bcmi/Awesome-Aesthetic-Evaluation-and-Cropping bcmi/Awesome-Aesthetic-Evaluation-and-Cropping

  • Stars
    star
    276
  • Rank 149,319 (Top 3 %)
  • Language
  • Created over 4 years ago
  • Updated 4 months ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
bcmi/Awesome-Aesthetic-Evaluation-and-Cropping
github

bcmi

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Awesome Image Aesthetic Assessment and Cropping Awesome

A curated list of resources including papers, datasets, and relevant links pertaining to aesthetic evaluation and cropping.

Contributing

Contributions are welcome. If you wish to contribute, feel free to send a pull request. If you have suggestions for new sections to be included, please raise an issue and discuss before sending a pull request.

Table of Contents

Papers

Image Aesthetic Assessment

  • Ran Yi, Haoyuan Tian, Zhihao Gu, Yu-Kun Lai, Paul L. Rosin: "Towards Artistic Image Aesthetics Assessment: a Large-scale Dataset and a New Method" CVPR (2023) [pdf] [dataset]
  • Junjie Ke, Keren Ye, Jiahui Yu, Yonghui Wu, Peyman Milanfar, Feng Yang: "VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining" CVPR (2023) [pdf]
  • Shuai He, Yongchang Zhang, Rui Xie, Dongxiang Jiang, Anlong Ming: "Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks" IJCAI (2022) [pdf] [code]
  • Yuzhe Yang, Liwu Xu, Leida Li, Nan Qie, Yaqian Li, Peng Zhang, Yandong Guo: "Personalized Image Aesthetics Assessment with Rich Attributes" CVPR (2022) [pdf] [homepage]
  • Dongyu She, Yu-Kun Lai, Gaoxiong Yi, Kun Xu: "Hierarchical layout-aware graph convolutional network for unified aesthetics assessment." CVPR (2021) [pdf]
  • Hao Lou, Heng Huang, Chaoen Xiao, Xin Jin: "Aesthetic Evaluation and Guidance for Mobile Photography." ACM MM(2021) [pdf]
  • Pei Lv, Jianqi Fan, Xixi Nie, Weiming Dong, Xiaoheng Jiang, Bing Zhou, Mingliang Xu, Changsheng Xu: "User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning." TMM (2021) [pdf]
  • Jingwen Hou, Sheng Yang, Weisi Lin, Baoquan Zhao, Yuming Fang: "Learning Image Aesthetic Assessment from Object-level Visual Components." TIP (2021) [pdf]
  • Lin Zhao, Meimei Shang, Fei Gao, Rongsheng Li, Fei Huang, Jun Yu: "Representation learning of image composition for aesthetic prediction." CVIU (2020) [pdf] [code]
  • Jingwen Hou, Sheng Yang, Weisi Lin: "Object-level attention for aesthetic rating distribution prediction." ACM MM (2020) [pdf]
  • Kekai Sheng, Weiming Dong, Menglei Chai, Guohui Wang, Peng Zhou, Feiyue Huang, Bao-Gang Hu, Rongrong Ji, Chongyang Ma: "Revisiting image aesthetic assessment via self-supervised feature learning." AAAI (2020) [pdf]
  • Qiuyu Chen, Wei Zhang, Ning Zhou, Peng Lei, Yi Xu, Yu Zheng, Jianping Fan: "Adaptive fractional dilated convolution network for image aesthetics assessment." CVPR (2020) [pdf]
  • Hui Zeng, Zisheng Cao, Lei Zhang, Alan C. Bovik: "A unified probabilistic formulation of image aesthetic assessment." TIP (2020) [pdf] [code]
  • Dong Liu, Rohit Puri, Nagendra Kamath, Subhabrata Bhattacharya: "Composition-aware image aesthetics assessment." WACV(2020) [pdf]
  • Hancheng Zhu, Leida Li, Jinjian Wu, Sicheng Zhao, Guiguang Ding, Guangming Shi: "Personalized Image Aesthetics Assessment via Meta-Learning With Bilevel Gradient Optimization." IEEE Trans. Cybern. (2020) [pdf] [code]
  • Weining Wang, Rui Deng: "Modeling human perception for image aesthetic assessme." ICIP (2019) [pdf]
  • Vlad Hosu, Bastian Goldlucke, Dietmar Saupe: "Effective aesthetics prediction with multi-level spatially pooled features." CVPR (2019) [pdf] [code]
