mlsanityData to 14 Oct 2026

Photo Rater: Photographs Auto-Selector with Deep Learning

arXiv 2211.14420 ↗First posted 26 Nov 2022cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

Photo Rater is a computer vision project that uses neural networks to help photographers select the best photo among those that are taken based on the same scene. This process is usually referred to as "culling" in photography, and it can be tedious and time-consuming if done manually. Photo Rater utilizes three separate neural networks to complete such a task: one for general image quality assessment, one for classifying whether the photo is blurry (either due to unsteady hands or out-of-focusness), and one for assessing general aesthetics (including the composition of the photo, among others). After feeding the image through each neural network, Photo Rater outputs a final score for each image, ranking them based on this score and presenting it to the user.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 7 references that are in the map. First posted on arXiv 26 Nov 2022.

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References · 2

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2020MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessmentcited by 144

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