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The explosion of visual content available online underscores the requirement for an accurate machine assessor to robustly evaluate scores across diverse types of visual contents.
ITU-R Rec. BT.500, 2000
Recommendation 500-10: Methodology for the subjective assessment of the quality of television pictures · 2000
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Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A., Sheikh, H., and Simoncelli, E · 2003
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Live image quality assessment database release 2
Sheikh, H. R., Wang, Z., Cormack, L., and Bovik, A. C · 2005
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No-reference image quality assessment in the spatial domain
Mittal, A., Moorthy, A. K., and Bovik, A. C · 2012
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Ava: A large-scale database for aesthetic visual analysis
Murray, N., Marchesotti, L., and Perronnin, F · 2012
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Making a “completely blind” image quality analyzer
Mittal, A., Soundararajan, R., and Bovik, A. C · 2013
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Photo aesthetics ranking network with attributes and content adaptation
Kong, S., Shen, X., Lin, Z., Mech, R., and Fowlkes, C · 2016
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Ava: A video dataset of spatio-temporally localized atomic visual actions
Gu, C., Sun, C., Ross, D. A., Vondrick, C., Pantofaru, C., Li, Y., Vijayanarasimhan, S., Toderici, G., Ricco, S., Sukthankar, R., Schmid, C., and Malik, J · 2018
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Nima: Neural image assessment
Talebi, H. and Milanfar, P · 2018
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Aesthetic image captioning from weakly-labelled photographs
Ghosal, K., Rana, A., and Smolic, A · 2019
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Effective aesthetics prediction with multi-level spatially pooled features
Hosu, V., Goldlücke, B., and Saupe, D · 2019
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Two-level approach for no-reference consumer video quality assessment
Korhonen, J · 2019
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Quality assessment of in-the-wild videos
Li, D., Jiang, T., and Jiang, M · 2019
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Language models are unsupervised multitask learners, 2019
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
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Perceptual quality assessment of smartphone photography
Fang, Y., Zhu, H., Zeng, Y., Ma, K., and Wang, Z · 2020
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Blindly assess image quality in the wild guided by a self-adaptive hyper network
Su, S., Yan, Q., Zhu, Y., Zhang, C., Ge, X., Sun, J., and Zhang, Y · 2020
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Blind image quality assessment using a deep bilinear convolutional neural network
Zhang, W., Ma, K., Yan, J., Deng, D., and Wang, Z · 2020
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Musiq: Multi-scale image quality transformer
Ke, J., Wang, Q., Wang, Y., Milanfar, P., and Yang, F · 2021
Coca: Contrastive captioners are image-text foundation models
Yu, J., Wang, Z., Vasudevan, V., Yeung, L., Seyedhosseini, M., and Wu, Y · 2022
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Learning to prompt for vision-language models
Zhou, K., Yang, J., Loy, C. C., and Liu, Z · 2022
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Shikra: Unleashing multimodal llm’s referential dialogue magic
Chen, K., Zhang, Z., Zeng, W., Zhang, R., Zhu, F., and Zhao, R · 2023
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Instructblip: Towards general-purpose vision-language models with instruction tuning, 2023
Dai, W., Li, J., Li, D., Tiong, A. M. H., Zhao, J., Wang, W., Li, B., Fung, P., and Hoi, S · 2023
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Llama-adapter v2: Parameter-efficient visual instruction model
Gao, P., Han, J., Zhang, R., Lin, Z., Geng, S., Zhou, A., Zhang, W., Lu, P., He, C., Yue, X., Li, H., and Qiao, Y · 2023
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Learning transferable visual models from natural language supervision, 2021
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I · 2021
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Patch-vq: ’patching up’ the video quality problem
Ying, Z., Mandal, M., Ghadiyaram, D., and Bovik, A · 2021
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Blindly assess quality of in-the-wild videos via quality-aware pre-training and motion perception
Li, B., Zhang, W., Tian, M., Zhai, G., and Wang, X · 2022
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A deep learning based no-reference quality assessment model for ugc videos
Sun, W., Min, X., Lu, W., and Zhai, G · 2022
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Exploring clip for assessing the look and feel of images, 2022
Wang, J., Chan, K. C. K., and Loy, C. C · 2022
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Fast-vqa: Efficient end-to-end video quality assessment with fragment sampling
Wu, H., Chen, C., Hou, J., Liao, L., Wang, A., Sun, W., Yan, Q., and Lin, W · 2022
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Towards transparent deep image aesthetics assessment with tag-based content descriptors
Hou, J., Lin, W., Fang, Y., Wu, H., Chen, C., Liao, L., and Liu, W · 2023
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Vila: Learning image aesthetics from user comments with vision-language pretraining, 2023
Ke, J., Ye, K., Yu, J., Wu, Y., Milanfar, P., and Yang, F · 2023
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Aesthetic predictor
LAION · 2023
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Agiqa-3k: An open database for ai-generated image quality assessment, 2023
Li, C., Zhang, Z., Wu, H., Sun, W., Min, X., Liu, X., Zhai, G., and Lin, W · 2023
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Llama 2: Open foundation and fine-tuned chat models, 2023
Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D., Blecher, L., Ferrer, C. C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., Fuller, B., Gao, C., Goswami, V., Goyal, N., Hartshorn, A., Hosseini, S., Hou, R., Inan, H., Kardas, M., Kerkez, V., Khabsa, M., Kloumann, I., Korenev, A., Koura, P. S., Lachaux, M.-A., Lavril, T., Lee, J., Liskovich, D., Lu, Y., Mao, Y., Martinet, X., Mihaylov, T., Mishra, P., Molybog, I., Nie, Y., Poulton, A., Reizenstein, J., Rungta, R., Saladi, K., Schelten, A., Silva, R., Smith, E. M., Subramanian, R., Tan, X. E., Tang, B., Taylor, R., Williams, A., Kuan, J. X., Xu, P., Yan, Z., Zarov, I., Zhang, Y., Fan, A., Kambadur, M., Narang, S., Rodriguez, A., Stojnic, R., Edunov, S., and Scialom, T · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena, 2023
Zheng, L., Chiang, W.-L., Sheng, Y., Zhuang, S., Wu, Z., Zhuang, Y., Lin, Z., Li, Z., Li, D., Xing, E. P., Zhang, H., Gonzalez, J. E., and Stoica, I · 2023
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