computer-vision-11
Video Quality Assessment
3260 papers • 126 benchmarks • 313 datasets
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.
(Image credit: Papersgraph)
Benchmarks
These leaderboards are used to track progress in video-quality-assessment-34
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Dataset
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MSU SR-QA Dataset
MSU SR-QA Dataset
MSU NR VQA Database
MSU NR VQA Database
KoNViD-1k
Libraries
iUse these libraries to find video-quality-assessment-34 models and implementations
3 papers 217
Datasets
Subtasks
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Most implemented papers
Towards Deep Learning Models Resistant to Adversarial Attacks
Ludwig Schmidt, Dimitris Tsipras, A. Ma̧dry, Aleksandar Makelov, Adrian Vladu•Sun Jun 18 2017
This work studies the adversarial robustness of neural networks through the lens of robust optimization, and suggests the notion of security against a first-order adversary as a natural and broad security guarantee.
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