NIMA: Neural Image Assessment

Talebi, Hossein; Milanfar, Peyman

doi:10.1109/tip.2018.2831899

Cited by 756 publications

(531 citation statements)

References 43 publications

Supporting

Mentioning

467

Contrasting

Unclassified

Order By: Relevance

“…Since these real world examples do not have ground truth, to obtain objective scores of these results, we evaluate the quality of composition results using our matting results. Specifically, we composite the foreground objects in source images onto some external background images using our matting results and then measure the visual quality of the composition results using the NIMA quality assessment algorithm [48]. As reported in Table 4, our data augmentation algorithms are helpful.…”

Section: Ablation Studymentioning

confidence: 99%

Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation

Hou

Liu

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

152

166

View full text Add to dashboard Cite

Natural image matting is an important problem in computer vision and graphics. It is an ill-posed problem when only an input image is available without any external information. While the recent deep learning approaches have shown promising results, they only estimate the alpha matte. This paper presents a context-aware natural image matting method for simultaneous foreground and alpha matte estimation. Our method employs two encoder networks to extract essential information for matting. Particularly, we use a matting encoder to learn local features and a context encoder to obtain more global context information. We concatenate the outputs from these two encoders and feed them into decoder networks to simultaneously estimate the foreground and alpha matte. To train this whole deep neural network, we employ both the standard Laplacian loss and the feature loss: the former helps to achieve high numerical performance while the latter leads to more perceptually plausible results. We also report several data augmentation strategies that greatly improve the network's generalization performance. Our qualitative and quantitative experiments show that our method enables high-quality matting for a single natural image. Our inference codes and models have been made publicly available at https://github. com/hqqxyy/Context-Aware-Matting.

show abstract

Section: Ablation Studymentioning

confidence: 99%

Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation

Hou

Liu

2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

152

166

View full text Add to dashboard Cite

show abstract

“…To compute a score on attractiveness, we choose to employ the aesthetics score, which measures how beautiful a visual material looks to humans. Here, we leverage previous research on designing deep learning models to predict the aesthetics scores of images [9,25,27,30].…”

Section: What Makes Videos Persuasive?mentioning

confidence: 99%

Generating Persuasive Visual Storylines for Promotional Videos

Liu

Dong

et al. 2019

Proceedings of the 28th ACM International Conference on Information and Knowledge Management

View full text Add to dashboard Cite

Video contents have become a critical tool for promoting products in E-commerce. However, the lack of automatic promotional video generation solutions makes large-scale video-based promotion campaigns infeasible. The first step of automatically producing promotional videos is to generate visual storylines, which is to select the building block footage and place them in an appropriate order. This task is related to the subjective viewing experience. It is hitherto performed by human experts and thus, hard to scale. To address this problem, we propose WundtBackpack, an algorithmic approach to generate storylines based on available visual materials, which can be video clips or images. It consists of two main parts, 1) the Learnable Wundt Curve to evaluate the perceived persuasiveness based on the stimulus intensity of a sequence of visual materials, which only requires a small volume of data to train; and 2) a clustering-based backpacking algorithm to generate persuasive sequences of visual materials while considering video length constraints. In this way, the proposed approach provides a dynamic structure to empower artificial intelligence (AI) to organize video footage in order to construct a sequence of visual stimuli with persuasive power. Extensive real-world experiments show that our approach achieves close to 10% higher perceived persuasiveness scores by human testers, and 12.5% higher expected revenue compared to the best performing state-of-the-art approach.

show abstract

“…The second popular assessment task is to give a continuously numerical score of aesthetics. Another numerical assessment task is to predict a score distribution of human rating on the aesthetic aspect of an image [8,15,28].…”

Section: Introductionmentioning

confidence: 99%

Aesthetic Attributes Assessment of Images

Jin

Zhao

et al. 2019

Proceedings of the 27th ACM International Conference on Multimedia

View full text Add to dashboard Cite

Figure 1: Aesthetic Attributes Assessment of Images. We predict caption and score of each aesthetic attribute of an image.ABSTRACT Image aesthetic quality assessment has been a relatively hot topic during the last decade. Most recently, comments type assessment (aesthetic captions) has been proposed to describe the general aesthetic impression of an image using text. In this paper, we propose Aesthetic Attributes Assessment of Images, which means the aesthetic attributes captioning. This is a new formula of image aesthetic assessment, which predicts aesthetic attributes captions together with the aesthetic score of each attribute. We introduce a new dataset named DPC-Captions which contains comments of up to 5 aesthetic attributes of one image through knowledge transfer from a full-annotated small-scale dataset. Then, we propose Aesthetic Multi-Attribute Network (AMAN), which is trained on a mixture of fully-annotated small-scale PCCD dataset and weakly-annotated large-scale DPC-Captions dataset. Our AMAN makes full use of transfer learning and attention model in a single framework. The experimental results on our DPC-Captions and PCCD dataset reveal that our method can predict captions of 5 aesthetic attributes together with numerical score assessment of each attribute. We use the evaluation criteria used in image captions to prove that our specially designed AMAN model outperforms traditional CNN-LSTM model and modern SCA-CNN model of image captions.

show abstract

NIMA: Neural Image Assessment

Cited by 756 publications

References 43 publications

Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation

Context-Aware Image Matting for Simultaneous Foreground and Alpha Estimation

Generating Persuasive Visual Storylines for Promotional Videos

Aesthetic Attributes Assessment of Images

Contact Info

Product

Resources

About