  • Xin Jin, Le Wu, Geng Zhao, Xiaodong Li, Xiaokun Zhang, Shiming Ge, Dongqing Zou, Bin Zhou, Xinghui Zhou: "Aesthetic attributes assessment of images." ACM MM (2019) [pdf] [project]
  • Leida Li, Hancheng Zhu, Sicheng Zhao, Guiguang Ding, Hongyan Jiang, Allen Tan: "Personality driven multi-task learning for image aesthetic assessment." ICME (2019) [pdf]
  • Ning Ma, Alexey Volkov, Aleksandr Livshits, Pawel Pietrusinski, Houdong Hu, Mark Bolin: "An universal image attractiveness ranking framework." WACV (2019) [pdf]
  • Jun-Tae Lee, Han-Ul Kim, Chul Lee, Chang-Su Kim: "Photographic composition classification and dominant geometric element detection for outdoor scenes." JVCIR (2018) [pdf] [code]
  • Katja Thömmes and Ronald Hübner: "Instagram likes for architectural photos can be predicted by quantitative balance measures and curvature." Front Psychol (2018) [pdf]
  • Kekai Sheng, Weiming Dong, Chongyang Ma, Xing Mei, Feiyue Huang, Bao-Gang Hu: "Attention-based multi-patch aggregation for image aesthetic assessment." ACM MM (2018) [pdf] [code]
  • Ning Yu, Xiaohui Shen, Zhe Lin, Radomir Mech, Connelly Barnes: "Learning to detect multiple photographic defects." WACV (2018) [pdf]
  • Keunsoo Ko, Jun Tae Lee, Chang-Su Kim: "PAC-Net: Pairwise aesthetic comparison network for image aesthetic assessment." ICIP (2018) [pdf]
  • Hossein Talebi and Peyman Milanfar: "NIMA: Neural image assessment." TIP (2018) [pdf] [code]
  • Katharina Schwarz, Patrick Wieschollek, Hendrik P. A. Lensch: "Will people like your image? Learning the aesthetic space." WACV (2018) [pdf] [code]
  • Guolong Wang, Junchi Yan, Zheng Qin: "Collaborative and attentive learning for personalized image aesthetic assessment." IJCAI (2018) [pdf]
  • Shuang Ma, Jing Liu, Chang Wen Chen: "A-Lamp: Adaptive layout-aware multi-patch deep convolutional neural network for photo aesthetic assessment." CVPR (2017) [pdf] [code]
  • Jian Ren, Xiaohui Shen, Zhe Lin, Radomir Mech, David J. Foran: "Personalized image aesthetics." ICCV (2017) [pdf] [code]
  • Anselm Brachmann and Christoph Redies: "Computational and experimental approaches to visual aesthetics." Front Hum Neurosci (2017) [pdf]
  • Anselm Brachmann, Erhardt Barth, Christoph Redies: "Using CNN features to better understand what makes visual artworks special." Front Psychol (2017) [pdf]
  • Deng Yubin, Chen Change Loy, Xiaoou Tang: "Image aesthetic assessment: An experimental survey." IEEE Signal Processing Magazine (2017) [pdf]
  • Long Mai, Hailin Jin, Feng Liu: "Composition-preserving deep photo aesthetics assessment." CVPR (2016) [pdf]
  • Shu Kong, Xiaohui Shen, Zhe L. Lin, Radomír Mech, Charless C. Fowlkes: "Photo aesthetics ranking network with attributes and content adaptation." ECCV (2016) [pdf] [code]
  • Xin Lu, Zhe Lin, Xiaohui Shen, Radomir Mech, James Z. Wang: "Deep multi-patch aggregation network for image style, aesthetics, and quality estimation." ICCV (2015) [pdf]
  • Xin Lu, Zhe Lin, Hailin Jin, Jianchao Yang, James Z. Wang: "Rapid: Rating pictorial aesthetics using deep learning." ACM MM (2014) [pdf] [code]
  • Naila Murray, Luca Marchesotti, Florent Perronnin: "AVA: A large-scale database for aesthetic visual analysis." CVPR (2012) [pdf]
  • Luca Marchesotti, Florent Perronnin, Diane Larlus, Gabriela Csurka: "Assessing the aesthetic quality of photographs using generic image descriptors." ICCV (2011) [pdf]
  • Sagnik Dhar, Vicente Ordonez, Tamara L Berg: "High level describable attributes for predicting aesthetics and interestingness." CVPR (2011) [pdf]
  • Ritendra Datta, Jia Li, and James Z. Wang: "Algorithmic inferencing of aesthetics and emotion in natural images: An exposition." ICIP (2008) [pdf]

