{"type":"ScholarlyArticle","title":"Hierarchical Prompt Learning for Compositional Zero-Shot Recognition","authors":[{"name":"Henan Wang","slug":"henan-wang","given":"Henan","family":"Wang","affiliation":"School of Electronic Engineering , Xidian University , Xi'an , China"},{"name":"Muli Yang","slug":"muli-yang","given":"Muli","family":"Yang","affiliation":"School of Electronic Engineering , Xidian University , Xi'an , China"},{"name":"Kun Wei","slug":"kun-wei","given":"Kun","family":"Wei","affiliation":"School of Electronic Engineering , Xidian University , Xi'an , China"},{"name":"Cheng Deng","slug":"cheng-deng","given":"Cheng","family":"Deng","affiliation":"School of Electronic Engineering , Xidian University , Xi'an , China"}],"abstract":"Compositional Zero-Shot Learning (CZSL) aims to imitate the powerful generalization ability of human beings to recognize novel compositions of known primitive concepts that correspond to a state and an object, e.g., purple apple. To fully capture the intra- and inter-class correlations between compositional concepts, in this paper, we propose to learn them in a hierarchical manner. Specifically, we set up three hierarchical embedding spaces that respectively model the states, the objects, and their compositions, which serve as three “experts” that can be combined in inference for more accurate predictions. We achieve this based on the recent success of large-scale pretrained vision-language models, e.g., CLIP, which provides a strong initial knowledge of image-text relationships. To better adapt this knowledge to CZSL, we propose to learn three hierarchical prompts by explicitly fixing the unrelated word tokens in the three embedding spaces. Despite its simplicity, our proposed method consistently yields superior performance over current state-of-the-art approaches on three widely-used CZSL benchmarks.","doi":"10.24963/ijcai.2023/163","url":"https://ijcai.org/proceedings/2023/163","pdf":"https://ijcai.org/proceedings/2023/0163.pdf","markdown":"https://ijcai.org/proceedings/2023/163.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/163","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":163,"pages":{"first":1470,"last":1478},"keywords":["Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning","Computer Vision: CV: Recognition (object detection, categorization)"],"videos":[],"references":[{"position":1,"raw":"[Atzmon et al., 2016] Yuval Atzmon, Jonathan Berant, Vahid Kezami, Amir Globerson, and Gal Chechik. Learning to generalize to new compositions in image understanding. arXiv preprint arXiv:1608.07639, 2016.","title":"Learning to generalize to new compositions in image understanding","authors":"Atzmon","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Atzmon et al., 2020] Yuval Atzmon, Felix Kreuk, Uri Shalit, and Gal Chechik. A causal view of compositional zero-shot recognition. In Conference on Neural Informa- tion Processing Systems, pages 1462-1473, 2020.","title":"A causal view of compositional zero-shot recognition","authors":"Atzmon","year":"2020","venue":"Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bach et al., 2022] Stephen H Bach, Victor Sanh, Zheng-Xin Yong, Albert Webson, Colin Raffel, Nihal V Nayak, Ab- heesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, et al. Promptsource: An integrated development environment and repository for natural language prompts. arXiv preprint arXiv:2202.01279, 2022.","title":"Promptsource: An integrated development environment and repository for natural language prompts","authors":"H Stephen, Victor Bach, Zheng-Xin Sanh, Albert Yong, Colin Webson, Raffel, V Nihal, Abheesht Nayak, Taewoon Sharma, M Kim, Thibault Saiful Bari, Fevry","year":"2022","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bommasani et al., 2021] Rishi Bommasani, Drew A Hud- son, Ehsan Adeli, Russ Altman, Simran Arora, Syd- ney von Arx, Michael S Bernstein, Jeannette Bohg, An- toine Bosselut, Emma Brunskill, et al. On the oppor- tunities and risks of foundation models. arXiv preprint arXiv:2108.07258, 2021.","title":"On the opportunities and risks of foundation models","authors":"Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney Von Arx, Jeannette Michael S Bernstein, Antoine Bohg, Emma Bosselut, Brunskill","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Brown et al., 2020] Tom Brown, Benjamin Mann, Nick Ry- der, Melanie Subbiah, Jared D Kaplan, Prafulla Dhari- wal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. In Conference on Neural Information Process- ing Systems, pages 1877-1901, 2020.","title":"Language models are few-shot learners","authors":"Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell","year":"2020","venue":"Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Chao et al., 2016] Wei-Lun Chao, Soravit Changpinyo, Bo- qing Gong, and Fei Sha. An empirical study and analysis of generalized zero-shot learning for object recognition in the wild. In European Conference on Computer Vision, pages 52-68, 2016.","title":"An empirical study and analysis of generalized zero-shot learning for object recognition in the wild","authors":"Wei-Lun Chao, Soravit Changpinyo, Boqing