-Content Transformers for Video Quality Assessment
Based on prominent time-series modeling ability of transformers, we propose a novel and effective transformer-based VQA method to tackle these two issues.
Ranked #5 on
Video Quality Assessment
on KoNViD-1k
Exploring the Effectiveness of Video Perceptual Representation in Blind Video Quality Assessment
Experiments show that the perceptual representation in the HVS is an effective way of predicting subjective temporal quality, and thus TPQI can, for the first time, achieve comparable performance to the spatial quality metric and be even more effective in assessing videos with large temporal variations.
SAFE: Scale Aware Feature Encoder for Scene Text Recognition
We propose a novel scale aware feature encoder (SAFE) that is designed specifically for encoding characters with different scales.
Char-Net: A Character-Aware Neural Network for Distorted Scene Text Recognition
Unlike previous work which employed a global spatial transformer network to rectify the entire distorted text image, we take an approach of detecting and rectifying individual characters.
PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo
It then jointly optimizes the surface normals, spatially-varying BRDFs, and lights based on a shadow-aware differentiable rendering layer.
S$^3$-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint
Different from existing single-view methods which can only recover a 2. 5D scene representation (i. e., a normal / depth map for the visible surface), our method learns a neural reflectance field to represent the 3D geometry and BRDFs of a scene.
NTIRE 2023 Quality Assessment of Video Enhancement Challenge
61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions.
Semi-supervised Cycle-GAN for face photo-sketch translation in the wild
Experiments show that SCG achieves competitive performance on public benchmarks and superior results on photos in the wild.
Local Distortion Aware Efficient Transformer Adaptation for Image Quality Assessment
Further, we propose a local distortion extractor to obtain local distortion features from the pretrained CNN and a local distortion injector to inject the local distortion features into ViT.
Towards Open-ended Visual Quality Comparison
Comparative settings (e. g. pairwise choice, listwise ranking) have been adopted by a wide range of subjective studies for image quality assessment (IQA), as it inherently standardizes the evaluation criteria across different observers and offer more clear-cut responses.
