{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T17:49:22Z","timestamp":1790704162476,"version":"4.1.0"},"publisher-location":"New York, NY, USA","reference-count":59,"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":"National Natural Science Foundation of China Project","award":["No. 62072116"],"award-info":[{"award-number":["No. 62072116"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1145\/3503161.3547995","type":"proceedings-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T15:43:01Z","timestamp":1665416581000},"page":"6609-6617","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["ME-D2N: Multi-Expert Domain Decompositional Network for Cross-Domain Few-Shot Learning"],"prefix":"10.1145","author":[{"given":"Yuqian","family":"Fu","sequence":"first","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Xie","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanwei","family":"Fu","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Chen","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-Gang","family":"Jiang","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,10,10]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"David Bau Bolei Zhou Aditya Khosla Aude Oliva and Antonio Torralba. 2017. Network dissection: Quantifying interpretability of deep visual representations. In CVPR. 6541--6549.  David Bau Bolei Zhou Aditya Khosla Aude Oliva and Antonio Torralba. 2017. Network dissection: Quantifying interpretability of deep visual representations. In CVPR. 6541--6549.","DOI":"10.1109\/CVPR.2017.354"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2018.07.011"},{"key":"e_1_3_2_2_3_1","unstructured":"Jonathon Byrd and Zachary Lipton. 2019. What is the effect of importance weighting in deep learning?. In ICML. PMLR 872--881.  Jonathon Byrd and Zachary Lipton. 2019. What is the effect of importance weighting in deep learning?. In ICML. PMLR 872--881."},{"key":"e_1_3_2_2_4_1","volume-title":"DAMSL: Domain Agnostic Meta Score-based Learning. In CVPR. 2591--2595.","author":"Cai John","year":"2021","unstructured":"John Cai , Bill Cai , and Shen Sheng Mei . 2021 . DAMSL: Domain Agnostic Meta Score-based Learning. In CVPR. 2591--2595. John Cai, Bill Cai, and Shen Sheng Mei. 2021. DAMSL: Domain Agnostic Meta Score-based Learning. In CVPR. 2591--2595."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i1.19887"},{"key":"e_1_3_2_2_6_1","volume-title":"Ace: Ally complementary experts for solving long-tailed recognition in one-shot. In ICCV. 112--121.","author":"Cai Jiarui","year":"2021","unstructured":"Jiarui Cai , Yizhou Wang , and Jenq-Neng Hwang . 2021 . Ace: Ally complementary experts for solving long-tailed recognition in one-shot. In ICCV. 112--121. Jiarui Cai, Yizhou Wang, and Jenq-Neng Hwang. 2021. Ace: Ally complementary experts for solving long-tailed recognition in one-shot. In ICCV. 112--121."},{"key":"e_1_3_2_2_7_1","volume-title":"Learning imbalanced datasets with label-distribution-aware margin loss. NeurIPS 32","author":"Cao Kaidi","year":"2019","unstructured":"Kaidi Cao , Colin Wei , Adrien Gaidon , Nikos Arechiga , and Tengyu Ma. 2019. Learning imbalanced datasets with label-distribution-aware margin loss. NeurIPS 32 ( 2019 ). Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma. 2019. Learning imbalanced datasets with label-distribution-aware margin loss. NeurIPS 32 (2019)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3161453"},{"key":"e_1_3_2_2_9_1","volume-title":"Yu-Chiang Frank Wang, and Jia-Bin Huang","author":"Chen Wei-Yu","year":"2019","unstructured":"Wei-Yu Chen , Yen-Cheng Liu , Zsolt Kira , Yu-Chiang Frank Wang, and Jia-Bin Huang . 