{"type":"ScholarlyArticle","title":"Contrastive Transformer Masked Image Hashing for Degraded Image Retrieval","authors":[{"name":"Xiaobo Shen","slug":"xiaobo-shen","given":"Xiaobo","family":"Shen","affiliation":"Nanjing University of Science and Technology"},{"name":"Haoyu Cai","slug":"haoyu-cai","given":"Haoyu","family":"Cai","affiliation":"Nanjing University of Science and Technology"},{"name":"Xiuwen Gong","slug":"xiuwen-gong","given":"Xiuwen","family":"Gong","affiliation":"University of Technology Sydney"},{"name":"Yuhui Zheng","slug":"yuhui-zheng","given":"Yuhui","family":"Zheng","affiliation":"Qinghai Normal University"}],"abstract":"Hashing utilizes hash code as a compact image representation, offering excellent performance in large-scale image retrieval due to its computational and storage advantages. However, the prevalence of degraded images on social media platforms, resulting from imperfections in the image capture process, poses new challenges for conventional image retrieval methods. To address this issue, we propose Contrastive Transformer Masked Image Hashing (CTMIH), a novel deep unsupervised hashing method specifically designed for degraded image retrieval, which is challenging yet relatively less studied. CTMIH addresses the problem by training on transformed and masked images, aiming to learn transform-invariant hash code in an unsupervised manner to mitigate performance degradation caused by image deterioration. CTMIH utilizes Vision Transformer (ViT) architecture applied to image patches to capture distant semantic relevance. CTMIH introduces cross-view debiased contrastive loss to align hash tokens from augmented views of the same image and presents semantic mask reconstruction loss at the patch level to recover masked patch tokens. Extensive empirical studies conducted on three benchmark datasets demonstrate the superiority of the proposed CTMIH over the state-of-the-art in both degraded and normal image retrieval.","doi":"10.24963/ijcai.2024/135","url":"https://ijcai.org/proceedings/2024/135","pdf":"https://ijcai.org/proceedings/2024/0135.pdf","markdown":"https://ijcai.org/proceedings/2024/135.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/135","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":135,"pages":{"first":1218,"last":1226},"keywords":["Computer Vision: CV: Image and video retrieval","Machine Learning: ML: Unsupervised learning"],"videos":[],"references":[{"position":1,"raw":"[Cao et al., 2017] Zhangjie Cao, Mingsheng Long, Jianmin Wang, and Philip S Yu. Hashnet: Deep learning to hash by continuation. In Proceedings of IEEE International Con- ference on Computer Vision, pages 5608-5617, 2017.","title":"Hashnet: Deep learning to hash by continuation","authors":"Cao","year":"2017","venue":"Proceedings of IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Chen et al., 2020] Ting Chen, Simon Kornblith, Moham- mad Norouzi, and Geoffrey E. Hinton. A simple frame- work for contrastive learning of visual representations. In Proceedings of International Conference on Machine Learning, volume 119, pages 1597-1607, 2020.","title":"A simple framework for contrastive learning of visual representations","authors":"Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey E Hinton","year":"2020","venue":"Proceedings of International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chua et al., 2009] Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng. Nus- wide: a real-world web image database from national uni- versity of singapore. In Proceedings of ACM Interna- tional Conference on Image and Video Retrieval, pages 1-9, 2009.","title":"Nuswide: a real-world web image database from national university of singapore","authors":"Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, Yantao Zheng","year":"2009","venue":"Proceedings of ACM International Conference on Image and Video Retrieval","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Dai et al., 2017] Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, and Le Song. Stochastic generative hashing. In Pro- ceedings of International Conference on Machine Learn- ing, pages 913-922, 2017.","title":"Stochastic generative hashing","authors":"Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song","year":"2017","venue":"Proceedings of International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Dosovitskiy et al., 2021] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Min- derer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Trans- formers for image recognition at scale. In Proceedings of International Conference on Learning Representations, 2021.","title":"An image is worth 16x16 words: Transformers for image recognition at scale","authors":"Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby","year":"2021","venue":"Proceedings of International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Fan