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Works33 from public data
- Exploring Rich Subjective Quality Information for Image Quality Assessment in the Wild136
RichIQA outperforms state-of-the-art competitors on multiple large-scale in the wild IQA databases with rich subjective rating labels and can fully exploit the potentials within subjective quality rating and enhance the prediction performance and generalizability of the network.
- Attention-Guided Neural Networks for Full-Reference and No-Reference Audio-Visual Quality Assessment126
- 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.
- NTIRE 2024 Quality Assessment of AI-Generated Content Challenge54
This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC).
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- VDPVE: VQA Dataset for Perceptual Video Enhancement31
A Video quality assessment dataset for Perceptual Video Enhancement (VDPVE), which has 1211 videos with different enhancements, is constructed and the performance of several state-of-the-art video quality assessment methods on the test set of the VDPVE is verified.
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- AGAV-Rater: Adapting Large Multimodal Model for AI-Generated Audio-Visual Quality Assessment15
This work introduces AGAVQA-3k, the first large-scale AGAV quality assessment dataset, and proposes AGAV-Rater, a LMM-based model that can score AGAVs, as well as audio and music generated from text, across multiple dimensions, and selects the best AGAV generated by VTA methods to present to the user.
- AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results13
This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC), to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos.
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- UNQA: Unified No-Reference Quality Assessment for Audio, Image, Video, and Audio-Visual Content12
The Unified No-reference Quality Assessment model (UNQA) is proposed, which tries to train a single QA model across different media modalities, and the multi-modality training strategy makes the QA model learn more general and robust quality-aware feature representation as evidenced by the superior performance of UNQA compared to state-of-the-art QA methods.
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- Multi-Dimensional Text-to-Face Image Quality Assessment Using LLM: Database and Method9
The Fine -grained Text-to-Face Image Quality Assessment (FineTFIQA) database is introduced, which is designed to evaluate the ability of T2I models to generate fine-grained face images and significantly outperforms existing methods on all evaluation dimensions.
- Audio-Visual Quality Assessment for User Generated Content: Database and Method9
The first UGC AVQA database named the SJTU-UAV database is constructed, which includes 520 in-the-wild UGC audio and video (A/V) sequences, and the experimental results show that with the help of audio signals, the VQA models can evaluate the perceptual quality more accurately.
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- Engagement Prediction of Short Videos with Large Multimodal Models7
This work empirically investigate the potential of large multimodal models (LMMs) for video engagement prediction by ensembling two representative LMMs: VideoLLaMA2, which integrates audio, visual, and language modalities, and Qwen2.5-VL, which models only visual and language modalities.
- Ges-QA: A Multidimensional Quality Assessment Dataset for Audio-to-3D Gesture Generation6
A multi-modal transformer-based neural network with 3 branches for video, audio and 3D skeleton modalities, which can score A2G contents in multiple dimensions is proposed and yields state-of-the-art performance on the Ges-QA dataset.
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- Surveillance Facial Image Quality Assessment: A Multi-Dimensional Dataset and Lightweight Model3
This work proposes SFIQA-Assessor, a lightweight multi-task FIQA model that jointly exploits complementary facial views through cross-view feature interaction, and employs learnable task tokens to guide the unified regression of multiple quality dimensions, validating its effectiveness for real-world surveillance applications.
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- Mitigating Low-Level Visual Hallucinations Requires Self-Awareness: Database, Model, and Training Strategy3
The HLPU instruction database is introduced, the first instruction database specifically focused on hallucinations in low-level vision tasks are introduced, and the Self-Awareness Failure Elimination (SAFEQA) model is proposed, which utilizes image features, salient region features and quality features to improve the perception and comprehension abilities of the model in low-level vision tasks.
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- VidAudio-Bench: Benchmarking V2A and VT2A Generation across Four Audio Categories1
VidAudio-Bench is established as a comprehensive and scalable framework for diagnosing V2A systems and new insights into multimodal audio generation are provided.
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Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. 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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