2019 . A closer look at few-shot classification. arXiv preprint (2019). Wei-Yu Chen, Yen-Cheng Liu, Zsolt Kira, Yu-Chiang Frank Wang, and Jia-Bin Huang. 2019. A closer look at few-shot classification. arXiv preprint (2019)."},{"key":"e_1_3_2_2_10_1","unstructured":"Rajshekhar Das Yu-Xiong Wang and Jos\u00e9 MF Moura. 2021. On the importance of distractors for few-shot classification. In ICCV. 9030--9040.  Rajshekhar Das Yu-Xiong Wang and Jos\u00e9 MF Moura. 2021. On the importance of distractors for few-shot classification. In ICCV. 9030--9040."},{"key":"e_1_3_2_2_11_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_12_1","volume-title":"Model-agnostic metalearning for fast adaptation of deep networks. arXiv preprint","author":"Finn Chelsea","year":"2017","unstructured":"Chelsea Finn , Pieter Abbeel , and Sergey Levine . 2017. Model-agnostic metalearning for fast adaptation of deep networks. arXiv preprint ( 2017 ). Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic metalearning for fast adaptation of deep networks. arXiv preprint (2017)."},{"key":"e_1_3_2_2_13_1","unstructured":"Yuqian Fu Yanwei Fu and Yu-Gang Jiang. 2021. Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data. In ACM Multimedia. 5326-- 5334.  Yuqian Fu Yanwei Fu and Yu-Gang Jiang. 2021. Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data. In ACM Multimedia. 5326-- 5334."},{"key":"e_1_3_2_2_14_1","volume-title":"Wave- SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning. arXiv preprint","author":"Fu Yuqian","year":"2022","unstructured":"Yuqian Fu , Yu Xie , Yanwei Fu , Jingjing Chen , and Yu-Gang Jiang . 2022. Wave- SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning. arXiv preprint ( 2022 ). Yuqian Fu, Yu Xie, Yanwei Fu, Jingjing Chen, and Yu-Gang Jiang. 2022. Wave- SAN: Wavelet based Style Augmentation Network for Cross-Domain Few-Shot Learning. arXiv preprint (2022)."},{"key":"e_1_3_2_2_15_1","unstructured":"Yuqian Fu Li Zhang Junke Wang Yanwei Fu and Yu-Gang Jiang. 2020. Depth guided adaptive meta-fusion network for few-shot video recognition. In ACM Multimedia. 1142--1151.  Yuqian Fu Li Zhang Junke Wang Yanwei Fu and Yu-Gang Jiang. 2020. Depth guided adaptive meta-fusion network for few-shot video recognition. In ACM Multimedia. 1142--1151."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2022.3181789"},{"key":"e_1_3_2_2_17_1","volume-title":"Few-shot learning with graph neural networks. arXiv preprint","author":"Garcia Victor","year":"2017","unstructured":"Victor Garcia and Joan Bruna . 2017. Few-shot learning with graph neural networks. arXiv preprint ( 2017 ). Victor Garcia and Joan Bruna. 2017. Few-shot learning with graph neural networks. arXiv preprint (2017)."},{"key":"e_1_3_2_2_18_1","unstructured":"Yunhui Guo Noel C Codella Leonid Karlinsky James V Codella John R Smith Kate Saenko Tajana Rosing and Rogerio Feris. 2020. A broader study of crossdomain few-shot learning. In ECCV.  Yunhui Guo Noel C Codella Leonid Karlinsky James V Codella John R Smith Kate Saenko Tajana Rosing and Rogerio Feris. 2020. A broader study of crossdomain few-shot learning. In ECCV."},{"key":"e_1_3_2_2_19_1","first-page":"1263","article-title":"Learning from imbalanced data","volume":"21","author":"He Haibo","year":"2009","unstructured":"Haibo He and Edwardo A Garcia . 