{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T16:49:20Z","timestamp":1777654160844,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T00:00:00Z","timestamp":1665360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Shanghai Municipal Science and Technology Key Project","award":["20511100300"],"award-info":[{"award-number":["20511100300"]}]},{"name":"Shanghai Municipal Science and Technology Major Project","award":["2021SHZDZX0102"],"award-info":[{"award-number":["2021SHZDZX0102"]}]},{"name":"National Science Foundation of China","award":["61902247"],"award-info":[{"award-number":["61902247"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3503161.3548158","type":"proceedings-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T15:43:12Z","timestamp":1665416592000},"page":"2796-2804","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Few-shot Image Generation Using Discrete Content Representation"],"prefix":"10.1145","author":[{"given":"Yan","family":"Hong","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Niu","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianfu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340","author":"Antoniou Antreas","year":"2017","unstructured":"Antreas Antoniou , Amos Storkey , and Harrison Edwards . 2017. Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340 ( 2017 ). Antreas Antoniou, Amos Storkey, and Harrison Edwards. 2017. Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340 (2017)."},{"key":"e_1_3_2_2_2_1","unstructured":"Sergey Bartunov and Dmitry Vetrov. 2018. Few-shot generative modelling with generative matching networks. In AISTATS.  Sergey Bartunov and Dmitry Vetrov. 2018. Few-shot generative modelling with generative matching networks. In AISTATS."},{"key":"e_1_3_2_2_3_1","unstructured":"Sagie Benaim and Lior Wolf. 2018. One-Shot Unsupervised Cross Domain Translation. In NeurIPS.  Sagie Benaim and Lior Wolf. 2018. One-Shot Unsupervised Cross Domain Translation. In NeurIPS."},{"key":"e_1_3_2_2_4_1","volume-title":"Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432","author":"Bengio Yoshua","year":"2013","unstructured":"Yoshua Bengio , Nicholas L\u00e9onard , and Aaron Courville . 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 ( 2013 ). Yoshua Bengio, Nicholas L\u00e9onard, and Aaron Courville. 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013)."},{"key":"e_1_3_2_2_5_1","unstructured":"Andrew Brock Jeff Donahue and Karen Simonyan. 2018. Large Scale GAN Training for High Fidelity Natural Image Synthesis. In ICLR.  Andrew Brock Jeff Donahue and Karen Simonyan. 2018. Large Scale GAN Training for High Fidelity Natural Image Synthesis. In ICLR."},{"key":"e_1_3_2_2_6_1","volume-title":"FIGR: Few-shot image generation with reptile. arXiv preprint arXiv:1901.02199","author":"Clou\u00e2tre Louis","year":"2019","unstructured":"Louis Clou\u00e2tre and Marc Demers . 2019 . FIGR: Few-shot image generation with reptile. arXiv preprint arXiv:1901.02199 (2019). Louis Clou\u00e2tre and Marc Demers. 2019. FIGR: Few-shot image generation with reptile. arXiv preprint arXiv:1901.02199 (2019)."},{"key":"e_1_3_2_2_7_1","volume-title":"Imagenet: A large-scale hierarchical image database. In CVPR.","author":"Deng Jia","year":"2009","unstructured":"Jia Deng , Wei Dong , Richard Socher , Li-Jia Li , Kai Li , and Li Fei-Fei . 2009 . Imagenet: A large-scale hierarchical image database. In CVPR. Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In CVPR."