3 papers with code • 1 benchmarks • 3 datasets
Automatically learned quality assessment for images has recently become a hot topic due to its usefulness in a wide variety of applications such as evaluating image capture pipelines, storage techniques and sharing media.
Ranked #4 on Aesthetics Quality Assessment on AVA
In this work, we propose to learn a deep convolutional neural network to rank photo aesthetics in which the relative ranking of photo aesthetics are directly modeled in the loss function.
Ranked #7 on Aesthetics Quality Assessment on AVA
We propose an effective deep learning approach to aesthetics quality assessment that relies on a new type of pre-trained features, and apply it to the AVA data set, the currently largest aesthetics database.
Ranked #3 on Aesthetics Quality Assessment on AVA