2009 . Learning from imbalanced data . IEEE TKDE 21 , 9 (2009), 1263 -- 1284 . Haibo He and Edwardo A Garcia. 2009. Learning from imbalanced data. IEEE TKDE 21, 9 (2009), 1263--1284.","journal-title":"IEEE TKDE"},{"key":"e_1_3_2_2_20_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_21_1","volume-title":"Chen Change Loy, and Xiaoou Tang","author":"Huang Chen","year":"2016","unstructured":"Chen Huang , Yining Li , Chen Change Loy, and Xiaoou Tang . 2016 . Learning deep representation for imbalanced classification. In CVPR. 5375--5384. Chen Huang, Yining Li, Chen Change Loy, and Xiaoou Tang. 2016. Learning deep representation for imbalanced classification. In CVPR. 5375--5384."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2914680"},{"key":"e_1_3_2_2_23_1","volume-title":"Training cnns with low-rank filters for efficient image classification. arXiv preprint","author":"Ioannou Yani","year":"2015","unstructured":"Yani Ioannou , Duncan Robertson , Jamie Shotton , Roberto Cipolla , and Antonio Criminisi . 2015. Training cnns with low-rank filters for efficient image classification. arXiv preprint ( 2015 ). Yani Ioannou, Duncan Robertson, Jamie Shotton, Roberto Cipolla, and Antonio Criminisi. 2015. Training cnns with low-rank filters for efficient image classification. arXiv preprint (2015)."},{"key":"e_1_3_2_2_24_1","volume-title":"Rameswar Panda, Leonid Karlinsky, Rogerio Feris, and Richard Radke.","author":"Islam Ashraful","year":"2021","unstructured":"Ashraful Islam , Chun-Fu Richard Chen , Rameswar Panda, Leonid Karlinsky, Rogerio Feris, and Richard Radke. 2021 . Dynamic distillation network for crossdomain few-shot recognition with unlabeled data. NeurIPS 34 (2021). Ashraful Islam, Chun-Fu Richard Chen, Rameswar Panda, Leonid Karlinsky, Rogerio Feris, and Richard Radke. 2021. Dynamic distillation network for crossdomain few-shot recognition with unlabeled data. NeurIPS 34 (2021)."},{"key":"e_1_3_2_2_25_1","volume-title":"Categorical reparameterization with gumbel-softmax. arXiv preprint","author":"Jang Eric","year":"2016","unstructured":"Eric Jang , Shixiang Gu , and Ben Poole . 2016. Categorical reparameterization with gumbel-softmax. arXiv preprint ( 2016 ). Eric Jang, Shixiang Gu, and Ben Poole. 2016. Categorical reparameterization with gumbel-softmax. arXiv preprint (2016)."},{"key":"e_1_3_2_2_26_1","volume-title":"Decoupling representation and classifier for long-tailed recognition. arXiv preprint","author":"Kang Bingyi","year":"2019","unstructured":"Bingyi Kang , Saining Xie , Marcus Rohrbach , Zhicheng Yan , Albert Gordo , Jiashi Feng , and Yannis Kalantidis . 2019. Decoupling representation and classifier for long-tailed recognition. arXiv preprint ( 2019 ). Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis. 2019. Decoupling representation and classifier for long-tailed recognition. arXiv preprint (2019)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Jonathan Krause Michael Stark Jia Deng and Li Fei-Fei. 2013. 3d object representations for fine-grained categorization. In ICCVW.  Jonathan Krause Michael Stark Jia Deng and Li Fei-Fei. 2013. 3d object representations for fine-grained categorization. In ICCVW.","DOI":"10.1109\/ICCVW.2013.77"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"Kai Li Yulun Zhang Kunpeng Li and Yun Fu. 2020. Adversarial Feature Hallucination Networks for Few-Shot Learning. In CVPR.  