Image Cropping

  • Wang Chao, Li Niu, Bo Zhang, Liqing Zhang: "Image Cropping with Spatial-aware Feature and Rank Consistency." CVPR (2023)
  • Gengyun Jia, Huaibo Huang, Chaoyou Fu, Ran He: "Rethinking Image Cropping: Exploring Diverse Compositions From Global Views." CVPR (2022) [pdf]
  • Yang Cheng, Qian Lin, Jan P. Allebach: "Re-Compose the Image by Evaluating the Crop on More Than Just a Score." WACV (2022) [pdf]
  • Zhiyu Pan, Zhiguo Cao, Kewei Wang, Hao Lu, Weicai Zhong: "TransView: Inside, Outside, and Across the Cropping View Boundaries." ICCV (2021) [pdf]
  • Lei Zhong, Feng-Heng Li, Hao-Zhi Huang, Yong Zhang, Shao-Ping Lu, Jue Wang: "Aesthetic-guided outward image cropping." TOG (2021) [pdf]
  • Chaoyi Hong, Shuaiyuan Du, Ke Xian, Hao Lu, Zhiguo Cao, Weicai Zhong: "Composing photos like a photographer." CVPR (2021) [pdf] [code]
  • Debang Li, Junge Zhang, Kaiqi Huang: "Learning to learn cropping models for different aspect ratio requirements." CVPR (2020) [pdf]
  • Debang Li, Junge Zhang, Kaiqi Huang, Ming-Hsuan Yang: "Composing good shots by exploiting mutual relations." CVPR (2020) [pdf] [code]
  • Yi Tu, Li Niu, Weijie Zhao, Dawei Cheng, Liqing Zhang: "Image cropping with composition and saliency aware aesthetic score map." AAAI (2020) [pdf]
  • Hui Zeng, Lida Li, Zisheng Cao, Lei Zhang: "Grid anchor based image cropping: a new benchmark and an efficient model." TPAMI (2020) [pdf] [code]
  • Hui Zeng, Lida Li, Zisheng Cao, Lei Zhang: "Reliable and efficient image cropping: a grid anchor based approach." CVPR (2019) [pdf] [code]
  • Weirui Lu, Xiaofen Xing, Bolun Cai, Xiangmin Xu: "Listwise view ranking for image cropping." IEEE Access (2019) [pdf] [code]
  • Zijun Wei, Jianming Zhang, Xiaohui Shen, Zhe Lin, Radomír Mech, Minh Hoai, Dimitris Samaras: "Good view hunting: learning photo composition from dense view pairs." CVPR (2018) [pdf] [VEN code] [VPN code]
  • Debang Li, Huikai Wu, Junge Zhang, Kaiqi Huang: "A2-RL: aesthetics aware reinforcement learning for image cropping." [pdf] [code]
  • Seyed A. Esmaeili, Bharat Singh, Larry S. Davis: "Fast-At: Fast automatic thumbnail generation using deep neural networks." CVPR (2017) [pdf]
  • Wenguan Wang, Jianbing Shen: "Deep cropping via attention box prediction and aesthetics assessment." ICCV (2017) [pdf]
  • Yi-Ling Chen, Jan Klopp, Min Sun, Shao-Yi Chien, Kwan-Liu Ma: "Learning to compose with professional photographs on the web." ACM MM (2017) [pdf] [code]
  • Yi-Ling Chen, Tzu-Wei Huang, Kai-Han Chang, Yu-Chen Tsai, Hwann-Tzong Chen, Bing-Yu Chen: "Quantitative analysis of automatic image cropping algorithms: a dataset and comparative study." WACV (2017) [pdf]
  • Jiansheng Chen, Gaocheng Bai, Shaoheng Liang, Zhengqin Li: "Automatic image cropping: a computational complexity study." CVPR (2016) [pdf]
  • Jonas Abeln, Leonie Fresz, Seyed Ali Amirshahi, Chris McManus, Michael Koch, Helene Kreysa, Christoph Redies: "Preference for well-balanced saliency in details cropped from photographs." Front Hum Neurosci (2016) [pdf]
  • Chen Fang, Zhe Lin, Radomír Mech, Xiaohui Shen: "Automatic image Cropping using visual composition, boundary simplicity and content preservation models." ACM MM (2014) [pdf]
  • Jianzhou Yan, Stephen Lin, Sing Bing Kang, Xiaoou Tang: "Learning the change for automatic image cropping." CVPR (2013) [pdf]
  • Bongwon Suh, Haibin Ling, Benjamin B. Bederson, David W. Jacobs: "Automatic thumbnail cropping and its effectiveness." UIST (2003) [pdf]

