{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:05:09Z","timestamp":1750309509646,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T00:00:00Z","timestamp":1730073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,28]]},"DOI":"10.1145\/3664647.3680668","type":"proceedings-article","created":{"date-parts":[[2024,10,26]],"date-time":"2024-10-26T06:59:27Z","timestamp":1729925967000},"page":"738-747","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Visual-Language Collaborative Representation Network for Broad-Domain Few-Shot Image Classification"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2497-2639","authenticated-orcid":false,"given":"Qianyu","family":"Guo","sequence":"first","affiliation":[{"name":"Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6381-6830","authenticated-orcid":false,"given":"Jieji","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3018-3824","authenticated-orcid":false,"given":"Haofen","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Design and Innovation, Tongji University, Shanghai, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4669-3570","authenticated-orcid":false,"given":"Tianxing","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Southeast University, Shanghai, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6258-6225","authenticated-orcid":false,"given":"Weifeng","family":"Ge","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3339-8751","authenticated-orcid":false,"given":"Wenqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Engineering Research Center of AI &amp; Robotics, Ministry of Education, Academy for Engineering &amp; Technology, Fudan University, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2024,10,28]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00668"},{"key":"e_1_3_2_1_2_1","volume-title":"ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection. CoRR abs\/1703","author":"Berseth Matt","year":"2017","unstructured":"Matt Berseth. 2017. ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection. CoRR abs\/1703.00523 (2017)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2023.102959"},{"key":"e_1_3_2_1_4_1","volume-title":"Pareto Self-Supervised Training for Few-Shot Learning. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021","author":"Chen Zhengyu","year":"2021","unstructured":"Zhengyu Chen, Jixie Ge, Heshen Zhan, Siteng Huang, and Donglin Wang. 2021. Pareto Self-Supervised Training for Few-Shot Learning. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021. Computer Vision Foundation \/ IEEE, 13663--13672."},{"key":"e_1_3_2_1_5_1","volume-title":"Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC). CoRR abs\/1902.03368","author":"Codella Noel C. F.","year":"2019","unstructured":"Noel C. F. Codella, Veronica Rotemberg, Philipp Tschandl, M. Emre Celebi, Stephen W. Dusza, David A. Gutman, Brian Helba, Aadi Kalloo, Konstantinos Liopyris, Michael A. Marchetti, Harald Kittler, and Allan Halpern. 2019. Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC). CoRR abs\/1902.03368 (2019)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"e_1_3_2_1_7_1","volume-title":"MetaFormer: A Unified Meta Framework for Fine-Grained Recognition. CoRR abs\/2203.02751","author":"Diao Qishuai","year":"2022","unstructured":"Qishuai Diao, Yi Jiang, Bin Wen, Jia Sun, and Zehuan Yuan. 2022. MetaFormer: A Unified Meta Framework for Fine-Grained Recognition. CoRR abs\/2203.02751 (2022)."},{"key":"e_1_3_2_1_8_1","volume-title":"Camouflaged Object Detection. In 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020","author":"Fan Deng-Ping","year":"2020","unstructured":"Deng-Ping Fan, Ge-Peng Ji, Guolei Sun, Ming-Ming Cheng, Jianbing Shen, and Ling Shao. 2020. Camouflaged Object Detection. In 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020. Computer Vision Foundation \/ IEEE, 2774--2784."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2005.09.012"},{"key":"e_1_3_2_1_10_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning, ICML 2017","author":"Finn Chelsea","year":"2017","unstructured":"Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 (Proceedings of Machine Learning Research, Vol. 70), Doina Precup and Yee Whye Teh (Eds.). PMLR, 1126--1135."