Gong, Fei Sha","year":"2016","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Dong et al., 2019] Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. Unified language model pre- training for natural language understanding and genera- tion. In Conference on Neural Information Processing Sys- tems, 2019.","title":"Unified language model pretraining for natural language understanding and generation","authors":"Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, Hsiao-Wuen Hon","year":"2019","venue":"Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hudson and Manning, 2018] Drew A Hudson and Christo- pher D Manning. Compositional attention networks for machine reasoning. In International Conference on Learn- ing Representations, pages 1-20, 2018.","title":"Compositional attention networks for machine reasoning","authors":"A Drew, Christopher D Hudson, Manning","year":"2018","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hudson and Manning, 2019] Drew A Hudson and Christo- pher D Manning. Gqa: A new dataset for real-world vi- sual reasoning and compositional question answering. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6700-6709, 2019.","title":"Gqa: A new dataset for real-world visual reasoning and compositional question answering","authors":"A Drew, Christopher D Hudson, Manning","year":"2019","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Huynh and Elhamifar, 2020] Dat Huynh and Ehsan Elham- ifar. Compositional zero-shot learning via fine-grained dense feature composition. In Conference on Neural In- formation Processing Systems, pages 19849-19860, 2020.","title":"Dat Huynh and Ehsan Elhamifar. Compositional zero-shot learning via fine-grained dense feature composition","authors":"Elhamifar Huynh","year":"2020","venue":"Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Isola et al., 2015] Phillip Isola, Joseph J Lim, and Edward H Adelson. Discovering states and transformations in image collections. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1383-1391, 2015.","title":"Discovering states and transformations in image collections","authors":"Phillip Isola, Joseph J Lim, Edward H Adelson","year":"2015","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Johnson et al., 2017] Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, and Ross Girshick. Clevr: A diagnostic dataset for com- positional language and elementary visual reasoning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2901-2910, 2017.","title":"Clevr: A diagnostic dataset for compositional language and elementary visual reasoning","authors":"Justin Johnson, Bharath Hariharan, Laurens Van Der Maaten, Li Fei-Fei, C Lawrence Zitnick, Ross Girshick","year":"2017","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Karthik et al., 2022] Shyamgopal Karthik, Massimiliano Mancini, and Zeynep Akata. Kg-sp: Knowledge guided simple primitives for open world compositional zero-shot learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9336-9345, 2022.","title":"Kg-sp: Knowledge guided simple primitives for open world compositional zero-shot learning","authors":"Karthik","year":"2022","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Kingma and Ba, 2014] Diederik P Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.","title":"Adam: A method for stochastic optimization","authors":"P Diederik, Jimmy Kingma, Ba","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Li et al., 2020] Yong-Lu Li, Yue Xu, Xiaohan Mao, and Cewu Lu. Symmetry and group in attribute-object com- positions. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11316-11325, 2020.","title":"Symmetry and group in attribute-object compositions","authors":"Yong-Lu Li, Yue Xu, Xiaohan Mao, Cewu Lu","year":"2020","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Li et al., 2021] Xiangyu Li, Zhe Xu, Kun Wei, and Cheng Deng. Generalized zero-shot learning via disentangled representation. In AAAI Conference on Artificial Intelli- gence, pages 1966-1974, 2021.","title":"Generalized zero-shot learning via disentangled representation","authors":"Xiangyu Li, Zhe Xu, Kun Wei, Cheng Deng","year":"2021","venue":"AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2022] Xiangyu Li, Xu Yang, Kun Wei, Cheng Deng, and Muli Yang. Siamese contrastive embedding net- work for compositional zero-shot learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9326-9335, 2022.","title":"Siamese contrastive embedding network for compositional zero-shot learning","authors":"Xiangyu Li, Xu Yang, Kun Wei, Cheng Deng, Muli Yang","year":"2022","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Mancini et al., 2021] Massimiliano Mancini, Muham- mad Ferjad Naeem, Yongqin Xian, and Zeynep Akata. Open world compositional zero-shot learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5222-5230, 2021.","title":"Open world compositional zero-shot learning","authors":"Massimiliano Mancini, Muhammad Ferjad Naeem, Yongqin Xian, Zeynep Akata","year":"2021","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Mancini et al., 2022] Massimiliano Mancini, Muham- mad Ferjad Naeem, Yongqin Xian, and Zeynep Akata. Learning graph embeddings