et al., 2020] Lixin Fan, Kam Woh Ng, Ce Ju, Tianyu Zhang, and Chee Seng Chan. Deep polarized network for supervised learning of accurate binary hashing codes. In Proceedings of International Joint Conference on Artifi- cial Intelligence, pages 825-831, 2020.","title":"Deep polarized network for supervised learning of accurate binary hashing codes","authors":"Lixin Fan, Kam Woh Ng, Ce Ju, Tianyu Zhang, Chee Seng, Chan","year":"2020","venue":"Proceedings of International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2020/115"},{"position":7,"raw":"[Gong et al., 2012] Yunchao Gong, Svetlana Lazebnik, Al- bert Gordo, and Florent Perronnin. Iterative quantization: A procrustean approach to learning binary codes for large- scale image retrieval. IEEE Transactions on Pattern Anal- ysis and Machine Intelligence, 35(12):2916-2929, 2012.","title":"Iterative quantization: A procrustean approach to learning binary codes for largescale image retrieval","authors":"Gong","year":"2012","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Goodfellow et al., 2014] Ian J. Goodfellow, Jean Pouget- Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63:139 -144, 2014.","title":"Generative adversarial networks","authors":"Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C Courville, Yoshua Bengio","year":"2014","venue":"Communications of the ACM","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[He et al., 2020] Kaiming He, Haoqi Fan, Yuxin Wu, Sain- ing Xie, and Ross B. Girshick. Momentum contrast for unsupervised visual representation learning. In Proceed- ings of IEEE Conference on Computer Vision and Pattern Recognition, pages 9726-9735, 2020.","title":"Momentum contrast for unsupervised visual representation learning","authors":"Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, Ross B Girshick","year":"2020","venue":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Kingma and Welling, 2013] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. In Proceedings of International Conference on Learning Representations, 2013.","title":"Auto-encoding variational bayes","authors":"P Diederik, Max Kingma, Welling","year":"2013","venue":"Proceedings of International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Krizhevsky et al., 2012] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In Proceedings of Ad- vances in Neural Information Processing Systems, pages 1106-1114, 2012.","title":"Imagenet classification with deep convolutional neural networks","authors":"Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton","year":"2012","venue":"Proceedings of Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[LeCun et al., 2015] Yann LeCun, Yoshua Bengio, and Ge- offrey Hinton. Deep learning. Nature, 521(7553):436- 444, 2015.","title":"Deep learning","authors":"Yann Lecun, Yoshua Bengio, Geoffrey Hinton","year":"2015","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Li and van Gemert, 2021] Yunqiang Li and Jan van Gemert. Deep unsupervised image hashing by maximizing bit en- tropy. In Proceedings of AAAI Conference on Artificial Intelligence, pages 2002-2010, 2021.","title":"Yunqiang Li and Jan van Gemert. Deep unsupervised image hashing by maximizing bit entropy","authors":"Van Li, Gemert","year":"2021","venue":"Proceedings of AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Li et al., 2017] Qi Li, Zhenan Sun, Ran He, and Tieniu Tan. Deep supervised discrete hashing. In Proceedings of Ad- vances in Neural Information Processing Systems, pages 2482-2491, 2017.","title":"Deep supervised discrete hashing","authors":"Li","year":"2017","venue":"Proceedings of Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Li et al., 2019] Dangwei Li, Zhang Zhang, Xiaotang Chen, and Kaiqi Huang. A richly annotated pedestrian dataset for person retrieval in real surveillance scenarios. IEEE Trans- actions on Image Processing, 28(4):1575-1590, 2019.","title":"A richly annotated pedestrian dataset for person retrieval in real surveillance scenarios","authors":"Dangwei Li, Zhang Zhang, Xiaotang Chen, Kaiqi Huang","year":"2019","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Lin et al., 2014] Tsung-Yi Lin, Michael Maire, Serge Be- longie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Com- mon objects in context. In Proceedings of European Con- ference on Computer Vision, pages 740-755, 2014.","title":"Microsoft coco: Common objects in context","authors":"Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, C Lawrence, Zitnick","year":"2014","venue":"Proceedings of European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Liu et al., 2019] Haomiao Liu, Ruiping Wang, Shiguang Shan, and Xilin Chen. Deep supervised hashing for fast image retrieval. Proceedings of IEEE Conference on Com- puter Vision and Pattern Recognition, 127(9):1217-1234, 2019.","title":"Deep