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2023.3306929"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-023-01891-x"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i3.27963"},{"key":"e_1_3_2_1_14_1","volume-title":"Plug-and-Play Feature Generation for Few-Shot Medical Image Classification. In IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023","author":"Guo Qianyu","year":"2023","unstructured":"Qianyu Guo, Huifang Du, Xing Jia, Shuyong Gao, Yan Teng, Haofen Wang, and Wenqiang Zhang. 2023. Plug-and-Play Feature Generation for Few-Shot Medical Image Classification. In IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Istanbul, Turkiye, December 5-8, 2023, Xingpeng Jiang, Haiying Wang, Reda Alhajj, Xiaohua Hu, Felix Engel, Mufti Mahmud, Nadia Pisanti, Xuefeng Cui, and Hong Song (Eds.). IEEE, 1096--1103."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i1.25150"},{"key":"e_1_3_2_1_16_1","volume-title":"UK","volume":"141","author":"Guo Yunhui","year":"2020","unstructured":"Yunhui Guo, Noel Codella, Leonid Karlinsky, James V. Codella, John R. Smith, Kate Saenko, Tajana Rosing, and Rog\u00e9rio Feris. 2020. A Broader Study of Cross-Domain Few-Shot Learning. In Computer Vision - ECCV 2020 - 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXVII (Lecture Notes in Computer Science, Vol. 12372), Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm (Eds.). Springer, 124--141."},{"key":"e_1_3_2_1_17_1","volume-title":"Class-Aware Patch Embedding Adaptation for Few-Shot Image Classification. In IEEE\/CVF International Conference on Computer Vision, ICCV 2023","author":"Hao Fusheng","year":"2023","unstructured":"Fusheng Hao, Fengxiang He, Liu Liu, Fuxiang Wu, Dacheng Tao, and Jun Cheng. 2023. Class-Aware Patch Embedding Adaptation for Few-Shot Image Classification. In IEEE\/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1-6, 2023. IEEE, 18859--18869."},{"key":"e_1_3_2_1_18_1","volume-title":"Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022","author":"He Yangji","year":"2022","unstructured":"Yangji He, Weihan Liang, Dongyang Zhao, Hong-Yu Zhou, Weifeng Ge, Yizhou Yu, and Wenqiang Zhang. 2022. Attribute Surrogates Learning and Spectral Tokens Pooling in Transformers for Few-shot Learning. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022. IEEE, 9109--9119."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2023.3274223"},{"key":"e_1_3_2_1_20_1","volume-title":"Hughes and Marcel Salath\u00e9","author":"David","year":"2015","unstructured":"David P. Hughes and Marcel Salath\u00e9. 2015. An open access repository of images on plant health to enable the development of mobile disease diagnostics through machine learning and crowdsourcing. CoRR abs\/1511.08060 (2015)."},{"key":"e_1_3_2_1_21_1","volume-title":"Contrastive Meta-Learning for Partially Observable Few-Shot Learning. In The Eleventh International Conference on Learning Representations, ICLR 2023","author":"Jelley Adam","year":"2023","unstructured":"Adam Jelley, Amos J. Storkey, Antreas Antoniou, and Sam Devlin. 2023. Contrastive Meta-Learning for Partially Observable Few-Shot Learning. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture12030346"},{"key":"e_1_3_2_1_23_1","volume-title":"Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems","author":"Ke Tianjun","year":"2023","unstructured":"Tianjun Ke, Haoqun Cao, Zenan Ling, and Feng Zhou. 2023. Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023, Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, and Sergey Levine (Eds.)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01441"},{"key":"e_1_3_2_1_25_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.)."},{"key":"e_1_3_2_1_26_1","volume-title":"Tel Aviv","volume":"19","author":"Lai Jinxiang","year":"2022","unstructured":"Jinxiang Lai, Siqian Yang, Wenlong Liu, Yi Zeng, Zhongyi Huang, Wenlong Wu, Jun Liu, Bin-Bin Gao, and Chengjie Wang. 2022. tSF: Transformer-Based Semantic Filter for Few-Shot Learning. In Computer Vision - ECCV 2022 - 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XX (Lecture Notes in Computer Science, Vol. 13680), Shai Avidan, Gabriel J. Brostow, Moustapha Ciss\u00e9, Giovanni Maria Farinella, and Tal Hassner (Eds.). Springer, 1--19."