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Found 30 papers, 22 papers with code
Embodied Image Quality Assessment for Robotic Intelligence
Image quality assessment (IQA) of user-generated content (UGC) is a critical technique for human quality of experience (QoE).
VQA$^2$: Visual Question Answering for Video Quality Assessment
To address this gap, we introduce the VQA2 Instruction Dataset - the first visual question answering instruction dataset that focuses on video quality assessment.
R-Bench: Are your Large Multimodal Model Robust to Real-world Corruptions?
However, various corruptions in the real world mean that images will not be as ideal as in simulations, presenting significant challenges for the practical application of LMMs.
Q-Bench-Video: Benchmarking the Video Quality Understanding of LMMs
With the rising interest in research on Large Multi-modal Models (LMMs) for video understanding, many studies have emphasized general video comprehension capabilities, neglecting the systematic exploration into video quality understanding.
Quality Assessment in the Era of Large Models: A Survey
Quality assessment, which evaluates the visual quality level of multimedia experiences, has garnered significant attention from researchers and has evolved substantially through dedicated efforts.
Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model
To bridge this gap, we propose the Unify Generated Video Quality assessment (UGVQ) model, designed to accurately evaluate the multi-dimensional quality of AIGC videos.
HazeCLIP: Towards Language Guided Real-World Image Dehazing
Combined with a region-specific dehazing technique and tailored prompt sets, the CLIP model accurately identifies hazy areas, providing a high-quality, human-like prior that guides the fine-tuning process of pre-trained networks.
CMC-Bench: Towards a New Paradigm of Visual Signal Compression
With the development of Large Multimodal Models (LMMs), a Cross Modality Compression (CMC) paradigm of Image-Text-Image has emerged.
A-Bench: Are LMMs Masters at Evaluating AI-generated Images?
How to accurately and efficiently assess AI-generated images (AIGIs) remains a critical challenge for generative models.
Light-VQA+: A Video Quality Assessment Model for Exposure Correction with Vision-Language Guidance
Equally important to the VEC is the Video Quality Assessment (VQA).
G-Refine: A General Quality Refiner for Text-to-Image Generation
Based on the mechanisms of the Human Visual System (HVS) and syntax trees, the first two indicators can respectively identify the perception and alignment deficiencies, and the last module can apply targeted quality enhancement accordingly.
LMM-PCQA: Assisting Point Cloud Quality Assessment with LMM
Although large multi-modality models (LMMs) have seen extensive exploration and application in various quality assessment studies, their integration into Point Cloud Quality Assessment (PCQA) remains unexplored.
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge
A total of 196 participants have registered in the video track.
AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
The performance of the top-5 submissions is reviewed and provided here as a survey of diverse deep models for efficient video quality assessment of user-generated content.
AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment
With the rapid advancements in AI-Generated Content (AIGC), AI-Generated Images (AIGIs) have been widely applied in entertainment, education, and social media.
Subjective-Aligned Dataset and Metric for Text-to-Video Quality Assessment
Based on T2VQA-DB, we propose a novel transformer-based model for subjective-aligned Text-to-Video Quality Assessment (T2VQA).
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.
MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Model
With the evolution of storage and communication protocols, ultra-low bitrate image compression has become a highly demanding topic.
Q-Refine: A Perceptual Quality Refiner for AI-Generated Image
With the rapid evolution of the Text-to-Image (T2I) model in recent years, their unsatisfactory generation result has become a challenge.
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
Q-Boost: On Visual Quality Assessment Ability of Low-level Multi-Modality Foundation Models
Recent advancements in Multi-modality Large Language Models (MLLMs) have demonstrated remarkable capabilities in complex high-level vision tasks.
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-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.
Advancing Zero-Shot Digital Human Quality Assessment through Text-Prompted Evaluation
To address this gap, we propose SJTU-H3D, a subjective quality assessment database specifically designed for full-body digital humans.
GMS-3DQA: Projection-based Grid Mini-patch Sampling for 3D Model Quality Assessment
Model-based 3DQA methods extract features directly from the 3D models, which are characterized by their high degree of complexity.
AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment
With the rapid advancements of the text-to-image generative model, AI-generated images (AGIs) have been widely applied to entertainment, education, social media, etc.
A Perceptual Quality Assessment Exploration for AIGC Images
\underline{AI} \underline{G}enerated \underline{C}ontent (\textbf{AIGC}) has gained widespread attention with the increasing efficiency of deep learning in content creation.
A real-time blind quality-of-experience assessment metric for HTTP adaptive streaming
In today's Internet, HTTP Adaptive Streaming (HAS) is the mainstream standard for video streaming, which switches the bitrate of the video content based on an Adaptive BitRate (ABR) algorithm.
A Survey on Dynamic Network Embedding
Real-world networks are composed of diverse interacting and evolving entities, while most of existing researches simply characterize them as particular static networks, without consideration of the evolution trend in dynamic networks.
Social and Information Networks Physics and Society
Contractibility of space of stability conditions on the projective plane via global dimension function
We compute the global dimension function $\mathrm{gldim}$ on the principal component $\mathrm{Stab}^{\dag}(\mathbb{P}^2)$ of the space of Bridgeland stability conditions on $\mathbb{P}^2$.
Algebraic Geometry General Topology Representation Theory 2020: 14F08 (18G80, 32Q55)