},{"key":"e_1_3_2_2_8_1","volume-title":"Words: Transformers for Image Recognition at Scale. In ICLR.","author":"Dosovitskiy Alexey","year":"2020","unstructured":"Alexey Dosovitskiy , Lucas Beyer , Alexander Kolesnikov , Dirk Weissenborn , Xiaohua Zhai , Thomas Unterthiner , Mostafa Dehghani , Matthias Minderer , Georg Heigold , Sylvain Gelly , 2020 . An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In ICLR. Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. 2020. An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In ICLR."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Patrick Esser Robin Rombach and Bjorn Ommer. 2021. Taming transformers for high-resolution image synthesis. In CVPR.  Patrick Esser Robin Rombach and Bjorn Ommer. 2021. Taming transformers for high-resolution image synthesis. In CVPR.","DOI":"10.1109\/CVPR46437.2021.01268"},{"key":"e_1_3_2_2_10_1","unstructured":"Yuchen Fan Jiahui Yu Ding Liu and Thomas S Huang. 2020. Scale-wise convolution for image restoration. In AAAI.  Yuchen Fan Jiahui Yu Ding Liu and Thomas S Huang. 2020. Scale-wise convolution for image restoration. In AAAI."},{"key":"e_1_3_2_2_11_1","unstructured":"Chelsea Finn Pieter Abbeel and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML.  Chelsea Finn Pieter Abbeel and Sergey Levine. 2017. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML."},{"key":"e_1_3_2_2_12_1","unstructured":"Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR.  Kaiming He Xiangyu Zhang Shaoqing Ren and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR."},{"key":"e_1_3_2_2_13_1","unstructured":"Martin Heusel Hubert Ramsauer Thomas Unterthiner Bernhard Nessler and Sepp Hochreiter. 2017. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In NeurIPS.  Martin Heusel Hubert Ramsauer Thomas Unterthiner Bernhard Nessler and Sepp Hochreiter. 2017. GANs trained by a two time-scale update rule converge to a local nash equilibrium. In NeurIPS."},{"key":"e_1_3_2_2_14_1","volume-title":"Matchinggan: Matching-Based Few-Shot Image Generation. In ICME.","author":"Hong Yan","year":"2020","unstructured":"Yan Hong , Li Niu , Jianfu Zhang , and Liqing Zhang . 2020 a. Matchinggan: Matching-Based Few-Shot Image Generation. In ICME. Yan Hong, Li Niu, Jianfu Zhang, and Liqing Zhang. 2020a. Matchinggan: Matching-Based Few-Shot Image Generation. In ICME."},{"key":"e_1_3_2_2_15_1","volume-title":"DeltaGAN: Towards diverse few-shot image generation with sample-specific delta. ECCV","author":"Hong Yan","year":"2022","unstructured":"Yan Hong , Li Niu , Jianfu Zhang , and Liqing Zhang . 2022. DeltaGAN: Towards diverse few-shot image generation with sample-specific delta. ECCV ( 2022 ). Yan Hong, Li Niu, Jianfu Zhang, and Liqing Zhang. 2022. DeltaGAN: Towards diverse few-shot image generation with sample-specific delta. ECCV (2022)."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"crossref","unstructured":"Yan Hong Li Niu Jianfu Zhang Weijie Zhao Chen Fu and Liqing Zhang. 2020b. F2GAN: Fusing-and-Filling GAN for Few-shot Image Generation. In ACM MM.  Yan Hong Li Niu Jianfu Zhang Weijie Zhao Chen Fu and Liqing Zhang. 2020b. F2GAN: Fusing-and-Filling GAN for Few-shot Image Generation. In ACM MM.","DOI":"10.1145\/3394171.3413561"},{"key":"e_1_3_2_2_17_1","unstructured":"Yueyu Hu Wenhan Yang and Jiaying Liu. 2020. Coarse-to-fine hyper-prior modeling for learned image compression. In AAAI.  Yueyu Hu Wenhan Yang and Jiaying Liu. 2020. Coarse-to-fine hyper-prior modeling for learned image compression. In AAAI."},{"key":"e_1_3_2_2_18_1","unstructured":"Lukasz Kaiser Samy Bengio Aurko Roy Ashish Vaswani Niki Parmar Jakob Uszkoreit and Noam Shazeer. 