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_1_28_1","volume-title":"Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning. In 9th International Conference on Learning Representations, ICLR 2021","author":"Markowitz Elan Sopher","year":"2021","unstructured":"Elan Sopher Markowitz, Keshav Balasubramanian, Mehrnoosh Mirtaheri, Sami Abu-El-Haija, Bryan Perozzi, Greg Ver Steeg, and Aram Galstyan. 2021. Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable Learning. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101911"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3311646"},{"key":"e_1_3_2_1_31_1","volume-title":"MEDICAL IMAGE UNDERSTANDING WITH PRETRAINED VISION LANGUAGE MODELS: A COMPREHENSIVE STUDY. In The Eleventh International Conference on Learning Representations, ICLR 2023","author":"Qin Ziyuan","year":"2023","unstructured":"Ziyuan Qin, Huahui Yi, Qicheng Lao, and Kang Li. 2023. MEDICAL IMAGE UNDERSTANDING WITH PRETRAINED VISION LANGUAGE MODELS: A COMPREHENSIVE STUDY. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023. OpenReview.net."},{"key":"e_1_3_2_1_32_1","volume-title":"Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18--24","volume":"8763","author":"Radford Alec","year":"2021","unstructured":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021. Learning Transferable Visual Models From Natural Language Supervision. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18--24 July 2021, Virtual Event (Proceedings of Machine Learning Research, Vol. 139), Marina Meila and Tong Zhang (Eds.). PMLR, 8748-8763."},{"key":"e_1_3_2_1_33_1","first-page":"10836","volume-title":"Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021","author":"Rizve Mamshad Nayeem","year":"2021","unstructured":"Mamshad Nayeem Rizve, Salman H. Khan, Fahad Shahbaz Khan, and Mubarak Shah. 2021. Exploring Complementary Strengths of Invariant and Equivariant Representations for Few-Shot Learning. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021. Computer Vision Foundation \/ IEEE, 10836-10846."},{"key":"e_1_3_2_1_34_1","volume-title":"Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022","author":"Roy Aniket","year":"2022","unstructured":"Aniket Roy, Anshul Shah, Ketul Shah, Prithviraj Dhar, Anoop Cherian, and Rama Chellappa. 2022. FeLMi: Few shot Learning with hard Mixup. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 - December 9, 2022, Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, and A. Oh (Eds.)."},{"key":"e_1_3_2_1_35_1","volume-title":"Prototypical Networks for Few-shot Learning. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017","author":"Snell Jake","year":"2017","unstructured":"Jake Snell, Kevin Swersky, and Richard S. Zemel. 2017. Prototypical Networks for Few-shot Learning. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett (Eds.)."},{"key":"e_1_3_2_1_36_1","volume-title":"Applications, Challenges, and Opportunities. ACM Comput. Surv. 55, 13s","author":"Song Yisheng","year":"2023","unstructured":"Yisheng Song, Ting Wang, Puyu Cai, Subrota K. Mondal, and Jyoti Prakash Sahoo. 2023. A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities. ACM Comput. Surv. 55, 13s (2023), 271:1?271:40."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00131"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2023.10.039"},{"key":"e_1_3_2_1_39_1","volume-title":"Oracle-MNIST: a Realistic Image Dataset for Benchmarking Machine Learning Algorithms. CoRR abs\/2205.09442","author":"Wang Mei","year":"2022","unstructured":"Mei Wang and Weihong Deng. 2022. Oracle-MNIST: a Realistic Image Dataset for Benchmarking Machine Learning Algorithms. CoRR abs\/2205.09442 (2022)."},{"key":"e_1_3_2_1_40_1","volume-title":"Ni","author":"Wang Yaqing","year":"2021","unstructured":"Yaqing Wang, Quanming Yao, James T. Kwok, and Lionel M. Ni. 2021. Generalizing from a Few Examples: A Survey on Few-shot Learning. ACM Comput. Surv. 53, 3 (2021), 63:1-63:34."