Kai Li Yulun Zhang Kunpeng Li and Yun Fu. 2020. Adversarial Feature Hallucination Networks for Few-Shot Learning. In CVPR.","DOI":"10.1109\/CVPR42600.2020.01348"},{"key":"e_1_3_2_2_29_1","volume-title":"Ranking Distance Calibration for Cross-Domain Few-Shot Learning. arXiv preprint","author":"Li Pan","year":"2021","unstructured":"Pan Li , Shaogang Gong , Yanwei Fu , and Chengjie Wang . 2021. Ranking Distance Calibration for Cross-Domain Few-Shot Learning. arXiv preprint ( 2021 ). Pan Li, Shaogang Gong, Yanwei Fu, and Chengjie Wang. 2021. Ranking Distance Calibration for Cross-Domain Few-Shot Learning. arXiv preprint (2021)."},{"key":"e_1_3_2_2_30_1","volume-title":"Luc Van Gool, and Radu Timofte","author":"Li Yawei","year":"2020","unstructured":"Yawei Li , Shuhang Gu , Christoph Mayer , Luc Van Gool, and Radu Timofte . 2020 . Group sparsity: The hinge between filter pruning and decomposition for network compression. In CVPR. 8018--8027. Yawei Li, Shuhang Gu, Christoph Mayer, Luc Van Gool, and Radu Timofte. 2020. Group sparsity: The hinge between filter pruning and decomposition for network compression. In CVPR. 8018--8027."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"crossref","unstructured":"Haoyu Liang Zhihao Ouyang Yuyuan Zeng Hang Su Zihao He Shu-Tao Xia Jun Zhu and Bo Zhang. 2020. Training interpretable convolutional neural networks by differentiating class-specific filters. In ECCV. 622--638.  Haoyu Liang Zhihao Ouyang Yuyuan Zeng Hang Su Zihao He Shu-Tao Xia Jun Zhu and Bo Zhang. 2020. Training interpretable convolutional neural networks by differentiating class-specific filters. In ECCV. 622--638.","DOI":"10.1007\/978-3-030-58536-5_37"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"crossref","unstructured":"Hanwen Liang Qiong Zhang Peng Dai and Juwei Lu. 2021. Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder. In ICCV. 9424--9434.  Hanwen Liang Qiong Zhang Peng Dai and Juwei Lu. 2021. Boosting the Generalization Capability in Cross-Domain Few-shot Learning via Noise-enhanced Supervised Autoencoder. In ICCV. 9424--9434.","DOI":"10.1109\/ICCV48922.2021.00929"},{"key":"e_1_3_2_2_33_1","volume-title":"Self-training for Few-shot Transfer Across Extreme Task Differences. arXiv preprint","author":"Phoo Cheng Perng","year":"2020","unstructured":"Cheng Perng Phoo and Bharath Hariharan . 2020. Self-training for Few-shot Transfer Across Extreme Task Differences. arXiv preprint ( 2020 ). Cheng Perng Phoo and Bharath Hariharan. 2020. Self-training for Few-shot Transfer Across Extreme Task Differences. arXiv preprint (2020)."},{"key":"e_1_3_2_2_34_1","unstructured":"Qiang Qiu Xiuyuan Cheng Guillermo Sapiro etal 2018. DCFNet: Deep neural network with decomposed convolutional filters. In ICML. PMLR 4198--4207.  Qiang Qiu Xiuyuan Cheng Guillermo Sapiro et al. 2018. DCFNet: Deep neural network with decomposed convolutional filters. In ICML. PMLR 4198--4207."},{"key":"e_1_3_2_2_35_1","unstructured":"Sachin Ravi and Hugo Larochelle. 2017. Optimization as a model for few-shot learning. In ICLR.  Sachin Ravi and Hugo Larochelle. 2017. Optimization as a model for few-shot learning. In ICLR."},{"key":"e_1_3_2_2_36_1","first-page":"4499","article-title":"Edp: An efficient decomposition and pruning scheme for convolutional neural network compression","volume":"32","author":"Ruan Xiaofeng","year":"2020","unstructured":"Xiaofeng Ruan , Yufan Liu , Chunfeng Yuan , Bing Li , Weiming Hu , Yangxi Li , and Stephen Maybank . 