2018. Fast decoding in sequence models using discrete latent variables. In ICML.  Lukasz Kaiser Samy Bengio Aurko Roy Ashish Vaswani Niki Parmar Jakob Uszkoreit and Noam Shazeer. 2018. Fast decoding in sequence models using discrete latent variables. In ICML."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"crossref","unstructured":"Tero Karras Samuli Laine and Timo Aila. 2019. A style-based generator architecture for generative adversarial networks. In CVPR.  Tero Karras Samuli Laine and Timo Aila. 2019. A style-based generator architecture for generative adversarial networks. In CVPR.","DOI":"10.1109\/CVPR.2019.00453"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Tero Karras Samuli Laine Miika Aittala Janne Hellsten Jaakko Lehtinen and Timo Aila. 2020. Analyzing and improving the image quality of styleGAN. In CVPR.  Tero Karras Samuli Laine Miika Aittala Janne Hellsten Jaakko Lehtinen and Timo Aila. 2020. Analyzing and improving the image quality of styleGAN. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"e_1_3_2_2_21_1","unstructured":"Alexander Kolesnikov and Christoph H Lampert. 2017. PixelCNN models with auxiliary variables for natural image modeling. In ICML.  Alexander Kolesnikov and Christoph H Lampert. 2017. PixelCNN models with auxiliary variables for natural image modeling. In ICML."},{"key":"e_1_3_2_2_22_1","volume-title":"Cognitive Science","volume":"33","author":"Lake Brenden M","year":"2011","unstructured":"Brenden M Lake , Ruslan Salakhutdinov , Jason Gross , and Joshua B Tenenbaum . 2011 . One shot learning of simple visual concepts . Cognitive Science , Vol. 33 , 33 (2011). Brenden M Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua B Tenenbaum. 2011. One shot learning of simple visual concepts. Cognitive Science, Vol. 33, 33 (2011)."},{"key":"e_1_3_2_2_23_1","unstructured":"Wenbin Li Lei Wang Jinglin Xu Jing Huo Yang Gao and Jiebo Luo. 2019. Revisiting local descriptor based image-to-class measure for few-shot learning. In CVPR.  Wenbin Li Lei Wang Jinglin Xu Jing Huo Yang Gao and Jiebo Luo. 2019. Revisiting local descriptor based image-to-class measure for few-shot learning. In CVPR."},{"key":"e_1_3_2_2_24_1","unstructured":"Yijun Li Richard Zhang Jingwan Lu and Eli Shechtman. 2020. Few-shot image generation with elastic weight consolidation. In NeurIPS.  Yijun Li Richard Zhang Jingwan Lu and Eli Shechtman. 2020. Few-shot image generation with elastic weight consolidation. In NeurIPS."},{"key":"e_1_3_2_2_25_1","volume-title":"DAWSON: A domain adaptive few shot generation framework. arXiv preprint arXiv:2001.00576","author":"Liang Weixin","year":"2020","unstructured":"Weixin Liang , Zixuan Liu , and Can Liu . 2020 . DAWSON: A domain adaptive few shot generation framework. arXiv preprint arXiv:2001.00576 (2020). Weixin Liang, Zixuan Liu, and Can Liu. 2020. DAWSON: A domain adaptive few shot generation framework. arXiv preprint arXiv:2001.00576 (2020)."},{"key":"e_1_3_2_2_26_1","volume-title":"GenDet: Meta Learning to Generate Detectors From Few Shots. Transactions on Neural Networks and Learning Systems","author":"Liu Liyang","year":"2021","unstructured":"Liyang Liu , Bochao Wang , Zhanghui Kuang , Jing-Hao Xue , Yimin Chen , Wenming Yang , Qingmin Liao , and Wayne Zhang . 2021. GenDet: Meta Learning to Generate Detectors From Few Shots. Transactions on Neural Networks and Learning Systems ( 2021 ). Liyang Liu, Bochao Wang, Zhanghui Kuang, Jing-Hao Xue, Yimin Chen, Wenming Yang, Qingmin Liao, and Wayne Zhang. 