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00899"},{"key":"e_1_3_2_1_42_1","volume-title":"IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022","author":"Xie Jiangtao","year":"2022","unstructured":"Jiangtao Xie, Fei Long, Jiaming Lv, Qilong Wang, and Peihua Li. 2022. Joint Distri- bution Matters: Deep Brownian Distance Covariance for Few-Shot Classification. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022. IEEE, 7962--7971."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.clinicalnlp-1.2"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00883"},{"key":"e_1_3_2_1_45_1","volume-title":"Prototype Completion With Primitive Knowledge for Few-Shot Learning. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021","author":"Zhang Baoquan","year":"2021","unstructured":"Baoquan Zhang, Xutao Li, Yunming Ye, Zhichao Huang, and Lisai Zhang. 2021. Prototype Completion With Primitive Knowledge for Few-Shot Learning. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021. Computer Vision Foundation \/ IEEE, 3754--3762."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3217373"},{"key":"e_1_3_2_1_47_1","first-page":"9432","volume-title":"Rethinking Class Relations: Absolute-Relative Supervised and Unsupervised Few-Shot Learning. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021","author":"Zhang Hongguang","year":"2021","unstructured":"Hongguang Zhang, Piotr Koniusz, Songlei Jian, Hongdong Li, and Philip H. S. Torr. 2021. Rethinking Class Relations: Absolute-Relative Supervised and Unsupervised Few-Shot Learning. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2021, virtual, June 19-25, 2021. Computer Vision Foundation \/ IEEE, 9432-9441."},{"key":"e_1_3_2_1_48_1","volume-title":"Vision-Language Models for Vision Tasks: A Survey. CoRR abs\/2304.00685","author":"Zhang Jingyi","year":"2023","unstructured":"Jingyi Zhang, Jiaxing Huang, Sheng Jin, and Shijian Lu. 2023. Vision-Language Models for Vision Tasks: A Survey. CoRR abs\/2304.00685 (2023)."},{"key":"e_1_3_2_1_49_1","volume-title":"Tel Aviv","volume":"510","author":"Zhang Renrui","year":"2022","unstructured":"Renrui Zhang, Wei Zhang, Rongyao Fang, Peng Gao, Kunchang Li, Jifeng Dai, Yu Qiao, and Hongsheng Li. 2022. Tip-Adapter: Training-Free Adaption of CLIP for Few-Shot Classification. In Computer Vision - ECCV 2022 - 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXV (Lecture Notes in Computer Science, Vol. 13695), Shai Avidan, Gabriel J. Brostow, Moustapha Ciss\u00e9, Giovanni Maria Farinella, and Tal Hassner (Eds.). Springer, 493--510."},{"key":"e_1_3_2_1_50_1","volume-title":"Conditional Prompt Learning for Vision-Language Models. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022","author":"Zhou Kaiyang","year":"2022","unstructured":"Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. 2022. Conditional Prompt Learning for Vision-Language Models. In IEEE\/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022. IEEE, 16795--16804."},{"key":"e_1_3_2_1_51_1","volume-title":"Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement. In IEEE\/CVF International Conference on Computer Vision, ICCV 2023","author":"Zhu Xiangyang","year":"2023","unstructured":"Xiangyang Zhu, Renrui Zhang, Bowei He, Aojun Zhou, Dong Wang, Bin Zhao, and Peng Gao. 2023. Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement. In IEEE\/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1-6, 2023. IEEE, 2605--2615."}],"event":{"name":"MM '24: The 32nd ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Melbourne VIC Australia","acronym":"MM '24"},"container-title":["Proceedings of the 32nd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680668","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3664647.3680668","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:57Z","timestamp":1750295877000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3664647.3680668"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,28]]},"references-count":51,"alternative-id":["10.1145\/3664647.3680668","10.1145\/3664647"],"URL":"https:\/\/doi.org\/10.1145\/3664647.3680668","relation":{},"subject":[],"published":{"date-parts":[[2024,10,28]]},"assertion":[{"value":"2024-10-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}