Papers with Code 아카이브
Aesthetics Quality Assessment
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 17
Automatic assessment of aesthetic-related subjective ratings.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Li-Wanhua/POEs
공식 코드Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression
https://github.com/bcmi/Image-Composition-Assessment-Dataset-CADB
공식 코드Image Composition Assessment with Saliency-augmented Multi-pattern Pooling
https://github.com/mediatechnologycenter/aestheval
공식 코드Understanding Aesthetics with Language: A Photo Critique Dataset for Aesthetic Assessment
https://github.com/q-future/q-align
공식 코드Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
https://github.com/xk-huang/OrdinalCLIP
공식 코드OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression
https://github.com/IsaacCorley/deep-aesthetics-pytorch
Photo Aesthetics Ranking Network with Attributes and Content Adaptation
https://github.com/Kin-Lau/ILGnet
https://github.com/MS-Mind/MS-Code-06/tree/main/nima
https://github.com/MS-Mind/MS-Code-08/tree/main/nima
https://github.com/Mind23-2/MindCode-101/tree/main/nima
https://github.com/Mind23-2/MindCode-3/tree/main/nima
https://github.com/Openning07/MPADA
https://github.com/aimerykong/deepImageAestheticsAnalysis
Photo Aesthetics Ranking Network with Attributes and Content Adaptation
https://github.com/code-implementation1/Code6/tree/main/nasnet
https://github.com/code-implementation1/Code6/tree/main/nima
https://github.com/code-implementation1/Code6/tree/main/nima_vgg16
https://github.com/gautamMalu/Aesthetic_attributes_maps
https://github.com/idealo/image-quality-assessment
https://github.com/kentsyx/Neural-IMage-Assessment
https://github.com/mindspore-ai/models/tree/master/official/cv/nima
https://github.com/rawmarshmellows/deep-photo-aesthetics
https://github.com/subpic/ava-mlsp
Effective Aesthetics Prediction with Multi-level Spatially Pooled Features
https://github.com/titu1994/neural-image-assessment
https://github.com/truskovskiyk/nima.pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.