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- Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images37
An effective deep learning–based no-reference no-reference IQA model is proposed by utilizing both distortion and aesthetic quality representation and outperforms all other state-of-the-art NR IQA methods on the constructed CGIQA-6k database and other CGIQA-related databases.
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- StableVQA: A Deep No-Reference Quality Assessment Model for Video Stability31
A new database named StableDB is built that contains 1,952 diversely-shaky UGC videos, where each video has a Mean Opinion Score on the degree of video stability rated by 34 subjects and a novel VQA-S model named St stableVQA, which consists of three feature extractors to acquire the optical flow, semantic, and blur features respectively, and a regression layer to predict the final stability score.
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- Benchmarking Multi-dimensional AIGC Video Quality Assessment: A Dataset and Unified Model27
The Unify Generated Video Quality Assessment (UGVQ) model is proposed, designed to accurately evaluate the multi-dimensional quality of AIGC videos, and established a benchmark for evaluating existing quality assessment metrics on the LGVQ dataset.
- Enhancing blind video quality assessment with rich quality-aware features27
This paper proposes Rich Quality-aware features enabled Video Quality Assessment (RQ-VQA), a simple yet effective method to enhance BVQA by leveraging rich quality-aware features extracted from off-the-shelf BIQA and BVQA models.
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- Fine localization and distortion resistant detection of multi-class barcode in complex environments22
A region-based end-to-end network to finely localize and classify 1D barcode and Quick Response (QR) code in complex environments is proposed and it is demonstrated that the method can resist some distortions by simulating barcode images of different image qualities.
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- Human-Activity AGV Quality Assessment: A Benchmark Dataset and an Objective Evaluation Metric20
An objective evaluation metric, named AI-Generated Human activity Video Quality metric (GHVQ), is developed, to automatically analyze the quality of human activity AGVs, and outperforms existing quality metrics on the Human-AGVQA dataset by a large margin.
- Deep Portrait Quality Assessment. A NTIRE 2024 Challenge Survey20
The NTIRE 2024 Portrait Quality Assessment Challenge is reviewed, highlighting the proposed solutions and results, and providing a gauge for the current state-of-the-art in Portrait Quality Assessment.
- 3DGCQA: A Quality Assessment Database for 3D AI-Generated Contents19
This paper introduces a novel 3DGC quality assessment dataset, 3DGCQA, built using 7 representative Text-to-3D generation methods, and reveals the presence of 6 common distortion categories in the generated 3DGCs.
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- SingingHead: A Large-Scale 4D Dataset for Singing Head Animation16
A simple but efficient non-autoregressive VAE-based framework with background music as an input signal to generate diverse and accurate 3D singing facial motions in real time and demonstrates the significance of the SingingHead dataset in promoting the development of singing head animation.
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- VQAThinker: Exploring Generalizable and Explainable Video Quality Assessment via Reinforcement Learning15
VQAThinker is proposed, a reasoning-based VQA framework that leverages large multimodal models (LMMs) with reinforcement learning to jointly model video quality understanding and scoring, emulating human perceptual decision-making and demonstrating that reinforcement learning offers an effective pathway toward building generalizable and explainable VQA models solely with score-level supervision.
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- BAND-2k: Banding Artifact Noticeable Database for Banding Detection and Quality Assessment14
An effective no-reference (NR) banding evaluator is developed for banding detection and quality assessment by leveraging frequency characteristics of banding artifacts, and a dual convolutional neural network is employed to concurrently learn the feature representation from the high-frequency and low-frequency maps.
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- 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.
- Text2QR: Harmonizing Aesthetic Customization and Scanning Robustness for Text-Guided QR Code Generation12
The QR Aesthetic Blueprint module is introduced, generating a blueprint image exerting control over the entire generation process, and the Scannability Enhancing Latent Refinement (SELR) process refines the output iteratively in the latent space, enhancing scanning robustness.
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- Deep Learning for Search and Recommender Systems in Practice11
This talk will go over the components of personalized search and recommender systems and demonstrate the applications of various deep learning techniques along the way and hope the audience would gain some first-hand experience building a personalized search/recommender system usingDeep learning techniques.
- CompressedVQA-HDR: Generalized Full-reference and No-reference Quality Assessment Models for Compressed High Dynamic Range Videos10
This paper adopts the Swin Transformer and SigLip 2 as the backbone networks for the proposed full-reference and no-reference VQA models, respectively, and achieves state-of-the-art performance compared to existing FR and NR VQA models.
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- RAWIW: RAW Image Watermarking robust to ISP pipeline9
The first deep learning-based RAW Image Watermarking (RAWIW) framework for copyright protection is proposed, which directly embed copyright information into RAW images, which can be later extracted from the corresponding RGB images generated by different post-processing methods.
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- AI-integrated IQPD framework of quality prediction and diagnostics in small-sample multi-unit pharmaceutical manufacturing: Advancing from experience-driven to data-driven manufacturing5
A novel path-enhanced double ensemble quality prediction model (PeDGAT) is proposed, which combines a graph attention network and path information to encode inter-unit long-range and sequential dependencies and enhances model stability in small samples.
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- Who is a Better Talker: Subjective and Objective Quality Assessment for AI-Generated Talking Heads4
This paper presents the largest AGTH quality assessment dataset THQA-10K to date, which selects 12 prominent T2I models and 14 advanced talkers to generate AGTHs for 14 prompts, and proposes an objective quality assessment method based on the first frame, Y-T slice and tonelip consistency.
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- Subjective and Objective Quality-of-Experience Evaluation Study for Live Video Streaming2
An end-to-end QoE evaluation model is proposed, Tao-QoE, which implicitly encodes statistical Quality of Service (QoS) features, integrates multi-scale semantic features and optical flow-based motion features to predict a retrospective QoE score.
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- Hidden Barcode in Sub-Images with Invisible Locating Marker1
Human perceptual characteristics are considered and an optimal hiding region recommendation algorithm that comprehensively incorporates Just Noticeable Difference and visual saliency factors into consideration is introduced that comprehensively incorporates Just Noticeable Difference and visual saliency factors into consideration.
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- Perceptual Quality Assessment for Video Frame Interpolation1
A no-reference perceptual quality assessment method that applies a triplet network architecture to extract the deep perceptual features of the interpolated frame as well as the original pair of frames incorporating the original pair of VFI inputs is proposed.
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- Research on IACA of distribution network fault location1
The switch function and evaluation function of distribution network fault location are discussed and the IACA(Improvement Ant Colony Algorithm) method is proposed to solve this problem.
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- A Large-Scale VR Panoramic Dataset of QR Code and Improved Detecting Algorithm–
A large-scale panoramic QR code dataset that is by far the largest dataset in terms of image quantity and a QR code detecting approach in a complex environment based on deep learning improving accuracy of QR code detection are proposed.
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- The Research and Implementation of Business-end Service Composition System–
A Web services based dynamic workflow model is proposed to raise the abstraction level of service composition by extending the traditional workflow model with service concepts to facilitate and experience business process integration based on J2EE.
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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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