{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T21:47:05Z","timestamp":1743112025457,"version":"3.40.3"},"publisher-location":"Cham","reference-count":50,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031781216"},{"type":"electronic","value":"9783031781223"}],"license":[{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T00:00:00Z","timestamp":1733356800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-78122-3_27","type":"book-chapter","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T07:15:51Z","timestamp":1733296551000},"page":"424-440","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["FewConv: Efficient Variant Convolution for\u00a0Few-Shot Image Generation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6591-4809","authenticated-orcid":false,"given":"Si-Hao","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8473-077X","authenticated-orcid":false,"given":"Cong","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5741-9318","authenticated-orcid":false,"given":"Xiao-Ning","family":"Song","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6088-8824","authenticated-orcid":false,"given":"Jia-Sheng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0310-5778","authenticated-orcid":false,"given":"Xiao-Jun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,5]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Careil, M., Verbeek, J., Lathuili\u00e8re, S.: Few-shot semantic image synthesis with class affinity transfer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 23611\u201323620 (2023)","key":"27_CR1","DOI":"10.1109\/CVPR52729.2023.02261"},{"doi-asserted-by":"crossref","unstructured":"Chen, J., He, T., Zhuo, W., Ma, L., Ha, S., Chan, S.H.G.: Tvconv: efficient translation variant convolution for layout-aware visual processing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12548\u201312558 (2022)","key":"27_CR2","DOI":"10.1109\/CVPR52688.2022.01222"},{"doi-asserted-by":"crossref","unstructured":"Chen, J., et al.: Run, don\u2019t walk: chasing higher flops for faster neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12021\u201312031 (2023)","key":"27_CR3","DOI":"10.1109\/CVPR52729.2023.01157"},{"doi-asserted-by":"crossref","unstructured":"Chen, Y., et al.: Drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3435\u20133444 (2019)","key":"27_CR4","DOI":"10.1109\/ICCV.2019.00353"},{"doi-asserted-by":"crossref","unstructured":"Deshpande, I., et al.: Max-sliced wasserstein distance and its use for gans. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10648\u201310656 (2019)","key":"27_CR5","DOI":"10.1109\/CVPR.2019.01090"},{"doi-asserted-by":"crossref","unstructured":"Duan, Y., Niu, L., Hong, Y., Zhang, L.: Weditgan: few-shot image generation via latent space relocation. arXiv preprint arXiv:2305.06671 (2023)","key":"27_CR6","DOI":"10.1609\/aaai.v38i2.27932"},{"doi-asserted-by":"crossref","unstructured":"Gu, Z., Li, W., Huo, J., Wang, L., Gao, Y.: Lofgan: fusing local representations for few-shot image generation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 8463\u20138471 (2021)","key":"27_CR7","DOI":"10.1109\/ICCV48922.2021.00835"},{"doi-asserted-by":"crossref","unstructured":"Hong, Y., Niu, L., Zhang, J., Zhao, W., Fu, C., Zhang, L.: F2gan: fusing-and-filling gan for few-shot image generation. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2535\u20132543 (2020)","key":"27_CR8","DOI":"10.1145\/3394171.3413561"},{"key":"27_CR9","doi-asserted-by":"publisher","first-page":"28966","DOI":"10.1109\/ACCESS.2023.3259066","volume":"11","author":"L Hou","year":"2023","unstructured":"Hou, L.: Regularizing label-augmented generative adversarial networks under limited data. IEEE Access 11, 28966\u201328976 (2023)","journal-title":"IEEE Access"},{"unstructured":"Howard, A.G., et al.: Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)","key":"27_CR10"},{"doi-asserted-by":"crossref","unstructured":"Hu, C., Li, Y., Feng, Z., Wu, X.: Attention-guided evolutionary attack with elastic-net regularization on face recognition. Pattern Recogn. 