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Found 26 papers, 10 papers with code
AesExpert: Towards Multi-modality Foundation Model for Image Aesthetics Perception
To address the above challenge, we first introduce a comprehensively annotated Aesthetic Multi-Modality Instruction Tuning (AesMMIT) dataset, which serves as the footstone for building multi-modality aesthetics foundation models.
Diffusion Model Based Visual Compensation Guidance and Visual Difference Analysis for No-Reference Image Quality Assessment
Firstly, we devise a new diffusion restoration network that leverages the produced enhanced image and noise-containing images, incorporating nonlinear features obtained during the denoising process of the diffusion model, as high-level visual information.
AesBench: An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perception
An obvious obstacle lies in the absence of a specific benchmark to evaluate the effectiveness of MLLMs on aesthetic perception.
Deep Shape-Texture Statistics for Completely Blind Image Quality Evaluation
The perceptual quality is quantified by the variant Mahalanobis Distance between the inner and outer Shape-Texture Statistics (DSTS), wherein the inner and outer statistics respectively describe the quality fingerprints of the distorted image and natural images.
Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attention
The core of MIA-VSR is leveraging feature-level temporal continuity between adjacent frames to reduce redundant computations and make more rational use of previously enhanced SR features.
Dehazed Image Quality Evaluation: From Partial Discrepancy to Blind Perception
Image dehazing aims to restore spatial details from hazy images.
HVS Revisited: A Comprehensive Video Quality Assessment Framework
Based on the revisited HVS, a no-reference VQA framework called HVS-5M (NRVQA framework with five modules simulating HVS with five characteristics) is proposed.
Ranked #6 on
Video Quality Assessment
on LIVE-FB LSVQ
Ecsnet: Spatio-temporal feature learning for event camera
To fully exploit their inherent sparsity with reconciling the spatio-temporal information, we introduce a compact event representation, namely 2D-1T event cloud sequence (2D-1T ECS).
Ranked #1 on
Event data classification
on N-CARS
Seeking Subjectivity in Visual Emotion Distribution Learning
In psychology, the \textit{Object-Appraisal-Emotion} model has demonstrated that each individual's emotion is affected by his/her subjective appraisal, which is further formed by the affective memory.
Personalized Image Aesthetics Assessment with Rich Attributes
To solve the dilemma, we conduct so far, the most comprehensive subjective study of personalized image aesthetics and introduce a new Personalized image Aesthetics database with Rich Attributes (PARA), which consists of 31, 220 images with annotations by 438 subjects.
Robust Depth Completion with Uncertainty-Driven Loss Functions
In this work, we introduce uncertainty-driven loss functions to improve the robustness of depth completion and handle the uncertainty in depth completion.
SOLVER: Scene-Object Interrelated Visual Emotion Reasoning Network
Inspired by this, we propose a novel Scene-Object interreLated Visual Emotion Reasoning network (SOLVER) to predict emotions from images.
StarVQA: Space-Time Attention for Video Quality Assessment
To capture the long-range spatiotemporal dependencies of a video sequence, StarVQA encodes the space-time position information of each patch to the input of the Transformer.
A Circular-Structured Representation for Visual Emotion Distribution Learning
Visual Emotion Analysis (VEA) has attracted increasing attention recently with the prevalence of sharing images on social networks.
Generalizable No-Reference Image Quality Assessment via Deep Meta-learning
Based on these two task sets, an optimization-based meta-learning is proposed to learn the generalized NR-IQA model, which can be directly used to evaluate the quality of images with unseen distortions.
Searching Efficient Model-guided Deep Network for Image Denoising
Similar to the success of NAS in high-level vision tasks, it is possible to find a memory and computationally efficient solution via NAS with highly competent denoising performance.
A new communication paradigm: from bit accuracy to semantic fidelity
In this article, we deploy semantics to solve the spectrum and power bottleneck and propose a first understanding and then transmission framework with high semantic fidelity.
Networking and Internet Architecture
Unsupervised Curriculum Domain Adaptation for No-Reference Video Quality Assessment
From this adaptation, we split the data in target domain into confident and uncertain subdomains using the proposed uncertainty-based ranking function, through measuring their prediction confidences.
Cuid: A new study of perceived image quality and its subjective assessment
It is thus critical to acquire reliable subjective data with controlled perception experiments that faithfully reflect human behavioural responses to distortions in visual signals.
Accurate and Lightweight Image Super-Resolution with Model-Guided Deep Unfolding Network
Deep neural networks (DNNs) based methods have achieved great success in single image super-resolution (SISR).
Personalized Image Aesthetics Assessment via Meta-Learning With Bilevel Gradient Optimization
Typical image aesthetics assessment (IAA) is modeled for the generic aesthetics perceived by an ``average'' user.
MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment
The underlying idea is to learn the meta-knowledge shared by human when evaluating the quality of images with various distortions, which can then be adapted to unknown distortions easily.
PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression
By optimizing the PCR loss, PDANet can generate a polarity preserved attention map and thus improve the emotion regression performance.
Incremental Few-Shot Learning for Pedestrian Attribute Recognition
Pedestrian attribute recognition has received increasing attention due to its important role in video surveillance applications.
Semantic Adversarial Network for Zero-Shot Sketch-Based Image Retrieval
Zero-shot sketch-based image retrieval (ZS-SBIR) is a specific cross-modal retrieval task for retrieving natural images with free-hand sketches under zero-shot scenario.
Image Quality Assessment Guided Deep Neural Networks Training
For many computer vision problems, the deep neural networks are trained and validated based on the assumption that the input images are pristine (i. e., artifact-free).