2021. GenDet: Meta Learning to Generate Detectors From Few Shots. Transactions on Neural Networks and Learning Systems (2021)."},{"key":"e_1_3_2_2_27_1","unstructured":"Ming-Yu Liu Xun Huang Arun Mallya Tero Karras Timo Aila Jaakko Lehtinen and Jan Kautz. 2019. Few-Shot Unsupervised Image-to-Image Translation. In ICCV.  Ming-Yu Liu Xun Huang Arun Mallya Tero Karras Timo Aila Jaakko Lehtinen and Jan Kautz. 2019. Few-Shot Unsupervised Image-to-Image Translation. In ICCV."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Maria-Elena Nilsback and Andrew Zisserman. 2008. Automated flower classification over a large number of classes. In CVGIP.  Maria-Elena Nilsback and Andrew Zisserman. 2008. Automated flower classification over a large number of classes. In CVGIP.","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"e_1_3_2_2_29_1","volume-title":"Eli Shechtman, and Richard Zhang.","author":"Ojha Utkarsh","year":"2021","unstructured":"Utkarsh Ojha , Yijun Li , Jingwan Lu , Alexei A Efros , Yong Jae Lee , Eli Shechtman, and Richard Zhang. 2021 . Few-shot Image Generation via Cross-domain Correspondence. In CVPR. Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A Efros, Yong Jae Lee, Eli Shechtman, and Richard Zhang. 2021. Few-shot Image Generation via Cross-domain Correspondence. In CVPR."},{"key":"e_1_3_2_2_30_1","unstructured":"A\"aron van den Oord Nal Kalchbrenner Oriol Vinyals Lasse Espeholt Alex Graves and Koray Kavukcuoglu. 2016. Conditional image generation with PixelCNN decoders. In NeurIPS.  A\"aron van den Oord Nal Kalchbrenner Oriol Vinyals Lasse Espeholt Alex Graves and Koray Kavukcuoglu. 2016. Conditional image generation with PixelCNN decoders. In NeurIPS."},{"key":"e_1_3_2_2_31_1","unstructured":"Niki Parmar Ashish Vaswani Jakob Uszkoreit Lukasz Kaiser Noam Shazeer Alexander Ku and Dustin Tran. 2018. Image transformer. In ICML.  Niki Parmar Ashish Vaswani Jakob Uszkoreit Lukasz Kaiser Noam Shazeer Alexander Ku and Dustin Tran. 2018. Image transformer. In ICML."},{"key":"e_1_3_2_2_32_1","unstructured":"Ali Razavi Aaron van den Oord and Oriol Vinyals. 2019. Generating diverse high-fidelity images with vq-vae-2. In NeurIPS.  Ali Razavi Aaron van den Oord and Oriol Vinyals. 2019. Generating diverse high-fidelity images with vq-vae-2. In NeurIPS."},{"key":"e_1_3_2_2_33_1","unstructured":"Danilo Jimenez Rezende Shakir Mohamed Ivo Danihelka Karol Gregor and Daan Wierstra. 2016. One-shot generalization in deep generative models. In ICML.  Danilo Jimenez Rezende Shakir Mohamed Ivo Danihelka Karol Gregor and Daan Wierstra. 2016. One-shot generalization in deep generative models. In ICML."},{"key":"e_1_3_2_2_34_1","volume-title":"Few-shot adaptation of generative adversarial networks. arXiv preprint arXiv:2010.11943","author":"Robb Esther","year":"2020","unstructured":"Esther Robb , Wen-Sheng Chu , Abhishek Kumar , and Jia-Bin Huang . 2020. Few-shot adaptation of generative adversarial networks. arXiv preprint arXiv:2010.11943 ( 2020 ). Esther Robb, Wen-Sheng Chu, Abhishek Kumar, and Jia-Bin Huang. 2020. Few-shot adaptation of generative adversarial networks. arXiv preprint arXiv:2010.11943 (2020)."},{"key":"e_1_3_2_2_35_1","volume-title":"Coco-funit: Few-shot unsupervised image translation with a content conditioned style encoder. In ECCV.","author":"Saito Kuniaki","year":"2020","unstructured":"Kuniaki Saito , Kate Saenko , and Ming-Yu Liu . 2020 . Coco-funit: Few-shot unsupervised image translation with a content conditioned style encoder. In ECCV. Kuniaki Saito, Kate Saenko, and Ming-Yu Liu. 2020. Coco-funit: Few-shot unsupervised image translation with a content conditioned style encoder. In ECCV."