https://github.com/fiabdu/Commonly-Interesting-Images
VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining
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Video Quality Assessment
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Video Quality Assessment is a computer vision task aiming to mimic video-based human subjective perception. The goal is to produce a mos score, where higher score indicates better perceptual quality. Some well-known benchmarks for this task are KoNViD-1k, LIVE-VQC, YouTube-UGC and LSVQ. SROCC/PLCC/RMSE are usually used to evaluate the performance of different models.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/francois-rozet/piqa
https://github.com/gfxdisp/FovVideoVDP
https://github.com/gukyeongkwon/3DPSD-VQA
Power of Tempospatially Unified Spectral Density for Perceptual Video Quality Assessment
https://github.com/h4nwei/SPAQ
https://github.com/henry8527/GCE
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/hope-yao/robust_attention_cifar
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/hrdwsong/TDLMR2AA-Paddle
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/idealo/image-quality-assessment
https://github.com/idearibosome/tf-perceptual-eusr
The 2018 PIRM Challenge on Perceptual Image Super-resolution
https://github.com/imrahulr/adversarial_robustness_pytorch
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/isaacschaal/SG_training
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/jarikorhonen/nr-vqa-consumervideo
https://github.com/jokeryan/post_training
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/jooyae/NVIDIA_STYLEGAN
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/kentsyx/Neural-IMage-Assessment
https://github.com/keunwoochoi/Auralisation
https://github.com/khieu/cifar10_challenge
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/khurram702/StyleBasedGAN
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/kozistr/gan-metrics
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/lemonadec/Relevance-between-Accuracy-under-Black-box-Attack-and-the-Similarity-between-Networks
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/lizatish/My_CNN
The 2018 PIRM Challenge on Perceptual Image Super-resolution
https://github.com/locuslab/convex_adversarial
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/locuslab/robust_overfitting
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/loes5307/vocaladversary2022
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/luizgh/adversarial_signatures
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/matanbt/attack-tabular
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/microsoft/MLCVQA
Full Reference Video Quality Assessment for Machine Learning-Based Video Codecs
https://github.com/microsoft/distance-learner
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/mihirp1/Deep_Learning_Project_CPSC8810
https://github.com/mindspore-ai/models/tree/master/official/cv/nima
https://github.com/mx54039q/cnn-visualizing
https://github.com/ndb796/pytorch-adversarial-training-cifar
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/olivesgatech/3DPSD-VQA
Power of Tempospatially Unified Spectral Density for Perceptual Video Quality Assessment
https://github.com/olivesgatech/UNIQUE-Project-Repository
https://github.com/olivesgatech/UNIQUE-Unsupervised-Image-Quality-Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.