{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T08:52:00Z","timestamp":1743151920983,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":24,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819784899"},{"type":"electronic","value":"9789819784905"}],"license":[{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T00:00:00Z","timestamp":1730937600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-97-8490-5_20","type":"book-chapter","created":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T09:09:07Z","timestamp":1730884147000},"page":"276-289","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Global Structural Consistency Set Transformer"],"prefix":"10.1007","author":[{"given":"Zengbiao","family":"Yang","sequence":"first","affiliation":[]},{"given":"Yihua","family":"Tan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"20_CR1","doi-asserted-by":"crossref","unstructured":"He, C., Li, R., Li, S., Zhang, L.: Voxel set transformer: a set-to-set approach to 3d object detection from point clouds. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8417\u20138427 (2022)","DOI":"10.1109\/CVPR52688.2022.00823"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Woedlinger, M., Reiter, M., Weijler, L., Maurer-Granofszky, M., Schumich, A., Sajaroff, E. O., ... Dworzak, M. N.: Automated identification of cell populations in flow cytometry data with transformers. Comput. Biol. Med. 144, 105314 (2022)","DOI":"10.1016\/j.compbiomed.2022.105314"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 213\u2013229 (2020)","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"20_CR4","unstructured":"Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R.R., Smola, A.J.: Deep sets. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"20_CR5","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N. et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"20_CR6","unstructured":"Lee, J., Lee, Y., Kim, J., Kosiorek, A., Choi, S., Teh, Y. W.: Set transformer: a framework for attention-based permutation-invariant neural networks. In: International Conference on Machine Learning, pp. 3744\u20133753. PMLR (2019)"},{"key":"20_CR7","unstructured":"Bucur, A.M., Cosma, A., Dinu, L.P., Rosso, P.: An End-to-End Set Transformer for User-Level Classification of Depression and Gambling Disorder (2022). arXiv:2207.00753 (2022)"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Gim, M., Choi, D., Maruyama, K., Choi, J., Kim, H., Park, D., Kang, J.: RecipeMind: guiding ingredient choices from food pairing to recipe completion using cascaded set transformer. In: Proceedings of the 31th ACM International on Conference on Information and Knowledge Management, pp. 3092\u20133102 (2022)","DOI":"10.1145\/3511808.3557092"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Kim, J., Yoo, J., Lee, J., Hong, S. Setvae: learning hierarchical composition for generative modeling of set-structured data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 15059\u201315068 (2021)","DOI":"10.1109\/CVPR46437.2021.01481"},{"key":"20_CR10","unstructured":"Jaegle, A., Gimeno, F., Brock, A., Vinyals, O., Zisserman, A., Carreira, J.: Perceiver: general perception with iterative attention. In: International Conference on Machine Learning, pp. 4651\u20134664. PMLR (2021)"},{"key":"20_CR11","unstructured":"Jaegle, A., Borgeaud, S., Alayrac, J.B., Doersch, C., Ionescu, C., Ding, D., Carreira, J.: Perceiver IO: A General Architecture for Structured Inputs and Outputs (2021). arXiv:2107.14795"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Tang, Z., Cho, J., Lei, J., Bansal, M.: Perceiver-vl: efficient vision-and-language modeling with iterative latent attention. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 4410\u20134420 (2023)","DOI":"10.1109\/WACV56688.2023.00439"},{"key":"20_CR13","unstructured":"Wang, S., Li, B. Z., Khabsa, M., Fang, H., Ma, H.: Linformer: Self-attention with Linear Complexity (2020). arXiv:2006.04768"},{"key":"20_CR14","unstructured":"Zhang, F., Liu, B., Wang, K., Tan, V., Yang, Z., Wang, Z.: Relational reasoning via set transformers: provable efficiency and applications to MARL. In: Advances in Neural Information Processing Systems, vol. 35 (2022)"},{"key":"20_CR15","unstructured":"Girgis, R., Golemo, F., Codevilla, F., Weiss, M., D\u2019Souza, J.A., Kahou, S.E., ... Pal, C.: Latent Variable Sequential Set Transformers for Joint Multi-agent Motion Prediction (2021). arXiv:2104.00563"},{"key":"20_CR16","unstructured":"Hudson, D.A., Zitnick, L.: Generative adversarial transformers. In: International Conference on Machine Learning, pp. 4487\u20134499. PMLR (2021)"},{"key":"20_CR17","unstructured":"Shahbazi, A., Kothapalli, A., Liu, X., Sheng, R., Kolouri, S.: Equivariant vs. Invariant Layers: A Comparison of Backbone and Pooling for Point Cloud Classification (2023). arXiv:2306.05553"},{"key":"20_CR18","unstructured":"Zare, S., Van Nguyen, H.: Picaso: Permutation-invariant Cascaded Attentional Set Operator (2021). arXiv:2107.08305"},{"key":"20_CR19","unstructured":"Zhang, L., Tozzo, V., Higgins, J., Ranganath, R.: Set norm and equivariant skip connections: putting the deep in deep sets. In: International Conference on Machine Learning, pp. 26559\u201326574. PMLR (2022)"},{"key":"20_CR20","unstructured":"Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: a deep representation for volumetric shapes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1912\u20131920 (2015)"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Uy, M.A., Pham, Q.H., Hua, B.S., Nguyen, T., Yeung, S.K.: Revisiting point cloud classification: a new benchmark dataset and classification model on real-world data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1588\u20131597 (2019)","DOI":"10.1109\/ICCV.2019.00167"},{"key":"20_CR22","unstructured":"Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., ... Yu, F.: Shapenet: An Information-rich 3D Model Repository (2015). arXiv:1512.03012"},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Yi, H., Stanley, N.: CytoSet: predicting clinical outcomes via set-modeling of cytometry data. In: Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, pp. 1\u20138 (2021)","DOI":"10.1145\/3459930.3469529"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Dworzak, M. N., Gaipa, G., Ratei, R., Veltroni, M., Schumich, A., Maglia, O., ... Basso, G.: Standardization of flow cytometric minimal residual disease evaluation in acute lymphoblastic leukemia: Multicentric assessment is feasible. Cytometry Part B: Clinical Cytometry. J. Int. Soc. Anal. Cytol. 74(6), 331\u2013340.(2008)","DOI":"10.1002\/cyto.b.20430"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8490-5_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T09:15:14Z","timestamp":1730884514000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8490-5_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,7]]},"ISBN":["9789819784899","9789819784905"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8490-5_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,7]]},"assertion":[{"value":"7 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision  (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Urumqi","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2024.prcv.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}