},{"key":"e_1_3_2_2_36_1","unstructured":"Qianru Sun Yaoyao Liu Tat-Seng Chua and Bernt Schiele. 2019. Meta-transfer learning for few-shot learning. In CVPR.  Qianru Sun Yaoyao Liu Tat-Seng Chua and Bernt Schiele. 2019. Meta-transfer learning for few-shot learning. In CVPR."},{"key":"e_1_3_2_2_37_1","volume-title":"Philip HS Torr, and Timothy M Hospedales","author":"Sung Flood","year":"2018","unstructured":"Flood Sung , Yongxin Yang , Li Zhang , Tao Xiang , Philip HS Torr, and Timothy M Hospedales . 2018 . Learning to compare: Relation network for few-shot learning. In CVPR. Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales. 2018. Learning to compare: Relation network for few-shot learning. In CVPR."},{"key":"e_1_3_2_2_38_1","unstructured":"Ilya Sutskever Oriol Vinyals and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In NeurIPS.  Ilya Sutskever Oriol Vinyals and Quoc V Le. 2014. Sequence to sequence learning with neural networks. In NeurIPS."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jon Shlens and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In CVPR.  Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jon Shlens and Zbigniew Wojna. 2016. Rethinking the inception architecture for computer vision. In CVPR.","DOI":"10.1109\/CVPR.2016.308"},{"key":"e_1_3_2_2_40_1","unstructured":"Hung-Yu Tseng Hsin-Ying Lee Jia-Bin Huang and Ming-Hsuan Yang. 2020. Cross-domain few-shot classification via Learned Feature-Wise Transformation. In ICLR.  Hung-Yu Tseng Hsin-Ying Lee Jia-Bin Huang and Ming-Hsuan Yang. 2020. Cross-domain few-shot classification via Learned Feature-Wise Transformation. In ICLR."},{"key":"e_1_3_2_2_41_1","unstructured":"Aaron van den Oord Oriol Vinyals and Koray Kavukcuoglu. 2017. Neural discrete representation learning. In NeurIPS.  Aaron van den Oord Oriol Vinyals and Koray Kavukcuoglu. 2017. Neural discrete representation learning. In NeurIPS."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"crossref","unstructured":"Grant Van Horn Steve Branson Ryan Farrell Scott Haber Jessie Barry Panos Ipeirotis Pietro Perona and Serge Belongie. 2015. Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection. In CVPR.  Grant Van Horn Steve Branson Ryan Farrell Scott Haber Jessie Barry Panos Ipeirotis Pietro Perona and Serge Belongie. 2015. Building a bird recognition app and large scale dataset with citizen scientists: The fine print in fine-grained dataset collection. In CVPR.","DOI":"10.1109\/CVPR.2015.7298658"},{"key":"e_1_3_2_2_43_1","unstructured":"Oriol Vinyals Charles Blundell Timothy Lillicrap Wierstra and Daan. 2016. Matching networks for one shot learning. In NeurIPS.  Oriol Vinyals Charles Blundell Timothy Lillicrap Wierstra and Daan. 2016. Matching networks for one shot learning. In NeurIPS."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/30.125072"},{"key":"e_1_3_2_2_45_1","volume-title":"Fahad Shahbaz Khan, and Joost van de Weijer","author":"Wang Yaxing","year":"2020","unstructured":"Yaxing Wang , Abel Gonzalez-Garcia , David Berga , Luis Herranz , Fahad Shahbaz Khan, and Joost van de Weijer . 2020 a. MineGAN: Effective Knowledge Transfer From GANs to Target Domains With Few Images. In CVPR. Yaxing Wang, Abel Gonzalez-Garcia, David Berga, Luis Herranz, Fahad Shahbaz Khan, and Joost van de Weijer. 2020a. MineGAN: Effective Knowledge Transfer From GANs to Target Domains With Few Images. In CVPR."