2020 . Edp: An efficient decomposition and pruning scheme for convolutional neural network compression . IEEE TNNLS 32 , 10 (2020), 4499 -- 4513 . Xiaofeng Ruan, Yufan Liu, Chunfeng Yuan, Bing Li, Weiming Hu, Yangxi Li, and Stephen Maybank. 2020. Edp: An efficient decomposition and pruning scheme for convolutional neural network compression. IEEE TNNLS 32, 10 (2020), 4499--4513.","journal-title":"IEEE TNNLS"},{"key":"e_1_3_2_2_37_1","volume-title":"Meta-learning with latent embedding optimization. arXiv preprint","author":"Rusu Andrei A","year":"2018","unstructured":"Andrei A Rusu , Dushyant Rao , Jakub Sygnowski , Oriol Vinyals , Razvan Pascanu , Simon Osindero , and Raia Hadsell . 2018. Meta-learning with latent embedding optimization. arXiv preprint ( 2018 ). Andrei A Rusu, Dushyant Rao, Jakub Sygnowski, Oriol Vinyals, Razvan Pascanu, Simon Osindero, and Raia Hadsell. 2018. Meta-learning with latent embedding optimization. arXiv preprint (2018)."},{"key":"e_1_3_2_2_38_1","volume-title":"Interpretable Compositional Convolutional Neural Networks. arXiv preprint","author":"Shen Wen","year":"2021","unstructured":"Wen Shen , Zhihua Wei , Shikun Huang , Binbin Zhang , Jiaqi Fan , Ping Zhao , and Quanshi Zhang . 2021. Interpretable Compositional Convolutional Neural Networks. arXiv preprint ( 2021 ). Wen Shen, Zhihua Wei, Shikun Huang, Binbin Zhang, Jiaqi Fan, Ping Zhao, and Quanshi Zhang. 2021. Interpretable Compositional Convolutional Neural Networks. arXiv preprint (2021)."},{"key":"e_1_3_2_2_39_1","unstructured":"Jake Snell Kevin Swersky and Richard Zemel. 2017. Prototypical networks for few-shot learning. In NeurIPS.  Jake Snell Kevin Swersky and Richard Zemel. 2017. Prototypical networks for few-shot learning. In NeurIPS."},{"key":"e_1_3_2_2_40_1","volume-title":"Explanation-guided training for crossdomain few-shot classification. arXiv preprint","author":"Sun Jiamei","year":"2020","unstructured":"Jiamei Sun , Sebastian Lapuschkin , Wojciech Samek , Yunqing Zhao , Ngai-Man Cheung , and Alexander Binder . 2020. Explanation-guided training for crossdomain few-shot classification. arXiv preprint ( 2020 ). Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek, Yunqing Zhao, Ngai-Man Cheung, and Alexander Binder. 2020. Explanation-guided training for crossdomain few-shot classification. arXiv preprint (2020)."},{"key":"e_1_3_2_2_41_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_42_1","doi-asserted-by":"crossref","unstructured":"Hao Tang Zechao Li Zhimao Peng and Jinhui Tang. 2020. BlockMix: Meta Regularization and Self-Calibrated Inference for Metric-Based Meta-Learning. In ACM Multimedia. 610--618.  Hao Tang Zechao Li Zhimao Peng and Jinhui Tang. 2020. BlockMix: Meta Regularization and Self-Calibrated Inference for Metric-Based Meta-Learning. In ACM Multimedia. 610--618.","DOI":"10.1145\/3394171.3413884"},{"key":"e_1_3_2_2_43_1","first-page":"108792","article-title":"Learning attention-guided pyramidal features for few-shot fine-grained recognition","volume":"130","author":"Tang Hao","year":"2022","unstructured":"Hao Tang , Chengcheng Yuan , Zechao Li , and Jinhui Tang . 