概要
长期以来困扰人工智能领域的一个问题是: 人工智能是否具有创造力, 或者说, 算法的推理过程是否可以具有创造性. 本文从思维科学的角度探讨人工智能创造力的问题. 首先, 列举形象思维推理的相关研究; 然后, 重点介绍一种特殊的视觉知识表示形式, 即视觉场景图; 最后, 详细介绍视觉场景图构造问题与潜在应用. 所有证据表明, 视觉知识和视觉思维不仅可以改善当前人工智能任务的性能, 而且可以用于机器创造力的实践.
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Arnheim R, 1997. Visual Thinking. University of California Press, San Francisco, USA.
Bau D, Zhu JY, Wulff J, et al., 2019. Seeing what a GAN cannot generate. Proc IEEE/CVF Int Conf on Computer Vision, p.4501–4510. https://doi.org/10.1109/ICCV.2019.00460
Chen L, Zhang HW, Xiao J, et al., 2019. Counterfactual critic multi-agent training for scene graph generation. Proc IEEE/CVF Int Conf on Computer Vision, p.4612–4622. https://doi.org/10.1109/ICCV.2019.00471
Denis M, 1991. Imagery and thinking. In: Cornoldi C, McDaniel MA (Eds.), Imagery and Cognition. Springer, New York, NY, USA, p.103–131. https://doi.org/10.1007/978-1-4684-6407-8_4
Elgammal A, Liu BC, Elhoseiny M, et al., 2017. CAN: creative adversarial networks, generating “art” by learning about styles and deviating from style norms. https://arxiv.org/abs/1706.07068
Gazzaniga MS, 1967. The split brain in man. Sci Am, 217(2): 24–29. https://doi.org/10.1038/scientificamerican0867-24
Gu JX, Zhao HD, Lin Z, et al., 2019. Scene graph generation with external knowledge and image reconstruction. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.1969–1978. https://doi.org/10.1109/CVPR.2019.00207
Haurilet M, Roitberg A, Stiefelhagen R, 2019. It’s not about the journey; it’s about the destination: following soft paths under question-guidance for visual reasoning. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.1930–1939. https://doi.org/10.1109/CVPR.2019.00203
Herzig R, Bar A, Xu HJ, et al., 2020. Learning canonical representations for scene graph to image generation. 16th European Conf on Computer Vision, p.210–227. https://doi.org/10.1007/978-3-030-58574-7_13
Hudson DA, Manning CD, 2019. GQA: a new dataset for real-world visual reasoning and compositional question answering. https://arxiv.org/abs/1902.09506
Johnson J, Gupta A, Li FF, 2018. Image generation from scene graphs. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.1219–1228. https://doi.org/10.1109/CVPR.2018.00133
Kolodner J, 2014. Case-Based Reasoning. Morgan Kaufmann, San Mateo, USA.
Krishna R, Zhu YK, Groth O, et al., 2017. Visual genome: connecting language and vision using crowdsourced dense image annotations. Int J Comput Vis, 123(1):32–73. https://doi.org/10.1007/s11263-016-0981-7
Li ML, Zareian A, Zeng Q, et al., 2020. Cross-media structured common space for multimedia event extraction. https://arxiv.org/abs/2005.02472
Li YL, Xu L, Huang XJ, et al., 2019. HAKE: human activity knowledge engine. https://arxiv.org/abs/1904.06539v2
Liu DQ, Zhang HW, Zha ZJ, et al., 2019. Referring expression grounding by marginalizing scene graph likelihood. https://arxiv.org/abs/1906.03561v1
McCarthy J, Minsky ML, Rochester N, et al., 2006. A proposal for the Dartmouth summer research project on artificial intelligence. AI Mag, 27(4):12–14.
Mittal G, Agrawal S, Agarwal A, et al., 2019. Interactive image generation using scene graphs. https://arxiv.org/abs/1905.03743
Mu Z, Tang S, Tan J, et al., 2021. Disentangled motif-aware graph learning for phrase grounding. Proc 35th AAAI Conf on Artificial Intelligence.
Norcliffe-Brown W, Vafeais E, Parisot S, 2018. Learning conditioned graph structures for interpretable visual question answering. https://arxiv.org/abs/1806.07243v1
Pan YH, 2019. On visual knowledge. Front Inform Technol Electron Eng, 20(8):1021–1025. https://doi.org/10.1631/FITEE.1910001
Pan YH, 2020a. Miniaturized five fundamental issues about visual knowledge. Front Inform Technol Electron Eng, online. https://doi.org/10.1631/FITEE.2040000
Pan YH, 2020b. Multiple knowledge representation of artificial intelligence. Engineering, 6(3):216–217. https://doi.org/10.1016/j.eng.2019.12.011
Radford A, Metz L, Chintala S, 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. https://arxiv.org/abs/1511.06434
Shen K, Wu LF, Xu FL, et al., 2020. Hierarchical attention based spatial-temporal graph-to-sequence learning for grounded video description. Proc 29th Int Joint Conf on Artificial Intelligence, p.941–947. https://doi.org/10.24963/ijcai.2020/131
Tripathi S, Bhiwandiwalla A, Bastidas A, et al., 2019. Using scene graph context to improve image generation. https://arxiv.org/abs/1901.03762
Yang JW, Lu JS, Lee S, et al., 2018. Graph R-CNN for scene graph generation. Proc 15th European Conf on Computer Vision, p.690–706. https://doi.org/10.1007/978-3-030-01246-5_41
Yang X, Tang KH, Zhang HW, et al., 2019. Auto-encoding scene graphs for image captioning. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.10677–10686. https://doi.org/10.1109/CVPR.2019.01094
Yang XY, Mei T, Xu YQ, et al., 2016. Automatic generation of visual-textual presentation layout. ACM Trans Multim Comput Commun Appl, 12(2):33. https://doi.org/10.1145/2818709
Yu RC, Li A, Morariu VI, et al., 2017. Visual relationship detection with internal and external linguistic knowledge distillation. Proc IEEE Int Conf on Computer Vision, p.1068–1076. https://doi.org/10.1109/ICCV.2017.121
Zareian A, Karaman S, Chang SF, 2020. Weakly supervised visual semantic parsing. Proc IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.3733–3742. https://doi.org/10.1109/CVPR42600.2020.00379
Zhang HW, Kyaw Z, Chang SF, et al., 2017. Visual translation embedding network for visual relation detection. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.3107–3115. https://doi.org/10.1109/CVPR.2017.331
Zhang W, Wang XE, Tang S, et al., 2020. Relational graph learning for grounded video description generation. Proc 28th ACM Int Conf on Multimedia, p.3807–3828. https://doi.org/10.1145/3394171.3413746
Zhang W, Shi H, Tang S, et al., 2021. Consensus graph representation learning for better grounded image captioning. Proc 35th AAAI Conf on Artificial Intelligence.
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Yueting ZHUANG provided the main idea and outlined the manuscript. Siliang TANG drafted the manuscript. Yueting ZHUANG and Siliang TANG revised and finalized the paper.
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Yueting ZHUANG and Siliang TANG declare that they have no conflict of interest.