Aesthetic Captioning

  • Junjie Ke, Keren Ye, Jiahui Yu, Yonghui Wu, Peyman Milanfar, Feng Yang: "VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining." CVPR (2023) [pdf]
  • Koustav Ghosal, Aakanksha Rana, Aljosa Smolic: "Aesthetic Image Captioning From Weakly-Labelled Photographs." ICCVW (2019) [pdf] [homepage]
  • Xin Jin, Le Wu, Geng Zhao, Xiaodong Li, Xiaokun Zhang, Shiming Ge, Dongqing Zou, Bin Zhou, Xinghui Zhou: "Aesthetic Attributes Assessment of Images." ACM MM (2019) [pdf] [code]
  • Wenshan Wang, Su Yang, Weishan Zhang, Jiulong Zhang: "Neural aesthetic image reviewer." IET Computer Vision (2019) [pdf]
  • Kuang-Yu Chang, Kung-Hung Lu, Chu-Song Chen: "Aesthetic Critiques Generation for Photos." ICCV (2017) [pdf] [code]
  • Ye Zhou, Xin Lu, Junping Zhang, James Z. Wang: "Joint image and text representation for aesthetics analysis." ACM MM (2016) [pdf]

Datasets

Aesthetic Assessment Datasets

images with aesthetic score/attribute

images with aesthetic caption

image with composition score/label

Image Cropping Datasets

densely annotated (multiple crops in each image are annotated)

sparsely annotated (only the best crop in each image is annotated)

More Repositories

1

Awesome-Image-Composition

A curated list of papers, code and resources pertaining to image composition/compositing or object insertion, which aims to generate realistic composite image.
1,171
star
2

Image-Harmonization-Dataset-iHarmony4

[CVPR 2020] The first large-scale public benchmark dataset for image harmonization. The code used in our paper "DoveNet: Deep Image Harmonization via Domain Verification", CVPR2020. Useful for image harmonization, image composition, etc.
MATLAB
764
star
3

libcom

Image composition toolbox: everything you want to know about image composition or object insertion
Python
499
star
4

Awesome-Image-Harmonization

A curated list of papers, code and resources pertaining to image harmonization.
425
star
5

DCI-VTON-Virtual-Try-On

[ACM Multimedia 2023] Taming the Power of Diffusion Models for High-Quality Virtual Try-On with Appearance Flow.
Python
398
star
6

Awesome-Few-Shot-Image-Generation

A curated list of papers, code and resources pertaining to few-shot image generation.
366
star
7

CaGNet-Zero-Shot-Semantic-Segmentation

Code for our ACMMM2020 paper "Context-aware Feature Generation for Zero-shot Semantic Segmentation".
Python
233
star
8

SLBR-Visible-Watermark-Removal

[ACM MM 2021] Visible Watermark Removal via Self-calibrated Localization and Background Refinement
Python
214
star
9

Awesome-Weak-Shot-Learning

A curated list of papers, code and resources pertaining to weak-shot classification, detection, and segmentation.
183
star
10