2022 . Learning attention-guided pyramidal features for few-shot fine-grained recognition . PR 130 (2022), 108792 . Hao Tang, Chengcheng Yuan, Zechao Li, and Jinhui Tang. 2022. Learning attention-guided pyramidal features for few-shot fine-grained recognition. PR 130 (2022), 108792.","journal-title":"PR"},{"key":"e_1_3_2_2_44_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_45_1","volume-title":"Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie.","author":"Horn Grant Van","year":"2018","unstructured":"Grant Van Horn , Oisin Mac Aodha , Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie. 2018 . The inaturalist species classification and detection dataset. In CVPR. Grant Van Horn, Oisin Mac Aodha, Yang Song, Yin Cui, Chen Sun, Alex Shepard, Hartwig Adam, Pietro Perona, and Serge Belongie. 2018. The inaturalist species classification and detection dataset. In CVPR."},{"key":"e_1_3_2_2_46_1","unstructured":"Oriol Vinyals Charles Blundell Timothy Lillicrap Daan Wierstra etal 2016. Matching networks for one shot learning. In NeurIPS.  Oriol Vinyals Charles Blundell Timothy Lillicrap Daan Wierstra et al. 2016. Matching networks for one shot learning. In NeurIPS."},{"key":"e_1_3_2_2_47_1","unstructured":"CatherineWah Steve Branson PeterWelinder Pietro Perona and Serge Belongie. 2011. The caltech-ucsd birds-200--2011 dataset. (2011).  CatherineWah Steve Branson PeterWelinder Pietro Perona and Serge Belongie. 2011. The caltech-ucsd birds-200--2011 dataset. (2011)."},{"key":"e_1_3_2_2_48_1","volume-title":"Cross-domain few-shot classification via adversarial task augmentation. arXiv preprint","author":"Wang Haoqing","year":"2021","unstructured":"Haoqing Wang and Zhi-Hong Deng . 2021. Cross-domain few-shot classification via adversarial task augmentation. arXiv preprint ( 2021 ). Haoqing Wang and Zhi-Hong Deng. 2021. Cross-domain few-shot classification via adversarial task augmentation. arXiv preprint (2021)."},{"key":"e_1_3_2_2_49_1","volume-title":"Longtailed recognition by routing diverse distribution-aware experts. arXiv preprint","author":"Wang Xudong","year":"2020","unstructured":"Xudong Wang , Long Lian , Zhongqi Miao , Ziwei Liu , and Stella X Yu. 2020. Longtailed recognition by routing diverse distribution-aware experts. arXiv preprint ( 2020 ). Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, and Stella X Yu. 2020. Longtailed recognition by routing diverse distribution-aware experts. arXiv preprint (2020)."},{"key":"e_1_3_2_2_50_1","volume-title":"Learning to model the tail. NeurIPS 30","author":"Wang Yu-Xiong","year":"2017","unstructured":"Yu-Xiong Wang , Deva Ramanan , and Martial Hebert . 2017. Learning to model the tail. NeurIPS 30 ( 2017 ). Yu-Xiong Wang, Deva Ramanan, and Martial Hebert. 2017. Learning to model the tail. NeurIPS 30 (2017)."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"crossref","unstructured":"Liuyu Xiang Guiguang Ding and Jungong Han. 2020. Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification. In ECCV. 247--263.  Liuyu Xiang Guiguang Ding and Jungong Han. 2020. Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification. In ECCV. 247--263.","DOI":"10.1007\/978-3-030-58558-7_15"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"crossref","unstructured":"Yu Xie Yanwei Fu Ying Tai Yun Cao Junwei Zhu and Chengjie Wang. 2022. Learning To Memorize Feature Hallucination for One-Shot Image Generation. In CVPR. 9130--9139.  Yu Xie Yanwei Fu Ying Tai Yun Cao Junwei Zhu and Chengjie Wang. 2022. Learning To Memorize Feature Hallucination for One-Shot Image Generation. In CVPR. 9130--9139.","DOI":"10.1109\/CVPR52688.2022.00892"},{"key":"e_1_3_2_2_53_1","volume-title":"Progressive Meta-learning with Curriculum. TCSVT","author":"Zhang Ji","year":"2022","unstructured":"Ji Zhang , Jingkuan Song , Lianli Gao , Ye Liu , and Heng Tao Shen . 