supervised hashing for fast image retrieval","authors":"Liu","year":"2019","venue":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Luo et al., 2021] Xiao Luo, Daqing Wu, Zeyu Ma, Chong Chen, Minghua Deng, Jinwen Ma, Zhongming Jin, Jian- qiang Huang, and Xian-Sheng Hua. Cimon: Towards high-quality hash codes. In Proceedings of International Joint Conference on Artificial Intelligence, pages 902- 908, 2021.","title":"Cimon: Towards high-quality hash codes","authors":"Xiao Luo, Daqing Wu, Zeyu Ma, Chong Chen, Minghua Deng, Jinwen Ma, Zhongming Jin, Jianqiang Huang, Xian-Sheng Hua","year":"2021","venue":"Proceedings of International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2021/125"},{"position":19,"raw":"[Luo et al., 2023] Xiao Luo, Haixin Wang, Daqing Wu, Chong Chen, Minghua Deng, Jianqiang Huang, and Xian-Sheng Hua. A survey on deep hashing methods. ACM Transactions on Knowledge Discovery from Data, 17(1):15:1-15:50, 2023.","title":"A survey on deep hashing methods","authors":"Xiao Luo, Haixin Wang, Daqing Wu, Chong Chen, Minghua Deng, Jianqiang Huang, Xian-Sheng Hua","year":"2023","venue":"ACM Transactions on Knowledge Discovery from Data","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Ma et al., 2022] Zeyu Ma, Xiao Luo, Yingjie Chen, Mix- iao Hou, Jinxing Li, Minghua Deng, and Guangming Lu. Improved deep unsupervised hashing with fine-grained se- mantic similarity mining for multi-label image retrieval. In Proceedings of International Joint Conference on Arti- ficial Intelligence, pages 1254-1260, 2022.","title":"Improved deep unsupervised hashing with fine-grained semantic similarity mining for multi-label image retrieval","authors":"Zeyu Ma, Xiao Luo, Yingjie Chen, Mixiao Hou, Jinxing Li, Minghua Deng, Guangming Lu","year":"2022","venue":"Proceedings of International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2022/175"},{"position":21,"raw":"[Ng et al., 2023] Kam Woh Ng, Xiatian Zhu, Jiun Tian Hoe, Chee Seng Chan, Tianyu Zhang, Yi-Zhe Song, Tao Xi- ang, Universiti Malaya CISiP, and GACRD Center. Un- supervised hashing with similarity distribution calibration. 2023.","title":"Unsupervised hashing with similarity distribution calibration","authors":"Kam Woh Ng, Xiatian Zhu, Jiun Tian Hoe, Chee Seng Chan, Tianyu Zhang, Yi-Zhe Song, Tao Xiang, Universiti Malaya Cisip, Center","year":"2023","venue":null,"doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Park et al., 2023] Dongwon Park, Byung Hyun Lee, and Se Young Chun. All-in-one image restoration for unknown degradations using adaptive discriminative filters for spe- cific degradations. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 5815- 5824, 2023.","title":"All-in-one image restoration for unknown degradations using adaptive discriminative filters for specific degradations","authors":"Dongwon Park, Byung, Hyun Lee, Se Young, Chun","year":"2023","venue":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Qiu et al., 2021] Zexuan Qiu, Qinliang Su, Zijing Ou, Jianxing Yu, and Changyou Chen. Unsupervised hashing with contrastive information bottleneck. In Proceedings of International Joint Conference on Artificial Intelligence, pages 959-965, 2021.","title":"Unsupervised hashing with contrastive information bottleneck","authors":"Qiu","year":"2021","venue":"Proceedings of International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2021/133"},{"position":24,"raw":"[Russakovsky et al., 2014] Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander Berg, and Li Fei-Fei. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115, 09 2014.","title":"Imagenet large scale visual recognition challenge","authors":"Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander Berg, Li Fei-Fei","year":"2014","venue":"International Journal of Computer Vision","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Shen et al., 2015] Fumin Shen, Chunhua Shen, Wei Liu, and Heng Tao Shen. Supervised discrete hashing. In Pro- ceedings of IEEE Conference on Computer Vision and Pat- tern Recognition, pages 37-45, 2015.","title":"Supervised discrete hashing","authors":"Fumin Shen, Chunhua Shen, Wei Liu, Heng Tao Shen","year":"2015","venue":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Shen et al., 2020] Yuming Shen, Jie Qin, Jiaxin Chen, Mengyang Yu, Li Liu, Fan Zhu, Fumin Shen, and Ling Shao. Auto-encoding twin-bottleneck hashing. In Pro- ceedings of IEEE Conference on Computer Vision and Pat- tern Recognition, pages 2818-2827, 2020.","title":"Auto-encoding twin-bottleneck hashing","authors":"Yuming Shen, Jie Qin, Jiaxin Chen, Mengyang Yu, Li Liu, Fan Zhu, Fumin Shen, Ling Shao","year":"2020","venue":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Su et al., 2018] Shupeng Su, Chao Zhang, Kai Han, and Yonghong Tian. Greedy hash: Towards fast optimization for accurate hash coding in cnn. Proceedings