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- Xiaokang YangSuggested from co-authorship
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Works417 from public data
- Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels664
The proposed Q-Align achieves state-of-the-art performance on image quality assessment (IQA), image aesthetic assessment (IAA), as well as video quality assessment (VQA) tasks under the original LMM structure and unify the three tasks into one model, termed the OneAlign.
- Perceptual image quality assessment: a survey660
This survey provides a general overview of classical algorithms and recent progresses in the field of perceptual image quality assessment and describes the performances of the state-of-the-art quality measures for visual signals.
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- Unified Blind Quality Assessment of Compressed Natural, Graphic, and Screen Content Images340
A unified content-type adaptive (UCA) blind image quality assessment model that is applicable across content types and leads to superior performance on the constructed CCT database, and is training-free, implying strong generalizability.
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- A Deep Learning based No-reference Quality Assessment Model for UGC Videos308
A very simple but effective UGC VQA model is proposed, which tries to address this problem by training an end-to-end spatial feature extraction network to directly learn the quality-aware spatial feature representation from raw pixels of the video frames.
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- AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment263
This work extensively considers various popular AGI models, generated AGI through different prompts and model parameters, and collected subjective scores at the perceptual quality and text-to-image alignment, thus building the most comprehensive AGI subjective quality database AGIQA-3K so far.
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- Screen Content Quality Assessment: Overview, Benchmark, and Beyond250
The background, history, recent progress, and future of the emerging screen content quality assessment research are provided, including an overview of the most technology-oriented part of QoE modeling.
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- Blind Quality Assessment for in-the-Wild Images via Hierarchical Feature Fusion and Iterative Mixed Database Training213
A novel BIQA model for in-the-wild images is proposed by addressing two critical problems in this field: how to learn better quality-aware feature representation, and how to solve the problem of insufficient training samples in terms of their content and distortion diversity.
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- Perceptual video quality assessment: a survey211
This survey provides a systematic overview of both classical works and recent progress in the realm of video quality assessment, which can help other researchers quickly access the field and conduct relevant research.
- No-Reference Quality Assessment for 3D Colored Point Cloud and Mesh Models194
A no-reference (NR) quality assessment metric for colored 3D models represented by both point cloud and mesh is proposed and greatly reduces the performance gap with the state-of-the-art FR 3D-QA metrics.
- Perceptual Quality Assessment of Omnidirectional Images171
A perceptual omnidirectional image quality assessment (IQA) study since it is extremely important to provide a good quality of experience under the VR environment, and some new observations different from traditional IQA are made.
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- The prediction of head and eye movement for 360 degree images140
This paper studies the problem of predicting head movement, head–eye motion, and scanpath of viewers when they are watching 360 degree images in the commodity HMDs and designs a model to predict the saliency maps for the first two, and the scanpaths for the last one.
- Attention-Guided Neural Networks for Full-Reference and No-Reference Audio-Visual Quality Assessment126
- A Perceptual Quality Assessment Exploration for AIGC Images122
The first perceptual AGI quality assessment database, AGIQA-1K, which consists of 1,080 AGIs generated from diffusion models is presented and a benchmark experiment is conducted to evaluate the performance of current image quality assessment (IQA) models.
- A dataset of eye movements for the children with autism spectrum disorder120
Based on this dataset, researchers could analyze the visual traits of children with ASD and design specialized visual attention models to promote research in related fields, as well as design specialized models to identify the individuals with ASD.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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