RAPID: Rating Pictorial Aesthetics using Deep Learning

By Xin Lu, Zhe L. Lin, Hailin Jin, Jianchao Yang, James Zijun Wang. 2014

430 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering (2016, background): “, ‘Deep CNN’) features extracted from raw product images present as a good option due to their widely demonstrated efficacy at capturing abstract notions of fine-grained categories [31], photographic style [17], aesthetic quality [24], and scene characteristics [8], among others.”
VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback (2015, background): “2014), and aesthetic quality categorization (Lu et al. 2014), among others.”
Building a Large Scale Dataset for Image Emotion Recognition: The Fine Print and The Benchmark (2016, background): “2011; Krizhevsky, Sutskever, and Hinton 2012), aesthetics estimation (Lu et al. 2014) and scene recognition (Zhou et al.”
Recognizing and Curating Photo Albums via Event-Specific Image Importance (2017, background): “Image importance is a complex image property which is related to various factors, such as aesthetics [15], interestingness [5] and image memorability [11].”
NIMA: Neural Image Assessment (2017, neutral): “…LCC SRCC LCC SRCC EMD (2 classes) (mean) (mean) (std.dev) (std.dev) Murray et al. [1] 66.70% – – – – – Kao et al. [9] 71.42% – – – – – Lu et al. [30] 74.46% – – – – – Lu et al. [16] 75.42% – – – – – Kao et al. [31] 76.58% – – – – – Wang et al. [32] 76.80% – – – – – Mai et al. [10] 77.10% – – –…”
Deep Multi-patch Aggregation Network for Image Style, Aesthetics, and Quality Estimation (2015, methodology): “Multi-column neural network [5, 1, 24] has been demonstrated as an efficient approach to improve performance of single-column neural networks in various classification problems.”
A Deep Network Solution for Attention and Aesthetics Aware Photo Cropping (2019, methodology): “We compare our methods with the state-of-the-art methods including AVA [63], RAP [51], RAP2 [70], DMA [53], ARC [54] and CPD [55], where AVA offers the state-of-the-art result based on manually designed features while other methods are based on deep learning model.”
Image Captioning with Deep Bidirectional LSTMs (2016, background): “Additionally, it has been proved that data augmentation such as randomly cropping and horizontal mirror [32, 22], adding noise, blur and rotation [40] can effectively alleviate over-fitting and other.”
Photo Aesthetics Ranking Network with Attributes and Content Adaptation (2016, methodology): “While judging image aesthetics is a subjective task, it has been an area of active study in recent years and substantial progress has been made in identifying and quantifying those image features that are predictive of favorable aesthetic judgements by most individuals [20,17,16,18,5].”
Image Aesthetic Assessment: An experimental survey (2016, methodology): “As shown in Table 5, fine-tuning from the vanilla AlexNet yields better performance than simply training the base net of RAPID from scratch.”