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ITU-R, B.T.: Methodology for the subjective assessment of the quality of television pictures. https://www.itu.int/rec/R-REC-BT.500 (2002)
2002
Earlier work this paper cites.
Tsukida, K., Gupta, M.R.: How to analyze paired comparison data (Technical Report UWEETR-2011-0004, University of Washington, 2011), https://api.semanticscholar.org/CorpusID:15425240
2011
Earlier work this paper cites.
Jayaraman, D., Mittal, A., Moorthy, A.K., Bovik, A.C.: Objective quality assessment of multiply distorted images. In: ASILOMAR. pp. 1693–1697 (2012)
2012
Earlier work this paper cites.
Mantiuk, R.K., Tomaszewska, A., Mantiuk, R.: Comparison of four subjective methods for image quality assessment. In: Computer Graphics Forum. vol. 31, pp. 2478–2491 (2012)
2012
Earlier work this paper cites.
Wauthier, F., Jordan, M., Jojic, N.: Efficient ranking from pairwise comparisons. In: ICML. pp. 109–117 (2013)
2013
Earlier work this paper cites.
Ye, P., Doermann, D.: Active sampling for subjective image quality assessment. In: IEEE CVPR. pp. 4249–4256 (2014)
2014
Earlier work this paper cites.
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C.L., Parikh, D.: VQA: Visual Question Answering. In: IEEE ICCV. pp. 2425–2433 (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Ghadiyaram, D., Bovik, A.C.: Massive online crowdsourced study of subjective and objective picture quality. IEEE TIP 25
2016
Earlier work this paper cites.
Rajkumar, A., Agarwal, S.: When can we rank well from comparisons of o (nlog (n)) non-actively chosen pairs? In: Conference on Learning Theory. pp. 1376–1401 (2016)
2016
Earlier work this paper cites.
Thomee, B., Shamma, D.A., Friedland, G., Elizalde, B., Ni, K., Poland, D., Borth, D., Li, L.J.: YFCC100M: The new data in multimedia research. Commun. ACM 59
2016
Earlier work this paper cites.
Liu, X., Van De Weijer, J., Bagdanov, A.D.: RankIQA: Learning from rankings for no-reference image quality assessment. In: IEEE ICCV. pp. 1040–1049 (2017)
2017
Earlier work this paper cites.
Ma, K., Liu, W., Liu, T., Wang, Z., Tao, D.: dipIQ: Blind image quality assessment by learning-to-rank discriminable image pairs. IEEE TIP 26
2017
Earlier work this paper cites.
Li, J., Mantiuk, R., Wang, J., Ling, S., Le Callet, P.: Hybrid-MST: A hybrid active sampling strategy for pairwise preference aggregation. In: NeurIPS. pp. 1–11 (2018)
2018
Earlier work this paper cites.
Prashnani, E., Cai, H., Mostofi, Y., Sen, P.: PieAPP: Perceptual image-error assessment through pairwise preference. In: IEEE CVPR. pp. 1808–1817 (2018)
2018
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: IEEE CVPR. pp. 586–595 (2018)
2018
Earlier work this paper cites.
Hudson, D.A., Manning, C.D.: GQA: A new dataset for real-world visual reasoning and compositional question answering. In: IEEE CVPR. pp. 6700–6709 (2019)
2019
Earlier work this paper cites.
Li, D., Jiang, T., Jiang, M.: Quality assessment of in-the-wild videos. In: ACM MM. pp. 2351–2359 (2019)
2019
Earlier work this paper cites.
Li, D., Jiang, T., Lin, W., Jiang, M.: Which has better visual quality: The clear blue sky or a blurry animal? IEEE TMM 21
2019
Earlier work this paper cites.
Lin, H., Hosu, V., Saupe, D.: KADID-10k: A large-scale artificially distorted iqa database. In: QoMEX. pp. 1–3 (2019)
2019
Earlier work this paper cites.
Liu, X., Van De Weijer, J., Bagdanov, A.D.: Exploiting unlabeled data in cnns by self-supervised learning to rank. IEEE TPAMI 41
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Zhang, W., Liu, Y., Dong, C., Qiao, Y.: RankSRGan: Generative adversarial networks with ranker for image super-resolution. In: ICCV. pp. 3096–3105 (2019)
2019
Earlier work this paper cites.