of Advances in Neural Information Processing Systems, 31, 2018.","title":"Greedy hash: Towards fast optimization for accurate hash coding in cnn","authors":"Su","year":"2018","venue":"Proceedings of Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[van der Maaten and Hinton, 2008] Laurens van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. The Journal of Machine Learning Research, 9(86):2579-2605, 2008.","title":"Visualizing data using t-sne","authors":"Van Der Maaten, Laurens Hinton, Geoffrey Van Der Maaten, Hinton","year":"2008","venue":"The Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Wang et al., 2017] Jingdong Wang, Ting Zhang, Nicu Sebe, Heng Tao Shen, et al. A survey on learning to hash. IEEE Transactions on Pattern Analysis and Machine In- telligence, 40(4):769-790, 2017.","title":"A survey on learning to hash","authors":"Jingdong Wang, Ting Zhang, Nicu Sebe, Heng Tao Shen","year":"2017","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Wang et al., 2020] Yang Wang, Yang Cao, Zheng-Jun Zha, Jing Zhang, and Zhiwei Xiong. Deep degradation prior for low-quality image classification. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 11049-11058, 2020.","title":"Deep degradation prior for low-quality image classification","authors":"Yang Wang, Yang Cao, Zheng-Jun Zha, Jing Zhang, Zhiwei Xiong","year":"2020","venue":"Proceedings of IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Wang et al., 2022] Jinpeng Wang, Ziyun Zeng, Bin Chen, Tao Dai, and Shu-Tao Xia. Contrastive quantization with code memory for unsupervised image retrieval. In Pro- ceedings of AAAI Conference on Artificial Intelligence, pages 2468-2476, 2022.","title":"Contrastive quantization with code memory for unsupervised image retrieval","authors":"Jinpeng Wang, Ziyun Zeng, Bin Chen, Tao Dai, Shu-Tao Xia","year":"2022","venue":"Proceedings of AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Weiss et al., 2008] Yair Weiss, Antonio Torralba, and Rob Fergus. Spectral hashing. Proceedings of Advances in Neural Information Processing Systems, 21, 2008.","title":"Spectral hashing","authors":"Yair Weiss, Antonio Torralba, Rob Fergus","year":"2008","venue":"Proceedings of Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Yang et al., 2018] Erkun Yang, Cheng Deng, Tongliang Liu, Wei Liu, and Dacheng Tao. Semantic structure-based unsupervised deep hashing. In Proceedings of Interna- tional Joint Conference on Artificial Intelligence, pages 1064-1070, 2018.","title":"Semantic structure-based unsupervised deep hashing","authors":"Erkun Yang, Cheng Deng, Tongliang Liu, Wei Liu, Dacheng Tao","year":"2018","venue":"Proceedings of International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2018/148"},{"position":34,"raw":"[Yang et al., 2022] Zhou Yang, Weisheng Dong, Xin Li, Jin- jian Wu, Leida Li, and Guangming Shi. Self-feature dis- tillation with uncertainty modeling for degraded image recognition. In Proceedings of European Conference on Computer Vision, pages 552-569, 2022.","title":"Self-feature distillation with uncertainty modeling for degraded image recognition","authors":"Zhou Yang, Weisheng Dong, Xin Li, Jinjian Wu, Leida Li, Guangming Shi","year":"2022","venue":"Proceedings of European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Yu et al., 2022] Jiaguo Yu, Huming Qiu, Dubing Chen, and Haofeng Zhang. Weighted contrative hashing. In Proceed- ings of the Asian Conference on Computer Vision, pages 3861-3876, 2022.","title":"Weighted contrative hashing","authors":"Yu","year":"2022","venue":"Proceedings of the Asian Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Yu et al., 2023] Jiaguo Yu, Yuming Shen, and Haofeng Zhang. Hashing one with all. In Proceedings of ACM In- ternational Conference on Multimedia, pages 6420-6431, 2023.","title":"Hashing one with all","authors":"Yu","year":"2023","venue":"Proceedings of ACM International Conference on Multimedia","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Zhang et al., 2017] Haofeng Zhang, Li Liu, Yang Long, and Ling Shao. Unsupervised deep hashing with pseudo labels for scalable image retrieval. IEEE Transactions on Image Processing, 27(4):1626-1638, 2017.","title":"Unsupervised deep hashing with pseudo labels for scalable image retrieval","authors":"Zhang","year":"2017","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Zhou et al., 2021] Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, and Tao Kong. ibot: Image bert pre-training with online tokenizer. Proceedings of International Conference on Learning Representations, 2021.","title":"ibot: Image bert pre-training with online tokenizer","authors":"Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, Tao Kong","year":"2021","venue":"Proceedings of International Conference on Learning Representations","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}