[1] RAMESH A, PAVLOV M, GOH G, et al. Zero-shot text-toimage generation[C]//International Conference on Machine Learning. New York:PMLR, 2021:8821-8831.
[2] DING M, YANG Z, HONG W, et al. Cogview:mastering textto-image generation via transformers[J]. Advances in neural information processing systems, 2021, 34:19822-19835.
[3] ESSER P, ROMBACH R, OMMER B. Taming transformers for high-resolution image synthesis[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.Piscataway:IEEE, 2021:12873-12883.
[4] GAFNI O, POLYAK A, ASHUAL O, et al. Make-a-scene:scenebased text-to-image generation with human priors[C]//European Conference on Computer Vision. Cham:Springer Nature, 2022:89-106.
[5] DING M, ZHENG W, HONG W, et al. Cogview2:faster and better text-to-image generation via hierarchical transformers[J].Advances in neural information processing systems, 2022, 35:16890-16902.
[6] YU J, XU Y, KOH J Y, et al. Scaling autoregressive models for content-rich text-to-image generation[J]. Transactions on machine learning research, 2022, 2(3):5-54.
[7] NICHOL A, DHARIWAl P, RAMESH A, et al. Glide:towards photorealistic image generation and editing with text-guided diffusion models[J]. arxiv preprint arxiv:2112.10741, 2021.
[8] ROMBACH R, BLATTMANN A, LORENZ D, et al. Highresolution image synthesis with latent diffusion models[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Piscataway:IEEE, 2022:10684-10695.
[9] RAMESH A, DHARIWAL P, NICHOL A, et al. Hierarchical text-conditional image generation with clip latents[J]. arxiv preprint arxiv:2204.06125, 2022.
[10] SAHARIA C, HO J, CHAN W, et al. Image super-resolution via iterative refinement[J]. IEEE transactions on pattern analysis and machine intelligence, 2022, 45(4):4713-4726.
[11] CHEN J, HUANG Y, LV T, et al. Textdiffuser:diffusion models as text painters[J]. Advances in neural information processing systems, 2024(36):9353-9387.
[12] CHEN J, HUANG Y, LV T, et al. TextDiffuser-2:unleashing the power of language models for text rendering[J]. arxiv preprint arxiv:2311.16465, 2023.
[13] FENG W, HE X, FU T J, et al. Training-free structured diffusion guidance for compositional text-to-image synthesis[J]. arxiv preprint arxiv:2212.05032, 2022.
[14] LIU N, LI S, DU Y, et al. Compositional visual generation with composable diffusion models[C]//European Conference on Computer Vision. Cham:Springer Nature, 2022:423-439.
[15] DENG J, DONG W, SOCHER R, et al. Imagenet:a largescale hierarchical image database[C]//2009 IEEE conference on computer vision and pattern recognition. Piscataway:IEEE,2009:248-255.
[16] LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft coco:common objects in context[C]//Computer Vision-ECCV 2014:13th European conference. Zurich:Springer, 2014:740-755.
[17] REED S, AKATA Z, LEE H, et al. Learning deep representations of fine-grained visual descriptions[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition.Piscataway:IEEE, 2016:49-58.
[18] SALIMANS T, GOODFELLOW I, ZAREMBA W, et al.Improved techniques for training gans[J]. Advances in neural information processing systems, 2016, 29:2234-2242.
[19] HEUSEl M, RAMSAUER H, UNTERTHINER T, et al. Gans trained by a two time-scale update rule converge to a local nash equilibrium[J]. Advances in neural information processing systems, 2017(30):6626-6637.
[20] ZHOU S, GORDON M, KRISHNA R, et al. Hype:a benchmark for human eye perceptual evaluation of generative models[J].Advances in neural information processing systems, 2019(32):3449-3461.
[21] ZHANG R, ISOLA P, EFROS A A, et al. The unreasonable effectiveness of deep features as a perceptual metric[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. Piscataway:IEEE, 2018:586-595.
[22] KIRSTAIN Y, POLYAK A, SINGER U, et al. Pick-a-pic:an open dataset of user preferences for text-to-image generation[J].Advances in neural information processing systems, 2023(36):36652-36663.
[23] XU J, LIU X, WU Y, et al. Imagereward:learning and evaluating human preferences for text-to-image generation[J]. Advances in neural information processing systems, 2024(36):15903-15935.
[24] HESSEL J, HOLTZMAN A, FORBES M, et al. Clipscore:a reference-free evaluation metric for image captioning[J]. arXiv preprint arXiv:2104.08718, 2021.