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"crossref","unstructured":"Yaxing Wang Salman Khan Abel Gonzalez-Garcia Joost van de Weijer and Fahad Shahbaz Khan. 2020b. Semi-supervised learning for few-shot image-to-image translation. In CVPR.  Yaxing Wang Salman Khan Abel Gonzalez-Garcia Joost van de Weijer and Fahad Shahbaz Khan. 2020b. Semi-supervised learning for few-shot image-to-image translation. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00451"},{"key":"e_1_3_2_2_47_1","unstructured":"Dirk Weissenborn Oscar T\"ackstr\u00f6m and Jakob Uszkoreit. 2019. Scaling Autoregressive Video Models. In ICLR.  Dirk Weissenborn Oscar T\"ackstr\u00f6m and Jakob Uszkoreit. 2019. Scaling Autoregressive Video Models. In ICLR."},{"key":"e_1_3_2_2_48_1","unstructured":"SHI Xingjian Zhourong Chen Hao Wang Dit-Yan Yeung Wai-Kin Wong and Wang-chun Woo. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In NeurIPS.  SHI Xingjian Zhourong Chen Hao Wang Dit-Yan Yeung Wai-Kin Wong and Wang-chun Woo. 2015. Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In NeurIPS."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"crossref","unstructured":"Fuzhi Yang Huan Yang Jianlong Fu Hongtao Lu and Baining Guo. 2020b. Learning texture transformer network for image super-resolution. In CVPR.  Fuzhi Yang Huan Yang Jianlong Fu Hongtao Lu and Baining Guo. 2020b. Learning texture transformer network for image super-resolution. In CVPR.","DOI":"10.1109\/CVPR42600.2020.00583"},{"key":"e_1_3_2_2_50_1","volume-title":"DPGN: Distribution Propagation Graph Network for Few-shot Learning. In CVPR.","author":"Yang Ling","year":"2020","unstructured":"Ling Yang , Liangliang Li , Zilun Zhang , Xinyu Zhou , Erjin Zhou , and Yu Liu . 2020 a. DPGN: Distribution Propagation Graph Network for Few-shot Learning. In CVPR. Ling Yang, Liangliang Li, Zilun Zhang, Xinyu Zhou, Erjin Zhou, and Yu Liu. 2020a. DPGN: Distribution Propagation Graph Network for Few-shot Learning. In CVPR."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"crossref","unstructured":"Chi Zhang Yujun Cai Guosheng Lin and Chunhua Shen. 2020. DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover's Distance and Structured Classifiers. In CVPR.  Chi Zhang Yujun Cai Guosheng Lin and Chunhua Shen. 2020. DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover's Distance and Structured Classifiers. In CVPR.","DOI":"10.1109\/CVPR42600.2020.01222"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"crossref","unstructured":"Richard Zhang Phillip Isola Alexei A Efros Eli Shechtman and Oliver Wang. 2018. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR.  Richard Zhang Phillip Isola Alexei A Efros Eli Shechtman and Oliver Wang. 2018. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR.","DOI":"10.1109\/CVPR.2018.00068"}],"event":{"name":"MM '22: The 30th ACM International Conference on Multimedia","location":"Lisboa Portugal","acronym":"MM '22","sponsor":["SIGMM ACM Special Interest Group on Multimedia"]},"container-title":["Proceedings of the 30th ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3548158","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3503161.3548158","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:00:19Z","timestamp":1750186819000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3548158"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":52,"alternative-id":["10.1145\/3503161.3548158","10.1145\/3503161"],"URL":"https:\/\/doi.org\/10.1145\/3503161.3548158","relation":{},"subject":[],"published":{"date-parts":[[2022,10,10]]},"assertion":[{"value":"2022-10-10","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}