109760 (2023)","key":"27_CR11","DOI":"10.1016\/j.patcog.2023.109760"},{"issue":"9","key":"27_CR12","doi-asserted-by":"publisher","first-page":"4603","DOI":"10.1109\/TCSVT.2023.3244786","volume":"33","author":"Y Hu","year":"2023","unstructured":"Hu, Y., Wang, Y., Zhang, J.: Dear-gan: degradation-aware face restoration with gan prior. IEEE Trans. Circuits Syst. Video Technol. 33(9), 4603\u20134615 (2023). https:\/\/doi.org\/10.1109\/TCSVT.2023.3244786","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"27_CR13","first-page":"21655","volume":"34","author":"L Jiang","year":"2021","unstructured":"Jiang, L., Dai, B., Wu, W., Loy, C.C.: Deceive d: adaptive pseudo augmentation for gan training with limited data. Adv. Neural. Inf. Process. Syst. 34, 21655\u201321667 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"27_CR14","first-page":"12104","volume":"33","author":"T Karras","year":"2020","unstructured":"Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data. Adv. Neural. Inf. Process. Syst. 33, 12104\u201312114 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"27_CR15","first-page":"852","volume":"34","author":"T Karras","year":"2021","unstructured":"Karras, T., et al.: Alias-free generative adversarial networks. Adv. Neural. Inf. Process. Syst. 34, 852\u2013863 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401\u20134410 (2019)","key":"27_CR16","DOI":"10.1109\/CVPR.2019.00453"},{"doi-asserted-by":"crossref","unstructured":"Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8110\u20138119 (2020)","key":"27_CR17","DOI":"10.1109\/CVPR42600.2020.00813"},{"issue":"5","key":"27_CR18","doi-asserted-by":"publisher","first-page":"2614","DOI":"10.1109\/TIP.2018.2887342","volume":"28","author":"H Li","year":"2019","unstructured":"Li, H., Wu, X.J.: Densefuse: a fusion approach to infrared and visible images. IEEE Trans. Image Process. 28(5), 2614\u20132623 (2019). https:\/\/doi.org\/10.1109\/TIP.2018.2887342","journal-title":"IEEE Trans. Image Process."},{"key":"27_CR19","doi-asserted-by":"publisher","first-page":"4733","DOI":"10.1109\/TIP.2020.2975984","volume":"29","author":"H Li","year":"2020","unstructured":"Li, H., Wu, X.J., Kittler, J.: Mdlatlrr: a novel decomposition method for infrared and visible image fusion. IEEE Trans. Image Process. 29, 4733\u20134746 (2020). https:\/\/doi.org\/10.1109\/TIP.2020.2975984","journal-title":"IEEE Trans. Image Process."},{"doi-asserted-by":"crossref","unstructured":"Li, J., Wen, Y., He, L.: Scconv: spatial and channel reconstruction convolution for feature redundancy. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6153\u20136162 (2023)","key":"27_CR20","DOI":"10.1109\/CVPR52729.2023.00596"},{"unstructured":"Li, Y., Zhang, R., Lu, J., Shechtman, E.: Few-shot image generation with elastic weight consolidation. arXiv preprint arXiv:2012.02780 (2020)","key":"27_CR21"},{"doi-asserted-by":"crossref","unstructured":"Lin, H., Han, G., Ma, J., Huang, S., Lin, X., Chang, S.F.: Supervised masked knowledge distillation for few-shot transformers. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 19649\u201319659 (2023)","key":"27_CR22","DOI":"10.1109\/CVPR52729.2023.01882"},{"unstructured":"Liu, B., Zhu, Y., Song, K., Elgammal, A.: Towards faster and stabilized gan training for high-fidelity few-shot image synthesis. In: International Conference on Learning Representations (2021)","key":"27_CR23"},{"issue":"1","key":"27_CR24","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1007\/s11063-022-10910-w","volume":"55","author":"Z Liu","year":"2023","unstructured":"Liu, Z., Song, X., Feng, Z., Xu, T., Wu, X., Kittler, J.: Global context-aware feature extraction and visible feature enhancement for occlusion-invariant pedestrian detection in crowded scenes. Neural Process. Lett. 55(1), 803\u2013817 (2023)","journal-title":"Neural Process. Lett."},{"doi-asserted-by":"crossref","unstructured":"Lu, Z., Deb, K., Boddeti, V.N.: Muxconv: information multiplexing in convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12044\u201312053 (2020)","key":"27_CR25","DOI":"10.1109\/CVPR42600.2020.01206"},{"doi-asserted-by":"crossref","unstructured":"Mangla, P., Kumari, N., Singh, M., Krishnamurthy, B., Balasubramanian, V.N.: Data instance prior (disp) in generative adversarial networks. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 451\u2013461 (2022)","key":"27_CR26","DOI":"10.1109\/WACV51458.2022.00353"},{"doi-asserted-by":"crossref","unstructured":"Ni, M., Li, X., Zuo, W.: Nuwa-lip: language-guided image inpainting with defect-free vqgan. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14183\u201314192 (2023)","key":"27_CR27","DOI":"10.1109\/CVPR52729.2023.01363"},{"doi-asserted-by":"crossref","unstructured":"Ojha, U., et al.: Few-shot image generation via cross-domain correspondence. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10743\u201310752 (2021)","key":"27_CR28","DOI":"10.1109\/CVPR46437.2021.01060"},{"doi-asserted-by":"crossref","unstructured":"Qi, Y., He, Y., Qi, X., Zhang, Y., Yang, G.: Dynamic snake convolution based on topological geometric constraints for tubular structure segmentation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6070\u20136079 (2023)","key":"27_CR29","DOI":"10.1109\/ICCV51070.2023.00558"},{"key":"27_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109702","volume":"142","author":"B Shi","year":"2023","unstructured":"Shi, B., Li, W., Huo, J., Zhu, P., Wang, L., Gao, Y.: Global-and local-aware feature augmentation with semantic orthogonality for few-shot image classification. Pattern Recogn. 142, 109702 (2023)","journal-title":"Pattern Recogn."},{"doi-asserted-by":"crossref","unstructured":"Skorokhodov, I., Tulyakov, S., Elhoseiny, M.: Stylegan-v: a continuous video generator with the price, image quality and perks of stylegan2. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3626\u20133636 (2022)","key":"27_CR31","DOI":"10.1109\/CVPR52688.2022.00361"},{"key":"27_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2022.104534","volume":"126","author":"A Srivastava","year":"2022","unstructured":"Srivastava, A., Chanda, S., Pal, U.: Aga-gan: attribute guided attention generative adversarial network with u-net for face hallucination. Image Vis. Comput. 126, 104534 (2022)","journal-title":"Image Vis. Comput."},{"doi-asserted-by":"crossref","unstructured":"Suzuki, T.: Teachaugment: data augmentation optimization using teacher knowledge. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10904\u201310914 (2022)","key":"27_CR33","DOI":"10.1109\/CVPR52688.2022.01063"},{"key":"27_CR34","doi-asserted-by":"publisher","first-page":"1882","DOI":"10.1109\/TIP.2021.3049346","volume":"30","author":"NT Tran","year":"2021","unstructured":"Tran, N.T., Tran, V.H., Nguyen, N.B., Nguyen, T.K., Cheung, N.M.: On data augmentation for gan training. IEEE Trans. Image Process. 30, 1882\u20131897 (2021)","journal-title":"IEEE Trans. Image Process."},{"doi-asserted-by":"crossref","unstructured":"Wang, Y., Gonzalez-Garcia, A., Berga, D., Herranz, L., Khan, F.S., Weijer, J.V.D.: Minegan: effective knowledge transfer from gans to target domains with few images. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9332\u20139341 (2020)","key":"27_CR35","DOI":"10.1109\/CVPR42600.2020.00935"},{"issue":"2","key":"27_CR36","doi-asserted-by":"publisher","first-page":"490","DOI":"10.1007\/s11263-023-01882-y","volume":"132","author":"Y Wang","year":"2024","unstructured":"Wang, Y., et al.: Minegan++: mining generative models for efficient knowledge transfer to limited data domains. Int. J. Comput. Vision 132(2), 490\u2013514 (2024)","journal-title":"Int. J. Comput. Vision"},{"unstructured":"Wu, X., Wang, H., Wu, Y., Li, X.: D3t-gan: data-dependent domain transfer gans for few-shot image generation. arXiv preprint arXiv:2205.06032 (2022)","key":"27_CR37"},{"issue":"7","key":"27_CR38","doi-asserted-by":"publisher","first-page":"3270","DOI":"10.1109\/TCSVT.2022.3232330","volume":"33","author":"G Xia","year":"2023","unstructured":"Xia, G., Luo, D., Zhang, Z., Sun, Y., Liu, Q.: 3d information guided motion transfer via sequential image based human model refinement and face-attention gan. IEEE Trans. Circuits Syst. Video Technol. 