{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T04:14:56Z","timestamp":1765340096773,"version":"3.46.0"},"publisher-location":"New York, NY, USA","reference-count":80,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,10,27]]},"DOI":"10.1145\/3746027.3755415","type":"proceedings-article","created":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T07:38:54Z","timestamp":1761377934000},"page":"4388-4397","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["TrustCLIP: Learning from Noisy Labels via Semantic Label Verification and Trust-aligned Gradient Projection"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2394-3518","authenticated-orcid":false,"given":"Xueyi","family":"Zhang","sequence":"first","affiliation":[{"name":"The Chinese University of Hong Kong, Shenzhen, Shenzhen, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2638-4166","authenticated-orcid":false,"given":"Peiyin","family":"Zhu","sequence":"additional","affiliation":[{"name":"National University of Singapore, singapore, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6069-4284","authenticated-orcid":false,"given":"Yuan","family":"Liao","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Shenzhen, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4321-7906","authenticated-orcid":false,"given":"Xiyu","family":"Wang","sequence":"additional","affiliation":[{"name":"Software Academy, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8413-7220","authenticated-orcid":false,"given":"Mingrui","family":"Lao","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4386-0472","authenticated-orcid":false,"given":"Siqi","family":"Cai","sequence":"additional","affiliation":[{"name":"Harbin Institute of Technology, Shenzhen, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9184-5313","authenticated-orcid":false,"given":"Yanming","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Systems and Engineering, National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9158-9401","authenticated-orcid":false,"given":"Haizhou","family":"Li","sequence":"additional","affiliation":[{"name":"School of Data Science, The Chinese University of Hong Kong, Shenzhen, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,10,27]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00130"},{"key":"e_1_3_2_1_2_1","volume-title":"On the relationship between skill neurons and robustness in prompt tuning. arXiv preprint arXiv:2309.12263","author":"Ackermann Leon","year":"2023","unstructured":"Leon Ackermann and Xenia Ohmer. 2023. On the relationship between skill neurons and robustness in prompt tuning. arXiv preprint arXiv:2309.12263 (2023)."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW60793.2023.00505"},{"key":"e_1_3_2_1_4_1","volume-title":"International conference on machine learning. PMLR, 312-321","author":"Arazo Eric","year":"2019","unstructured":"Eric Arazo, Diego Ortego, Paul Albert, Noel O'Connor, and Kevin McGuinness. 2019. Unsupervised label noise modeling and loss correction. In International conference on machine learning. PMLR, 312-321."},{"key":"e_1_3_2_1_5_1","volume-title":"Transfer learning for image classification using VGG19: Caltech-101 image data set. Journal of ambient intelligence and humanized computing","author":"Bansal Monika","year":"2023","unstructured":"Monika Bansal, Munish Kumar, Monika Sachdeva, and Ajay Mittal. 2023. Transfer learning for image classification using VGG19: Caltech-101 image data set. Journal of ambient intelligence and humanized computing (2023), 1-12."},{"volume-title":"Attribute-based Visual Reprogramming for Vision-Language Models. In The Thirteenth International Conference on Learning Representations.","author":"Cai Chengyi","key":"e_1_3_2_1_6_1","unstructured":"Chengyi Cai, Zesheng Ye, Lei Feng, Jianzhong Qi, and Feng Liu. [n.d.]. Attribute-based Visual Reprogramming for Vision-Language Models. In The Thirteenth International Conference on Learning Representations."},{"key":"e_1_3_2_1_7_1","volume-title":"Sample-specific masks for visual reprogramming-based prompting. arXiv preprint arXiv:2406.03150","author":"Cai Chengyi","year":"2024","unstructured":"Chengyi Cai, Zesheng Ye, Lei Feng, Jianzhong Qi, and Feng Liu. 2024. Sample-specific masks for visual reprogramming-based prompting. arXiv preprint arXiv:2406.03150 (2024)."},{"key":"e_1_3_2_1_8_1","unstructured":"Guangyi Chen Weiran Yao Xiangchen Song Xinyue Li Yongming Rao and Kun Zhang. 