Object-Shadow-Generation-Dataset-DESOBA

[AAAI 2022] The first dataset on foreground object shadow generation for image composition in real-world scenes. The code used in our paper "Shadow Generation for Composite Image in Real-world Scenes", AAAI2022. Useful for shadow generation, shadow removal, image composition, etc.
Python
165
star
11

ControlCom-Image-Composition

A controllable image composition model which could be used for image blending, image harmonization, view synthesis.
Python
141
star
12

CDTNet-High-Resolution-Image-Harmonization

[CVPR 2022] We unify pixel-to-pixel transformation and color-to-color transformation in a coherent framework for high-resolution image harmonization. We also release 100 high-resolution real composite images for evaluation.
Python
124
star
13

Image-Composition-Assessment-Dataset-CADB

[BMVC2021] The first image composition assessment dataset. Used in the paper "Image Composition Assessment with Saliency-augmented Multi-pattern Pooling". Useful for image composition assessment, image aesthetic assesment, etc.
Python
112
star
14

Awesome-Visible-Watermark-Removal

102
star
15

Object-Shadow-Generation-Dataset-DESOBAv2

[CVPR 2024] The dataset, code, and model for our paper "Shadow Generation for Composite Image Using Diffusion Model", CVPR, 2024.
Python
102
star
16

GracoNet-Object-Placement

[ECCV 2022] Official code for "Learning Object Placement via Dual-path Graph Completion"
Python
100
star
17

Awesome-Object-Shadow-Generation

A curated list of papers, code, and resources pertaining to object shadow generation.
89
star
18

Awesome-Generative-Image-Composition

A curated list of papers, code, and resources pertaining to generative image composition or object insertion.
Python
78
star
19

F2GAN-Few-Shot-Image-Generation

Fusing-and-Filling GAN (F2GAN) for few-shot image generation, ACM MM2020
Python
78
star
20

Awesome-Object-Placement

A curated list of papers, code, and resources pertaining to object placement.
75
star
21

Object-Placement-Assessment-Dataset-OPA

The first dataset of composite images with rationality score indicating whether the object placement in a composite image is reasonable.
Python
74
star
22

BargainNet-Image-Harmonization

BargainNet: Background-Guided Domain Translation for Image Harmonization. Useful for Image harmonization, image composition, etc.
Python
67
star
23

SimTrans-Weak-Shot-Classification

[NeurIPS 2021] The first weak-shot classification paper.
Python
63
star
24

SSP-AI-Generated-Image-Detection

The code for "A Single Simple Patch is All You Need for AI-generated Image Detection"
Python
62
star
25

Video-Harmonization-Dataset-HYouTube

[IJCAI 2022] The first public benchmark dataset for video harmonization. The code used in our paper "Deep Video Harmonization with Color Mapping Consistency", IJCAI 2022.
Python
59
star
26

ObjectStitch-Image-Composition

An unofficial implementation of the paper "ObjectStitch: Object Compositing with Diffusion Model", CVPR 2023.
Python
55
star
27

TraMaS-Weak-Shot-Object-Detection

[NeurIPS 2021] A weak-shot object detection approach by transferring semantic similarity and mask prior.
Python
52
star
28

PHDiffusion-Painterly-Image-Harmonization

[ACM MM 2023] The code used in our paper "Painterly Image Harmonization using Diffusion Model", ACM MM2023.
Python
51
star
29

DeltaGAN-Few-Shot-Image-Generation

[ECCV 2022] Generate sample-specific intra-category deltas for few-shot image generation.
Python
50
star
30

PHDNet-Painterly-Image-Harmonization

[AAAI 2023] Painterly image harmonization in both spatial domain and frequency domain.
Python
50
star
31

Causal-VidQA

[CVPR 2022] A large-scale public benchmark dataset for video question-answering, especially about evidence and commonsense reasoning. The code used in our paper "From Representation to Reasoning: Towards both Evidence and Commonsense Reasoning for Video Question-Answering", CVPR2022.
Python
50
star
32

SimFormer-Weak-Shot-Semantic-Segmentation

Python
44
star
33

DucoNet-Image-Harmonization

[ACM MM 23] Deep image harmonization in Dual Color Space
Python
38
star
34

Awesome-Image-Blending

A curated list of papers, code and resources pertaining to image blending.
38
star
35