Fang, Y., Zhu, H., Zeng, Y., Ma, K., Wang, Z.: Perceptual quality assessment of smartphone photography. In: IEEE CVPR. pp. 3677–3686 (2020)
2020
Cited alongside, same era.
Gu, J., Cai, H., Chen, H., Ye, X., Ren, J., Dong, C.: PIPAL: A large-scale image quality assessment dataset for perceptual image restoration. In: ECCV. pp. 633–651 (2020)
2020
Cited alongside, same era.
Hosu, V., Lin, H., Sziranyi, T., Saupe, D.: Koniq-10k: An ecologically valid database for deep learning of blind image quality assessment. IEEE TIP 29
2020
Cited alongside, same era.
Yim, J.G., Wang, Y., Birkbeck, N., Adsumilli, B.: Subjective quality assessment for youtube UGC dataset. In: IEEE ICIP. pp. 1–5 (2020)
2020
Cited alongside, same era.
Ying, Z., Niu, H., Gupta, P., Mahajan, D., Ghadiyaram, D., Bovik, A.: From patches to pictures (PaQ-2-PiQ): Mapping the perceptual space of picture quality. In: IEEE CVPR. pp. 3575–3585 (2020)
OpenAI: Gpt-4 technical report (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Wu, H., Chen, C., Liao, L., Hou, J., Sun, W., Yan, Q., Gu, J., Lin, W.: Neighbourhood representative sampling for efficient end-to-end video quality assessment. IEEE TPAMI (2023)
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2020
Cited alongside, same era.
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., Steinhardt, J.: Measuring massive multitask language understanding. In: ICLR. pp. 1–10 (2021)
2021
Cited alongside, same era.
Li, Y., Wang, S., Zhang, X., Wang, S., Ma, S., Wang, Y.: Quality assessment of end-to-end learned image compression: The benchmark and objective measure. In: ACM MM. pp. 4297–4305 (2021)
2021
Cited alongside, same era.
Mikhailiuk, A., Wilmot, C., Perez-Ortiz, M., Yue, D., Mantiuk, R.K.: Active sampling for pairwise comparisons via approximate message passing and information gain maximization. In: IEEE ICPR. pp. 2559–2566 (2021)
2021
Cited alongside, same era.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: ICML. pp. 8748–8763 (2021)
2021
Cited alongside, same era.
Zhang, W., Ma, K., Zhai, G., Yang, X.: Uncertainty-aware blind image quality assessment in the laboratory and wild. IEEE TIP 30
2021
Cited alongside, same era.
Golestaneh, S.A., Dadsetan, S., Kitani, K.M.: No-reference image quality assessment via transformers, relative ranking, and self-consistency. In: IEEE WACV. pp. 3209–3218 (2022)
2022
Cited alongside, same era.
Schwenk, D., Khandelwal, A., Clark, C., Marino, K., Mottaghi, R.: A-OKVQA: A benchmark for visual question answering using world knowledge. In: ECCV. pp. 146–162 (2022)
2022
Cited alongside, same era.
2023
Later among the works it cites.
Wu, H., Zhang, E., Liao, L., Chen, C., Hou, J., Wang, A., Sun, W., Yan, Q., Lin, W.: Exploring video quality assessment on user generated contents from aesthetic and technical perspectives. In: IEEE ICCV (2023)
2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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2023
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Zhang, W., Zhai, G., Wei, Y., Yang, X., Ma, K.: Blind image quality assessment via vision-language correspondence: A multitask learning perspective. In: IEEE CVPR. pp. 14071–14081 (2023)
2023
Later among the works it cites.
Zhang, Z., Sun, W., Wang, T., Lu, W., Zhou, Q., Wang, Q., Min, X., Zhai, G., et al.: Subjective and objective quality assessment for in-the-wild computer graphics images. ACM TOMM 20
2023
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2023
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2024
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SkunkworksAI: BakLLaVA (2024), https://github.com/SkunkworksAI/BakLLaVA
2024
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2024
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Wu, H., Zhang, Z., Zhang, E., Chen, C., Liao, L., Wang, A., Li, C., Sun, W., Yan, Q., Zhai, G., Lin, W.: Q-Bench: A benchmark for general-purpose foundation models on low-level vision. In: ICLR (2024)
2024
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