[25] CHO J, HU Y, GARG R, et al. Davidsonian scene graph:improving reliability in fine-grained evaluation for text-image generation[J]. arXiv preprint arXiv:2310.18235, 2023.
[26] CHO J, ZALA A, BANSAL M. Visual programming for stepby-step text-to-image generation and evaluation[J]. Advances in neural information processing systems, 2024(36):6048-6069.
[27] HU Y, LIU B, KASAI J, et al. Tifa:accurate and interpretable text-to-image faithfulness evaluation with question answering[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Piscataway:IEEE, 2023:20406-20417.
[28] YAROM M, BITTON Y, CHANGPINYO S, et al. What you see is what you read?Improving text-image alignment evaluation[J].Advances in neural information processing systems, 2024(36):1601-1619.
[29] GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al.Generative adversarial nets[J]. Advances in neural information processing systems, 2014(27):2672-2680.
[30] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. Advances in neural information processing systems,2017, 30:5998-6008.
[31] RUIZ N, LI Y, JAMPANI V, et al. Dreambooth:fine tuning textto-image diffusion models for subject-driven generation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE, 2023:22500-22510.
[32] ZHANG H, SHAO H. Exploring the latest applications of OpenAI and ChatGPT:an in-depth survey[J]. CMES-computer modeling in engineering&sciences, 2023, 138(3):2061-2102.
[33] RADFORD A, KIM J W, HALLACY C, et al. Learning transferable visual models from natural language supervision[C]//International conference on machine learning. New York:PMLR,2021:8748-8763.
[34] PALMER S E. Vision science:photons to phenomenology[M].Cambridge:MIT Press, 1999.
[35] LIU J, LUGHOFER E, ZENG X. Toward model building for visual aesthetic perception[J]. Computational intelligence and neuroscience, 2017(1):1292801.
[36] SALEHI S, SHAFIEI M, YEO T, et al. ViPer:visual personalization of generative models via individual preference learning[C]//European conference on computer vision. Cham:Springer, 2025:391-406.
[37] TIAN D P. A review on image feature extraction and representation techniques[J]. International journal of multimedia and ubiquitous engineering, 2013, 8(4):385-396.
[38] NIXON M, AGUADO A. Feature extraction and image processing for computer vision[M]. Academic Press, 2019:12-20.
[39] CHO J, ZALA A, BANSAL M. Dall-eval:probing the reasoning skills and social biases of text-to-image generation models[C]//Proceedings of the IEEE/CVF international conference on computer vision. Piscataway:IEEE, 2023:3043-3054.
[40] SAHARIA C, CHAN W, SAXENA S, et al. Photorealistic textto-image diffusion models with deep language understanding[J].Advances in neural information processing systems, 2022(35):36479-36494.
[41] SHI H, KWOK N, FANG G, et al. Gradient-guided color image contrast and saturation enhancement[J]. International journal of advanced robotic systems, 2017, 14(3):1-11.
[42] ŽEGER I, BILANOVIĆN,ŠIŠUL G, et al. Comparison of metrics for colorized image quality evaluation[C]//2022International Symposium ELMAR. Piscataway:IEEE, 2022:209-214.
[43] RAJEVENCELTHA J, GAIDHANE V H. An efficient approach for no-reference image quality assessment based on statistical texture and structural features[J]. Engineering science and technology, an international journal, 2022(30):101039.
[44] HUNG C C, SONG E, LAN Y, et al. Image texture, texture features, and image texture classification and segmentation[J].Image Texture analysis:foundations, models and algorithms,2019(1):3-14.
[45] DING K, MA K, WANG S, et al. Image quality assessment:unifying structure and texture similarity[J]. IEEE transactions on pattern analysis and machine intelligence, 2020, 44(5):2567-2581.
[46] NIXON M, AGUADO A. Feature extraction and image processing for computer vision[M]. Amsterdam:Academic Press,2019.
[47] LYU Y, WANG X, LIN R, et al. Communication in human-AI co-creation:perceptual analysis of paintings generated by text-toimage system[J]. Applied sciences, 2022, 12(22):11312.
[48] ZHENG M, SIMSAR E, YESILTEPE H, et al. Stylebreeder:exploring and democratizing artistic styles through text-to-image models[J]. arXiv preprint arXiv:2406.14599, 2024.
[49] LEE T, YASUNAGA M, MENG C, et al. Holistic evaluation of text-to-image models[J]. Advances in neural information processing systems, 2024(36):69981-70011.
[50] PARK D H, AZADI S, LIU X, et al. Benchmark for compositional text-to-image synthesis[C]//Thirty-fifth conference on neural information processing systems datasets and benchmarks track (round 1). Cambridge:MIT Press, 2021.