for open world compositional zero-shot learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 1-15, 2022.","title":"Learning graph embeddings for open world compositional zero-shot learning","authors":"Massimiliano Mancini, Muhammad Ferjad Naeem, Yongqin Xian, Zeynep Akata","year":"2022","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Misra et al., 2017] Ishan Misra, Abhinav Gupta, and Mar- tial Hebert. From red wine to red tomato: Composition with context. In IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 1792-1801, 2017.","title":"From red wine to red tomato: Composition with context","authors":"Ishan Misra, Abhinav Gupta, Martial Hebert","year":"2017","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Naeem et al., 2021] Muhammad Ferjad Naeem, Yongqin Xian, Federico Tombari, and Zeynep Akata. Learning graph embeddings for compositional zero-shot learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 953-962, 2021.","title":"Learning graph embeddings for compositional zero-shot learning","authors":"Naeem","year":"2021","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Nagarajan and Grauman, 2018] Tushar Nagarajan and Kris- ten Grauman. Attributes as operators: factorizing unseen attribute-object compositions. In European Conference on Computer Vision, pages 169-185, 2018.","title":"Attributes as operators: factorizing unseen attribute-object compositions","authors":"Tushar Nagarajan, Kristen Grauman","year":"2018","venue":"European Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Nan et al., 2019] Zhixiong Nan, Yang Liu, Nanning Zheng, and Song-Chun Zhu. Recognizing unseen attribute-object pair with generative model. In AAAI Conference on Artifi- cial Intelligence, pages 8811-8818, 2019.","title":"Recognizing unseen attribute-object pair with generative model","authors":"Nan","year":"2019","venue":"AAAI Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Nayak et al., 2023] Nihal V. Nayak, Peilin Yu, and Stephen Bach. Learning to compose soft prompts for composi- tional zero-shot learning. In International Conference on Learning Representations, pages 1-21, 2023.","title":"Learning to compose soft prompts for compositional zero-shot learning","authors":"V Nihal, Peilin Nayak, Stephen Yu, Bach","year":"2023","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Paszke et al., 2019] Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. Pytorch: An imperative style, high- performance deep learning library. In Conference on Neu- ral Information Processing Systems, pages 8024-8035, 2019.","title":"Pytorch: An imperative style, highperformance deep learning library","authors":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga","year":"2019","venue":"Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Peters et al., 2018] Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. Deep contextualized word representations. In Meeting of the Association for Com- putational Linguistics, pages 2227-2237, 2018.","title":"Deep contextualized word representations","authors":"Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, Luke Zettlemoyer","year":"2018","venue":"Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Purushwalkam et al., 2019] Senthil Purushwalkam, Maxi- milian Nickel, Abhinav Gupta, and Marc'Aurelio Ranzato. Task-driven modular networks for zero-shot compositional learning. In IEEE International Conference on Computer Vision, pages 3593-3602, 2019.","title":"Task-driven modular networks for zero-shot compositional learning","authors":"Senthil Purushwalkam, Maximilian Nickel, Abhinav Gupta, Marc'aurelio Ranzato","year":"2019","venue":"IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Qin and Eisner, 2021] Guanghui Qin and Jason Eisner. Learning how to ask: Querying lms with mixtures of soft prompts. arXiv preprint arXiv:2104.06599, 2021.","title":"Learning how to ask: Querying lms with mixtures of soft prompts","authors":"Eisner Qin, Guanghui Qin, Jason Eisner","year":"2021","venue":null,"doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Radford et al., 2021] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agar- wal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from nat- ural language supervision. In International Conference on Machine Learning, pages 8748-8763, 2021.","title":"Learning transferable visual models from natural language supervision","authors":"Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark","year":"2021","venue":"International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Ruis et al., 2021] Frank Ruis, Gertjan Burghouts, and Doina Bucur. Independent prototype propagation for zero- shot compositionality. In Conference on Neural Informa- tion Processing Systems, pages 10641-10653, 2021.","title":"Independent prototype propagation for zeroshot compositionality","authors":"Ruis","year":"2021","venue":"Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Saini et al., 2022] Nirat Saini, Khoi Pham, and Abhinav Shrivastava. Disentangling visual embeddings for at- tributes and objects. In IEEE/CVF Conference on Com- puter Vision and Pattern Recognition, pages 13658- 13667, 2022.","title":"Disentangling