2022. Progressive Meta-learning with Curriculum. TCSVT ( 2022 ). Ji Zhang, Jingkuan Song, Lianli Gao, Ye Liu, and Heng Tao Shen. 2022. Progressive Meta-learning with Curriculum. TCSVT (2022)."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"crossref","unstructured":"Ji Zhang Jingkuan Song Yazhou Yao and Lianli Gao. 2021. Curriculum-based meta-learning. In ACM Multimedia. 1838--1846.  Ji Zhang Jingkuan Song Yazhou Yao and Lianli Gao. 2021. Curriculum-based meta-learning. In ACM Multimedia. 1838--1846.","DOI":"10.1145\/3474085.3475335"},{"key":"e_1_3_2_2_55_1","volume-title":"Ying Nian Wu, and Song-Chun Zhu","author":"Zhang Quanshi","year":"2018","unstructured":"Quanshi Zhang , Ying Nian Wu, and Song-Chun Zhu . 2018 . Interpretable convolutional neural networks. In CVPR. 8827--8836. Quanshi Zhang, Ying Nian Wu, and Song-Chun Zhu. 2018. Interpretable convolutional neural networks. In CVPR. 8827--8836."},{"key":"e_1_3_2_2_56_1","volume-title":"Domain-aware dynamic networks. arXiv preprint","author":"Zhang Tianyuan","year":"2019","unstructured":"Tianyuan Zhang , Bichen Wu , Xin Wang , Joseph Gonzalez , and Kurt Keutzer . 2019. Domain-aware dynamic networks. arXiv preprint ( 2019 ). Tianyuan Zhang, Bichen Wu, Xin Wang, Joseph Gonzalez, and Kurt Keutzer. 2019. Domain-aware dynamic networks. arXiv preprint (2019)."},{"key":"e_1_3_2_2_57_1","volume-title":"Bbn: Bilateralbranch network with cumulative learning for long-tailed visual recognition. In CVPR. 9719--9728.","author":"Zhou Boyan","year":"2020","unstructured":"Boyan Zhou , Quan Cui , Xiu-Shen Wei , and Zhao-Min Chen . 2020 . Bbn: Bilateralbranch network with cumulative learning for long-tailed visual recognition. In CVPR. 9719--9728. Boyan Zhou, Quan Cui, Xiu-Shen Wei, and Zhao-Min Chen. 2020. Bbn: Bilateralbranch network with cumulative learning for long-tailed visual recognition. In CVPR. 9719--9728."},{"key":"e_1_3_2_2_58_1","volume-title":"Places: A 10 million image database for scene recognition. TPAMI","author":"Zhou Bolei","year":"2017","unstructured":"Bolei Zhou , Agata Lapedriza , Aditya Khosla , Aude Oliva , and Antonio Torralba . 2017 . Places: A 10 million image database for scene recognition. TPAMI (2017). Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba. 2017. Places: A 10 million image database for scene recognition. TPAMI (2017)."},{"key":"e_1_3_2_2_59_1","volume-title":"Yi-Ping Phoebe Chen, and Yu-Gang Jiang","author":"Zhu Jianggang","year":"2022","unstructured":"Jianggang Zhu , Zheng Wang , Jingjing Chen , Yi-Ping Phoebe Chen, and Yu-Gang Jiang . 2022 . Balanced Contrastive Learning for Long-Tailed Visual Recognition. In CVPR. 6908--6917. Jianggang Zhu, Zheng Wang, Jingjing Chen, Yi-Ping Phoebe Chen, and Yu-Gang Jiang. 2022. Balanced Contrastive Learning for Long-Tailed Visual Recognition. In CVPR. 6908--6917."}],"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\/3503161.3547995","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3503161.3547995","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:02:29Z","timestamp":1750186949000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3503161.3547995"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,10]]},"references-count":59,"alternative-id":["10.1145\/3503161.3547995","10.1145\/3503161"],"URL":"https:\/\/doi.org\/10.1145\/3503161.3547995","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"}}]}}