2022. Prompt learning with optimal transport for vision-language models. (2022)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs13101922"},{"key":"e_1_3_2_1_10_1","volume-title":"Learning with instance-dependent label noise: A sample sieve approach. arXiv preprint arXiv:2010.02347","author":"Cheng Hao","year":"2020","unstructured":"Hao Cheng, Zhaowei Zhu, Xingyu Li, Yifei Gong, Xing Sun, and Yang Liu. 2020. Learning with instance-dependent label noise: A sample sieve approach. arXiv preprint arXiv:2010.02347 (2020)."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.461"},{"key":"e_1_3_2_1_12_1","volume-title":"Harmonizing generalization and personalization in federated prompt learning. arXiv preprint arXiv:2405.09771","author":"Cui Tianyu","year":"2024","unstructured":"Tianyu Cui, Hongxia Li, Jingya Wang, and Ye Shi. 2024. Harmonizing generalization and personalization in federated prompt learning. arXiv preprint arXiv:2405.09771 (2024)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01551"},{"key":"e_1_3_2_1_14_1","first-page":"1","article-title":"Flower species recognition system using convolution neural networks and transfer learning. In 2017 fourth international conference on signal processing, communication and networking (ICSCN)","author":"Gogul I","year":"2017","unstructured":"I Gogul and V Sathiesh Kumar. 2017. Flower species recognition system using convolution neural networks and transfer learning. In 2017 fourth international conference on signal processing, communication and networking (ICSCN). IEEE, 1-6.","journal-title":"IEEE"},{"key":"e_1_3_2_1_15_1","volume-title":"Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572","author":"Goodfellow Ian J","year":"2014","unstructured":"Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014. Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572 (2014)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.3390\/rs16162975"},{"key":"e_1_3_2_1_17_1","volume-title":"LoPT: Low-Rank Prompt Tuning for Parameter Efficient Language Models. arXiv preprint arXiv:2406.19486","author":"Guo Shouchang","year":"2024","unstructured":"Shouchang Guo, Sonam Damani, and Keng-hao Chang. 2024a. LoPT: Low-Rank Prompt Tuning for Parameter Efficient Language Models. arXiv preprint arXiv:2406.19486 (2024)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02711"},{"key":"e_1_3_2_1_19_1","volume-title":"Co-teaching: Robust training of deep neural networks with extremely noisy labels. Advances in neural information processing systems","author":"Han Bo","year":"2018","unstructured":"Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama. 2018. Co-teaching: Robust training of deep neural networks with extremely noisy labels. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2019.2918242"},{"key":"e_1_3_2_1_21_1","volume-title":"International conference on machine learning. PMLR, 2712-2721","author":"Hendrycks Dan","year":"2019","unstructured":"Dan Hendrycks, Kimin Lee, and Mantas Mazeika. 2019. Using pre-training can improve model robustness and uncertainty. In International conference on machine learning. PMLR, 2712-2721."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00342"},{"key":"e_1_3_2_1_23_1","volume-title":"Unsupervised prompt learning for vision-language models. arXiv preprint arXiv:2204.03649","author":"Huang Tony","year":"2022","unstructured":"Tony Huang, Jack Chu, and Fangyun Wei. 2022. Unsupervised prompt learning for vision-language models. arXiv preprint arXiv:2204.03649 (2022)."},{"key":"e_1_3_2_1_24_1","volume-title":"International conference on machine learning. PMLR, 4904-4916","author":"Jia Chao","year":"2021","unstructured":"Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig. 2021. Scaling up visual and vision-language representation learning with noisy text supervision. In International conference on machine learning. PMLR, 4904-4916."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01436"},{"key":"e_1_3_2_1_26_1","volume-title":"Muzammal Naseer, Luc Van Gool, and Federico Tombari.","author":"Khattak Muhammad Uzair","year":"2024","unstructured":"Muhammad Uzair Khattak, Muhammad Ferjad Naeem, Muzammal Naseer, Luc Van Gool, and Federico Tombari. 2024. Learning to prompt with text only supervision for vision-language models. arXiv preprint arXiv:2401.02418 (2024)."