33(7), 3270\u20133283 (2023). https:\/\/doi.org\/10.1109\/TCSVT.2022.3232330","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"doi-asserted-by":"crossref","unstructured":"Xiao, J., Li, L., Wang, C., Zha, Z.J., Huang, Q.: Few shot generative model adaption via relaxed spatial structural alignment (2022)","key":"27_CR39","DOI":"10.1109\/CVPR52688.2022.01092"},{"doi-asserted-by":"crossref","unstructured":"Xiao, J., Li, L., Wang, C., Zha, Z.J., Huang, Q.: Few shot generative model adaption via relaxed spatial structural alignment. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11204\u201311213 (2022)","key":"27_CR40","DOI":"10.1109\/CVPR52688.2022.01092"},{"issue":"1","key":"27_CR41","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1109\/TCSVT.2022.3199496","volume":"33","author":"J Xu","year":"2023","unstructured":"Xu, J., Liu, B., Xiao, Y.: A variational inference method for few-shot learning. IEEE Trans. Circuits Syst. Video Technol. 33(1), 269\u2013282 (2023). https:\/\/doi.org\/10.1109\/TCSVT.2022.3199496","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"unstructured":"Yang, C., et al.: One-shot generative domain adaptation. arXiv preprint arXiv:2111.09876 (2021)","key":"27_CR42"},{"doi-asserted-by":"crossref","unstructured":"Yang, C., et al.: One-shot generative domain adaptation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7733\u20137742 (2023)","key":"27_CR43","DOI":"10.1109\/ICCV51070.2023.00711"},{"doi-asserted-by":"publisher","unstructured":"Yang, M., Wang, Z., Chi, Z., Feng, W.: Wavegan: frequency-aware gan for high-fidelity few-shot image generation. In: European Conference on Computer Vision, pp. 1\u201317. Springer, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-031-19784-0_1","key":"27_CR44","DOI":"10.1007\/978-3-031-19784-0_1"},{"issue":"11","key":"27_CR45","doi-asserted-by":"publisher","first-page":"4258","DOI":"10.1109\/TCSVT.2019.2953753","volume":"30","author":"M Yuan","year":"2020","unstructured":"Yuan, M., Peng, Y.: Bridge-gan: interpretable representation learning for text-to-image synthesis. IEEE Trans. Circuits Syst. Video Technol. 30(11), 4258\u20134268 (2020). https:\/\/doi.org\/10.1109\/TCSVT.2019.2953753","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"unstructured":"Zhang, D., Khoreva, A.: Pa-gan: improving gan training by progressive augmentation (2019)","key":"27_CR46"},{"unstructured":"Zhao, M., Cong, Y., Carin, L.: On leveraging pretrained gans for generation with limited data. In: International Conference on Machine Learning, pp. 11340\u201311351. PMLR (2020)","key":"27_CR47"},{"key":"27_CR48","first-page":"7559","volume":"33","author":"S Zhao","year":"2020","unstructured":"Zhao, S., Liu, Z., Lin, J., Zhu, J.Y., Han, S.: Differentiable augmentation for data-efficient gan training. Adv. Neural. Inf. Process. Syst. 33, 7559\u20137570 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"27_CR49","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1109\/TMM.2021.3050073","volume":"24","author":"XF Zhu","year":"2022","unstructured":"Zhu, X.F., Wu, X.J., Xu, T., Feng, Z.H., Kittler, J.: Robust visual object tracking via adaptive attribute-aware discriminative correlation filters. IEEE Trans. Multimedia 24, 301\u2013312 (2022). https:\/\/doi.org\/10.1109\/TMM.2021.3050073","journal-title":"IEEE Trans. Multimedia"},{"doi-asserted-by":"publisher","unstructured":"Zhu, Y., Zhao, W., Tang, Y., Rao, Y., Zhou, J., Lu, J.: Stableswap: stable face swapping in a shared and controllable latent space. IEEE Trans. Multimedia, 1\u201314 (2024). https:\/\/doi.org\/10.1109\/TMM.2024.3369853","key":"27_CR50","DOI":"10.1109\/TMM.2024.3369853"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78122-3_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T08:14:47Z","timestamp":1733300087000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78122-3_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,5]]},"ISBN":["9783031781216","9783031781223"],"references-count":50,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78122-3_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,12,5]]},"assertion":[{"value":"5 December 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kolkata","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"India","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icpr2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icpr2024.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}