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- Guangtao ZhaiSuggested from co-authorship
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Works134 from public data
- 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.
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- 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.
- 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.
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- Combination of near-infrared and thermal imaging techniques for the remote and simultaneous measurements of breathing and heart rates under sleep situation104
The overall performance of the proposed technique is acceptable for BR and HR estimations during nighttime and almost all the data points located within their own 95% limits of agreement are demonstrated.
- End-to-End Human-Gaze-Target Detection with Transformers96
Unlike existing two-stage methods that require human head locations as input and can predict only one human's gaze target at a time, HGTTR can directly predict the locations of all people and their gaze targets at one time in an end-to-end manner.
- Confusing Image Quality Assessment: Toward Better Augmented Reality Experience84
This paper considers AR technology as the superimposition of virtual scenes and real scenes, and introduces visual confusion as its basic theory, and establishes an ARIQA database to better simulate the real AR application scenarios.
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- Large multimodal models evaluation: a survey57
By unifying general-specialized understanding and modality-specific generation evaluations, this survey clarifies the current landscape and provides guidance for future research in the LMM evaluation field.
- Visual Attention Analysis and Prediction on Human Faces for Children with Autism Spectrum Disorder55
The proposed model shows the best performance among all competitors and could be used to design specialized education contents containing human faces for the children with ASD or produce the specific model for rapidly screening ASD using their eye movement data.
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- FineVQ: Fine-Grained User Generated Content Video Quality Assessment53
The first large-scale Fine-grained Video quality assessment Database is established, termed FineVD, which comprises 6104 UGC videos with fine-grained quality scores and descriptions across multiple dimensions, and a Fine-grained Video Quality assessment (FineVQ) model is proposed to learn the fine-grained quality of UGC videos.
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- AIGV-Assessor: Benchmarking and Evaluating the Perceptual Quality of Text-to-Video Generation with LMM46
A novel VQA model that leverages spatiotemporal features and LMM frameworks to capture the intricate quality attributes of AIGVs, thereby accurately predicting precise video quality scores and video pair preferences is introduced and demonstrates state-of-the-art performance.
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- Quality Assessment for AI Generated Images With Instruction Tuning43
The proposed MINT-IQA model achieves state-of-the-art performance in understanding and evaluating human visual preferences for AIGIs, and the proposed model also achieves competing results on traditional IQA tasks compared with state-of-the-art IQA models.
- Saliency in Augmented Reality43
To better predict the saliency in AR, a vector quantized saliency prediction method is proposed and generalize it for ARSaliency prediction, and experimental results demonstrate the superiority of the proposed method on both of the common saliency predictions problem and the AR saliency Prediction problem over benchmark methods.
- LMM4LMM: Benchmarking and Evaluating Large-Multimodal Image Generation With LMMs42
Extensive experimental results show that LMM4LMM achieves state-of-the-art performance on EvalMi-50K, and exhibits strong generalization ability on other AI-generated image evaluation benchmark datasets, manifesting the generality of both the EvalMi-50K dataset and LMM4LMM metric.
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- MMHead: Towards Fine-grained Multi-modal 3D Facial Animation34
A simple but efficient VQ-VAE-based method named MM2Face is proposed to unify the multi-modal information and generate diverse and plausible 3D facial motions, which achieves competitive results on both benchmarks.
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- LMM4Edit: Benchmarking and Evaluating Multimodal Image Editing with LMMs27
This work introduces EBench-18K, the first large-scale image Editing Benchmark including 18K edited images with fine-grained human preference annotations for evaluating TIE, and proposes LMM4Edit, a LMM-based metric for evaluating image Editing models from perceptual quality, editing alignment, attribute preservation, and task-specific QA accuracy in an all-in-one manner.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-14. 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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