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01832"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01394"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00164"},{"key":"e_1_3_2_1_30_1","volume-title":"Active Prompt Learning with Vision-Language Model Priors. arXiv preprint arXiv:2411.16722","author":"Kim Hoyoung","year":"2024","unstructured":"Hoyoung Kim, Seokhee Jin, Changhwan Sung, Jaechang Kim, and Jungseul Ok. 2024a. Active Prompt Learning with Vision-Language Model Priors. arXiv preprint arXiv:2411.16722 (2024)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00019"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2013.77"},{"key":"e_1_3_2_1_33_1","volume-title":"CLIP Adaptation by Intra-modal Overlap Reduction. arXiv preprint arXiv:2409.11338","author":"Kravets Alexey","year":"2024","unstructured":"Alexey Kravets and Vinay Namboodiri. 2024. CLIP Adaptation by Intra-modal Overlap Reduction. arXiv preprint arXiv:2409.11338 (2024)."},{"key":"e_1_3_2_1_34_1","volume-title":"International conference on machine learning. PMLR, 3763-3772","author":"Lee Kimin","year":"2019","unstructured":"Kimin Lee, Sukmin Yun, Kibok Lee, Honglak Lee, Bo Li, and Jinwoo Shin. 2019. Robust inference via generative classifiers for handling noisy labels. In International conference on machine learning. PMLR, 3763-3772."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00571"},{"key":"e_1_3_2_1_36_1","volume-title":"Dividemix: Learning with noisy labels as semi-supervised learning. arXiv preprint arXiv:2002.07394","author":"Li Junnan","year":"2020","unstructured":"Junnan Li, Richard Socher, and Steven CH Hoi. 2020. Dividemix: Learning with noisy labels as semi-supervised learning. arXiv preprint arXiv:2002.07394 (2020)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.02513"},{"key":"e_1_3_2_1_38_1","volume-title":"ATPrompt: Textual Prompt Learning with Embedded Attributes. arXiv preprint arXiv:2412.09442","author":"Li Zheng","year":"2024","unstructured":"Zheng Li, Yibing Song, Penghai Zhao, Ming-Ming Cheng, Xiang Li, and Jian Yang. 2024b. ATPrompt: Textual Prompt Learning with Embedded Attributes. arXiv preprint arXiv:2412.09442 (2024)."},{"key":"e_1_3_2_1_39_1","volume-title":"Thresholding classifiers to maximize F1 score. arXiv preprint arXiv:1402.1892","author":"Lipton Zachary Chase","year":"2014","unstructured":"Zachary Chase Lipton, Charles Elkan, and Balakrishnan Narayanaswamy. 2014. Thresholding classifiers to maximize F1 score. arXiv preprint arXiv:1402.1892 (2014)."},{"key":"e_1_3_2_1_40_1","volume-title":"Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983","author":"Loshchilov Ilya","year":"2016","unstructured":"Ilya Loshchilov and Frank Hutter. 2016. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)."},{"key":"e_1_3_2_1_41_1","volume-title":"Parameter-efficient low-resource dialogue state tracking by prompt tuning. arXiv preprint arXiv:2301.10915","author":"Ma Mingyu Derek","year":"2023","unstructured":"Mingyu Derek Ma, Jiun-Yu Kao, Shuyang Gao, Arpit Gupta, Di Jin, Tagyoung Chung, and Nanyun Peng. 2023. Parameter-efficient low-resource dialogue state tracking by prompt tuning. arXiv preprint arXiv:2301.10915 (2023)."},{"key":"e_1_3_2_1_42_1","volume-title":"Learning with noisy labels. Advances in neural information processing systems","author":"Natarajan Nagarajan","year":"2013","unstructured":"Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari. 2013. Learning with noisy labels. Advances in neural information processing systems, Vol. 26 (2013)."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICVGIP.2008.47"},{"key":"e_1_3_2_1_44_1","volume-title":"NLPrompt: Noise-Label Prompt Learning for Vision-Language Models. arXiv preprint arXiv:2412.01256","author":"Pan Bikang","year":"2024","unstructured":"Bikang Pan, Qun Li, Xiaoying Tang, Wei Huang, Zhen Fang, Feng Liu, Jingya Wang, Jingyi Yu, and Ye Shi. 2024. NLPrompt: Noise-Label Prompt Learning for Vision-Language Models. arXiv preprint arXiv:2412.01256 (2024)."},{"key":"e_1_3_2_1_45_1","volume-title":"SPECIAL: Zero-shot Hyperspectral Image Classification With CLIP. arXiv preprint arXiv:2501.16222","author":"Pang Li","year":"2025","unstructured":"Li Pang, Jing Yao, Kaiyu Li, and Xiangyong Cao. 2025. SPECIAL: Zero-shot Hyperspectral Image Classification With CLIP. arXiv preprint arXiv:2501.16222 (2025)."