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Image Quality Assessment
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/wangjiarui153/aigciqa2023
공식 코드https://github.com/wangpuyi/ma-agiqa
공식 코드Large Multi-modality Model Assisted AI-Generated Image Quality Assessment
https://github.com/wangzhihua520/SemiNCL_IQA
공식 코드Semi-Supervised Deep Ensembles for Blind Image Quality Assessment
https://github.com/weizhou-geek/SFSN
공식 코드Image Super-Resolution Quality Assessment: Structural Fidelity Versus Statistical Naturalness
https://github.com/weizhou-geek/srif
공식 코드Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical Fidelity
https://github.com/wenjuing/iqcaption360
공식 코드Omnidirectional Image Quality Captioning: A Large-scale Database and A New Model
https://github.com/wenjuing/max360iq
공식 코드Max360IQ: Blind Omnidirectional Image Quality Assessment with Multi-axis Attention
https://github.com/wkabbani/sclera-segmentation
공식 코드Robust Sclera Segmentation for Skin-tone Agnostic Face Image Quality Assessment
https://github.com/x1716/iqa-adapter
공식 코드https://github.com/xfetus/miua2022
공식 코드https://github.com/xialeiliu/RankIQA
공식 코드RankIQA: Learning from Rankings for No-reference Image Quality Assessment
https://github.com/xiangjiesui/gsr
공식 코드Perceptual Quality Assessment of 360$^\circ$ Images Based on Generative Scanpath Representation
https://github.com/xiaoqiwang/glintiqa
공식 코드https://github.com/xinliyue/vqa-generalization
공식 코드Revisiting Video Quality Assessment from the Perspective of Generalization
https://github.com/xk-huang/OrdinalCLIP
공식 코드OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression
https://github.com/yangid/defenseiqa-nt
공식 코드https://github.com/yangli-xjtu/SDS
공식 코드Saliency detection based on structural dissimilarity induced by image quality assessment model
https://github.com/yanglixiaoshen/SAP-Net
공식 코드https://github.com/yeppp27/q-adapt
공식 코드Q-Adapt: Adapting LMM for Visual Quality Assessment with Progressive Instruction Tuning
https://github.com/ytian73/aigi-vc
공식 코드AI-generated Image Quality Assessment in Visual Communication
https://github.com/yukexing/3dgs-ieval-15k
공식 코드3DGS-IEval-15K: A Large-scale Image Quality Evaluation Database for 3D Gaussian-Splatting
https://github.com/yuqing-liu-dut/nriqa_sr
공식 코드Textural-Perceptual Joint Learning for No-Reference Super-Resolution Image Quality Assessment
https://github.com/yuyi-sd/baiqa
공식 코드Backdoor Attacks against No-Reference Image Quality Assessment Models via a Scalable Trigger
https://github.com/zhengchen1999/grounding-iqa
공식 코드Grounding-IQA: Multimodal Language Grounding Model for Image Quality Assessment
https://github.com/zhengchen1999/ntire2025_realworld_face_restoration
공식 코드NTIRE 2025 Challenge on Real-World Face Restoration: Methods and Results
https://github.com/zht8506/uniqa
공식 코드UniQA: Unified Vision-Language Pre-training for Image Quality and Aesthetic Assessment
https://github.com/zhwzhong/Guided-Depth-Map-Super-resolution-A-Survey
공식 코드https://github.com/zijianchen98/band-2k
공식 코드BAND-2k: Banding Artifact Noticeable Database for Banding Detection and Quality Assessment
https://github.com/zwx8981/BIQA_CL
공식 코드https://github.com/zwx8981/BVQA-2021
공식 코드Blindly Assess Quality of In-the-Wild Videos via Quality-aware Pre-training and Motion Perception
https://github.com/zwx8981/DBCNN-PyTorch
공식 코드Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network
https://github.com/zwx8981/PerceptualAttack_BIQA
공식 코드Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop
https://github.com/zwx8981/UNIQUE
공식 코드Uncertainty-Aware Blind Image Quality Assessment in the Laboratory and Wild
https://github.com/zwx8981/liqe
공식 코드Blind Image Quality Assessment via Vision-Language Correspondence: A Multitask Learning Perspective
https://github.com/zwx8981/tsn-iqa
공식 코드Task-Specific Normalization for Continual Learning of Blind Image Quality Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.