[51] DINH T M, NGUYEN R, HUA B S. Tise:bag of metrics for text-to-image synthesis evaluation[C]//European conference on computer vision. Cham:Springer, 2022:594-609.
[52] JIANG M, HUANG Q, ZHANG L, et al. Tiger:text-to-image grounding for image caption evaluation[J]. arXiv preprint arXiv:1909.02050, 2019.
[53] SANTONEN T, KAIVO-OJA J. The Crowdsourcing Delphi:a method for combing expert judgements and wisdom of crowds[C]//International conference on knowledge management in organizations. Cham:Springer International Publishing, 2022:233-244.
[54] PAVLOVA I, PETRYTSA P, ANDRES A, et al. Measuring physical literacy in Ukraine:development of a set of indicators by Delphi method[J]. Physical activity review, 2021, 1(9):24-32.
[55] MEI W B, HSU C Y, OU S J. Research on the evaluation index system of the construction of communities suitable for aging by the Fuzzy Delphi method[J]. Environments, 2020, 7(10):92-119.
[56] 沈绮云,欧阳河,欧阳育良.产教融合目标达成度评价指标体系构建--基于德尔菲法和层次分析法的研究[J].高教探索, 2021(12):104-109.(SHEN Q Y, OUYANG H,OUYANG Y L. Construction of the evaluation index system for the achievement degree of production and education integration goal[J]. Higher education exploration, 2021(12):104-109.)
[57] LUO W, WANG X, TANG X. Content-based photo quality assessment[C]//2011 international conference on computer vision. Piscataway:IEEE, 2011:2206-2213.
[58] DHAR S, ORDONEZ V, BERG T L. High level describable attributes for predicting aesthetics and interestingness[C]//CVPR2011. Piscataway:IEEE, 2011:1657-1664.
[59] MAVRIDAKI E, MEZARIS V. A comprehensive aesthetic quality assessment method for natural images using basic rules of photography[C]//2015 IEEE international conference on image processing (ICIP). Piscataway:IEEE, 2015:887-891.
[60] ZHOU B, LAPEDRIZA A, KHOSLA A, et al. Places:a10 million image database for scene recognition[J]. IEEE transactions on pattern analysis and machine intelligence, 2017,40(6):1452-1464.
[61] HUTTO C, GILBERT E. Vader:a parsimonious rule-based model for sentiment analysis of social media text[C]//Proceedings of the international AAAI conference on web and social media. Menlo Park:AAAI, 2014, 8(1):216-225.
[62] BROCK A, DONAHUE J, SIMONYAN K. Large scale GAN training for high fidelity natural image synthesis[J]. arxiv preprint arxiv:1809.11096, 2018.
[63] KARRAS T, AITTALA M, LAINE S, et al. Alias-free generative adversarial networks[J]. Advances in neural information processing systems, 2021(34):852-863.
[64] WANG X D, ZHOU X, FATHI A, et al. Visual lexicon:rich image features in language space[J]. arXiv preprint arXiv:2412.06774, 2024.
[65] DEVLIN J, CHANG M W, LEE K, et al. Bert:pre-training of deep bidirectional transformers for language understanding[J].arxiv preprint arxiv:1810.04805, 2018.
[66] BROWN T, MANN B, RYDER N, et al. Language models are few-shot learners[J]. Advances in neural information processing systems, 2020, 33:1877-1901.
[67] CRESWELL A, WHITE T, DUMOULIN V, et al. Generative adversarial networks:an overview[J]. IEEE signal processing magazine, 2018, 35(1):53-65.
[68] FENG F, XIE Y, WANG J, et al. Redefining<Creative> in dictionary:towards a enhanced semantic understanding of creative generation[J]. arXiv preprint arXiv:2410.24160, 2024.
[69] AN J, ZHU Y, LI Z, et al. AGFSync:leveraging AI-Generated feedback for preference optimization in text-to-image generation[J]. arxiv preprint arxiv:2403.13352, 2024.
[70] SHIMODA W, INOUE N, HARAGUCHI D, et al. Type-R:automatically retouching typos for text-to-image generation[J].arXiv preprint arXiv:2411.18159, 2024.
[71] LEPCHA D C, GOYAL B, DOGRA A, et al. A deep journey into image enhancement:a survey of current and emerging trends[J].Information fusion, 2023, 93:36-76.
[72] HONG S. Smoothed energy guidance:guiding diffusion models with reduced energy curvature of attention[J]. arXiv preprint arXiv:2408.00760, 2024.