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2012.6248092"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV56688.2023.00392"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.240"},{"key":"e_1_3_2_1_49_1","volume-title":"Philip HS Torr, and Adel Bibi","author":"Petrov Aleksandar","year":"2023","unstructured":"Aleksandar Petrov, Philip HS Torr, and Adel Bibi. 2023. When do prompting and prefix-tuning work? a theory of capabilities and limitations. arXiv preprint arXiv:2310.19698 (2023)."},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.69"},{"key":"e_1_3_2_1_51_1","volume-title":"European Conference on Computer Vision. Springer, 196-212","author":"Qu Linhao","year":"2024","unstructured":"Linhao Qu, Dingkang Yang, Dan Huang, Qinhao Guo, Rongkui Luo, Shaoting Zhang, and Xiaosong Wang. 2024. Pathology-knowledge enhanced multi-instance prompt learning for few-shot whole slide image classification. In European Conference on Computer Vision. Springer, 196-212."},{"key":"e_1_3_2_1_52_1","volume-title":"International conference on machine learning. PmLR, 8748-8763","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, et al., 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PmLR, 8748-8763."},{"key":"e_1_3_2_1_53_1","volume-title":"Training deep neural networks on noisy labels with bootstrapping. arXiv preprint arXiv:1412.6596","author":"Reed Scott","year":"2014","unstructured":"Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich. 2014. Training deep neural networks on noisy labels with bootstrapping. arXiv preprint arXiv:1412.6596 (2014)."},{"key":"e_1_3_2_1_54_1","volume-title":"International conference on machine learning. PMLR, 4334-4343","author":"Ren Mengye","year":"2018","unstructured":"Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun. 2018. Learning to reweight examples for robust deep learning. In International conference on machine learning. PMLR, 4334-4343."},{"key":"e_1_3_2_1_55_1","volume-title":"Consistency-guided prompt learning for vision-language models. arXiv preprint arXiv:2306.01195","author":"Roy Shuvendu","year":"2023","unstructured":"Shuvendu Roy and Ali Etemad. 2023. Consistency-guided prompt learning for vision-language models. arXiv preprint arXiv:2306.01195 (2023)."},{"key":"e_1_3_2_1_56_1","unstructured":"Mahtab Sandhu Yann Batiste Pequignot Samer B Nashed Sabyasachi Sahoo and Liam Paull. [n.d.]. CLIP-Enhance: Improving CLIP Zero-Shot Classification via von Mises-Fisher Clustering. ( [n. d.])."},{"key":"e_1_3_2_1_57_1","volume-title":"International conference on machine learning. PMLR, 5907-5915","author":"Song Hwanjun","year":"2019","unstructured":"Hwanjun Song, Minseok Kim, and Jae-Gil Lee. 2019. Selfie: Refurbishing unclean samples for robust deep learning. In International conference on machine learning. PMLR, 5907-5915."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3152527"},{"key":"e_1_3_2_1_59_1","volume-title":"Amir Roshan Zamir, and Mubarak Shah","author":"Soomro Khurram","year":"2012","unstructured":"Khurram Soomro, Amir Roshan Zamir, and Mubarak Shah. 2012. UCF101: A dataset of 101 human actions classes from videos in the wild. arXiv preprint arXiv:1212.0402 (2012)."},{"key":"e_1_3_2_1_60_1","volume-title":"Training convolutional networks with noisy labels. arXiv preprint arXiv:1406.2080","author":"Sukhbaatar Sainbayar","year":"2014","unstructured":"Sainbayar Sukhbaatar, Joan Bruna, Manohar Paluri, Lubomir Bourdev, and Rob Fergus. 2014. Training convolutional networks with noisy labels. arXiv preprint arXiv:1406.2080 (2014)."},{"key":"e_1_3_2_1_61_1","volume-title":"Slide-Level Prompt Learning with Vision Language Models for Few-Shot Multiple Instance Learning in Histopathology. arXiv preprint arXiv:2503.17238","author":"Tomar Devavrat","year":"2025","unstructured":"Devavrat Tomar, Guillaume Vray, Dwarikanath Mahapatra, Sudipta Roy, Jean-Philippe Thiran, and Behzad Bozorgtabar. 2025. Slide-Level Prompt Learning with Vision Language Models for Few-Shot Multiple Instance Learning in Histopathology. arXiv preprint arXiv:2503.17238 (2025)."},{"key":"e_1_3_2_1_62_1","volume-title":"Toward robustness against label noise in training deep discriminative neural networks. Advances in neural information processing systems","author":"Vahdat Arash","year":"2017","unstructured":"Arash Vahdat. 2017. Toward robustness against label noise in training deep discriminative neural networks. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_63_1","volume-title":"Promptem: prompt-tuning for low-resource generalized entity matching. arXiv preprint arXiv:2207.04802","author":"Wang Pengfei","year":"2022","unstructured":"Pengfei Wang, Xiaocan Zeng, Lu Chen, Fan Ye, Yuren Mao, Junhao Zhu, and Yunjun Gao. 2022. Promptem: prompt-tuning for low-resource generalized entity matching. arXiv preprint arXiv:2207.04802 (2022)."},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3440068"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01420"},{"key":"e_1_3_2_1_66_1","volume-title":"International conference on learning representations.","author":"Xia Xiaobo","year":"2020","unstructured":"Xiaobo Xia, Tongliang Liu, Bo Han, Chen Gong, Nannan Wang, Zongyuan Ge, and Yi Chang. 2020. Robust early-learning: Hindering the memorization of noisy labels. In International conference on learning representations."},{"key":"e_1_3_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cag.2024.01.012"},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2018.2877939"},{"key":"e_1_3_2_1_69_1","volume-title":"Gradient surgery for multi-task learning. Advances in neural information processing systems","author":"Yu Tianhe","year":"2020","unstructured":"Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, and Chelsea Finn. 2020. Gradient surgery for multi-task learning. Advances in neural information processing systems, Vol. 33 (2020), 5824-5836."},{"key":"e_1_3_2_1_70_1","volume-title":"Florence: A new foundation model for computer vision. arXiv preprint arXiv:2111.11432","author":"Yuan Lu","year":"2021","unstructured":"Lu Yuan, Dongdong Chen, Yi-Ling Chen, Noel Codella, Xiyang Dai, Jianfeng Gao, Houdong Hu, Xuedong Huang, Boxin Li, Chunyuan Li, et al., 2021. Florence: A new foundation model for computer vision. arXiv preprint arXiv:2111.11432 (2021)."},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01759"},{"key":"e_1_3_2_1_72_1","volume-title":"Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530","author":"Zhang Chiyuan","year":"2016","unstructured":"Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. 2016. Understanding deep learning requires rethinking generalization. arXiv preprint arXiv:1611.03530 (2016)."},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19833-5_29"},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2025.112974"},{"key":"e_1_3_2_1_75_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i7.28568"},{"key":"e_1_3_2_1_76_1","volume-title":"Generalized cross entropy loss for training deep neural networks with noisy labels. Advances in neural information processing systems","author":"Zhang Zhilu","year":"2018","unstructured":"Zhilu Zhang and Mert Sabuncu. 2018. Generalized cross entropy loss for training deep neural networks with noisy labels. Advances in neural information processing systems, Vol. 31 (2018)."},{"key":"e_1_3_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17319"},{"key":"e_1_3_2_1_78_1","volume-title":"Benchmarking pathCLIP for pathology image analysis. Journal of Imaging Informatics in Medicine","author":"Zheng Sunyi","year":"2024","unstructured":"Sunyi Zheng, Xiaonan Cui, Yuxuan Sun, Jingxiong Li, Honglin Li, Yunlong Zhang, Pingyi Chen, Xueping Jing, Zhaoxiang Ye, and Lin Yang. 2024. Benchmarking pathCLIP for pathology image analysis. Journal of Imaging Informatics in Medicine (2024), 1-17."},{"key":"e_1_3_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01631"},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-022-01653-1"}],"event":{"name":"MM '25: The 33rd ACM International Conference on Multimedia","sponsor":["SIGMM ACM Special Interest Group on Multimedia"],"location":"Dublin Ireland","acronym":"MM '25"},"container-title":["Proceedings of the 33rd ACM International Conference on Multimedia"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746027.3755415","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T04:11:17Z","timestamp":1765339877000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746027.3755415"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,27]]},"references-count":80,"alternative-id":["10.1145\/3746027.3755415","10.1145\/3746027"],"URL":"https:\/\/doi.org\/10.1145\/3746027.3755415","relation":{},"subject":[],"published":{"date-parts":[[2025,10,27]]},"assertion":[{"value":"2025-10-27","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}