CaGNetv2-Zero-Shot-Semantic-Segmentation

Code for "From Pixel to Patch: Synthesize Context-aware Features for Zero-shot Semantic Segmentation".
Python
36
star
36

SycoNet-Adaptive-Image-Harmonization

[ICCV 2023] The code used in our paper "Deep Image Harmonization with Learnable Augmentation", ICCV2023.
Python
35
star
37

FOPA-Fast-Object-Placement-Assessment

A discriminative object placement approach
Python
32
star
38

MatchingGAN-Few-Shot-Image-Generation

code for Matchinggan: Matching-Based Few-Shot Image Generation
Python
30
star
39

ProPIH-Painterly-Image-Harmonization

[AAAI2024] Progressive Painterly Image Harmonization from Low-level Styles to High-level Styles
Python
24
star
40

RETAB-Weak-Shot-Semantic-Segmentation

Official Implementation for Weak-shot Semantic Segmentation by Transferring Semantic Affinity and Boundary (BMVC 2022)
Python
24
star
41

ArtoPIH-Painterly-Image-Harmonization

[AAAI2024] Painterly Image Harmonization by Learning from Painterly Objects
Python
24
star
42

Human-Centric-Image-Cropping

Official implementation for ECCV2022 paper: Human-centric Image Cropping with Partition-aware and Content-preserving Features.
Python
24
star
43

DIRL-Inharmonious-Region-Localization

[ICME2021]The first work on Deep Inharmonious Region Localization, which can help image harmonization in an adversarial way.
Python
24
star
44

Accessory-Try-On-Dataset-STRAT

A virtual accessory try-on dataset which could be used for image composition
Python
21
star
45

TopNet-Object-Placement

An unofficial implementation of the paper "TopNet: Transformer-based Object Placement Network for Image Compositing", CVPR 2023.
Python
20
star
46

Foreground-Object-Search-Dataset-FOSD

[ICCV 2023] The datasets and code used in our paper "Foreground Object Search by Distilling Composite Image Feature", ICCV2023.
Python
19
star
47

Composite-Image-Evaluation

19
star
48

stock-price-prediction

Fall 18' Class Project for Artificial Intelligence
Jupyter Notebook
19
star
49

Rendered-Shadow-Generation-Dataset-RdSOBA

[AAAI 2024] The dataset used in our paper "Shadow Generation with Decomposed Mask Prediction and Attentive Shadow Filling", AAAI 2024.
19
star
50

DreamCom-Image-Composition

A simple baseline for image composition using text-guided inpainting model
Python
18
star
51

MadisNet-Inharmonious-Region-Localization

[AAAI 2022] MadisNet: Inharmonious Region Localization by Magnifying Domain Discrepancy
Python
16
star
52

Rendered-Image-Harmonization-Dataset-RdHarmony

The first rendered image harmonization dataset. Used in our paper "CharmNet: Deep Image Harmonization by Bridging the Reality Gap". Useful for Image harmonization, image composition, etc.
Python
16
star
53

Image-Harmonization-Dataset-ccHarmony

[ICCV 2023] The color checker based harmonization dataset contributed in our paper "Deep Image Harmonization with Globally Guided Feature Transformation and Relation Distillation", ICCV2023.
Python
16
star
54

Color-Transfer-for-Image-Harmonization

Summarize different color transfer strategies for image harmonization task.
MATLAB
15
star
55

Awesome-Foreground-Object-Search

A curated list of papers, code, and resources pertaining to foreground object search.
13
star
56

AustNet-Inharmonious-Region-Localization

[BMVC2022] Inharmonious Region Localization with Auxiliary Style Feature
Python
13
star
57

GPSDiffusion-Object-Shadow-Generation

4
star
58

toolbox

Python
4
star
59

MureObjectStitch-Image-Composition

Python
3
star
60

iConReg

Regulating contagion risk to curb the systemic crisis in loan networks though deep graph learning
2
star
61

Awesome-Video-Composition

2
star
62

ESL-chinese

Element of Statistical Learning 中文翻译版
1
star
63

anormal-detection

anormal detection of time series data.
1
star
64

financial-time-series

Learning Causal Relationships in Financial Time Series
1
star