visual embeddings for attributes and objects","authors":"Nirat Saini, Khoi Pham, Abhinav Shrivastava","year":"2022","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Sanh et al., 2022] Victor Sanh, Albert Webson, Colin Raf- fel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. Multitask prompted training enables zero-shot task generalization. In International Conference on Learn- ing Representations, pages 1-216, 2022.","title":"Multitask prompted training enables zero-shot task generalization","authors":"Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja","year":"2022","venue":"International Conference on Learning Representations","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Vu et al., 2022] Tu Vu, Brian Lester, Noah Constant, Rami Al-Rfou, and Daniel Cer. Spot: Better frozen model adap- tation through soft prompt transfer. In Meeting of the Asso- ciation for Computational Linguistics, pages 5039-5059, 2022.","title":"Spot: Better frozen model adaptation through soft prompt transfer","authors":"Tu Vu, Brian Lester, Noah Constant, Rami Al-Rfou, Daniel Cer","year":"2022","venue":"Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Wei et al., 2019] Kun Wei, Muli Yang, Hao Wang, Cheng Deng, and Xianglong Liu. Adversarial fine-grained com- position learning for unseen attribute-object recognition. In IEEE International Conference on Computer Vision, pages 3741-3749, 2019.","title":"Adversarial fine-grained composition learning for unseen attribute-object recognition","authors":"Kun Wei, Muli Yang, Hao Wang, Cheng Deng, Xianglong Liu","year":"2019","venue":"IEEE International Conference on Computer Vision","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Wei et al., 2020] Kun Wei, Cheng Deng, Xu Yang, et al. Lifelong zero-shot learning. In International Joint Con- ference on Artificial Intelligence, pages 551-557, 2020.","title":"Lifelong zero-shot learning","authors":"Kun Wei, Cheng Deng, Xu Yang","year":"2020","venue":"International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2020/77"},{"position":36,"raw":"[Xu et al., 2021] Ziwei Xu, Guangzhi Wang, Yongkang Wong, and Mohan S Kankanhalli. Relation-aware compo- sitional zero-shot learning for attribute-object pair recog- nition. IEEE Transactions on Multimedia, 24(8):3652- 3664, 2021.","title":"Relation-aware compositional zero-shot learning for attribute-object pair recognition","authors":"Ziwei Xu, Guangzhi Wang, Yongkang Wong, Mohan S Kankanhalli","year":"2021","venue":"IEEE Transactions on Multimedia","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Yang et al., 2019] Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. Xlnet: Generalized autoregressive pretraining for lan- guage understanding. In Conference on Neural Informa- tion Processing Systems, 2019.","title":"Xlnet: Generalized autoregressive pretraining for language understanding","authors":"Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, Quoc V Le","year":"2019","venue":"Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Yang et al., 2020] Muli Yang, Cheng Deng, Junchi Yan, Xi- anglong Liu, and Dacheng Tao. Learning unseen con- cepts via hierarchical decomposition and composition. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10248-10256, 2020.","title":"Learning unseen concepts via hierarchical decomposition and composition","authors":"Muli Yang, Cheng Deng, Junchi Yan, Xianglong Liu, Dacheng Tao","year":"2020","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":39,"raw":"[Yang et al., 2021] Yanhua Yang, Xiaozhe Zhang, Muli Yang, and Cheng Deng. Adaptive bias-aware feature gen- eration for generalized zero-shot learning. IEEE Transac- tions on Multimedia, 25(11):280-290, 2021.","title":"Adaptive bias-aware feature generation for generalized zero-shot learning","authors":"Yanhua Yang, Xiaozhe Zhang, Muli Yang, Cheng Deng","year":"2021","venue":"IEEE Transactions on Multimedia","doi":null,"url":null,"internal_url":null},{"position":40,"raw":"[Yang et al., 2022] Muli Yang, Chenghao Xu, Aming Wu, and Cheng Deng. A decomposable causal view of com- positional zero-shot learning. IEEE Transactions on Mul- timedia, pages 1-11, 2022.","title":"A decomposable causal view of compositional zero-shot learning","authors":"Muli Yang, Chenghao Xu, Aming Wu, Cheng Deng","year":"2022","venue":"IEEE Transactions on Multimedia","doi":null,"url":null,"internal_url":null},{"position":41,"raw":"[Yu and Grauman, 2014] Aron Yu and Kristen Grauman. Fine-grained visual comparisons with local learning. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 192-199, 2014.","title":"Fine-grained visual comparisons with local learning","authors":"Aron Yu, Kristen Grauman","year":"2014","venue":"IEEE/CVF Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":42,"raw":"[Zhou et al., 2022] Kaiyang Zhou, Jingkang Yang, Chen Change Loy, and Ziwei Liu. Learning to prompt for vision-language models. International Journal of Computer Vision, 130(9):2337-2348, 2022.","title":"Learning to prompt for vision-language models","authors":"Zhou","year":"2022","venue":"International Journal of Computer Vision","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}