{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T23:57:59Z","timestamp":1726185479978},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031222153"},{"type":"electronic","value":"9783031222160"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-22216-0_42","type":"book-chapter","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T23:39:26Z","timestamp":1673912366000},"page":"630-642","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Towards Synthetic Data: Dealing with\u00a0the\u00a0Texture-Bias in\u00a0Sim2real Learning"],"prefix":"10.1007","author":[{"given":"Jelena","family":"Tabak","sequence":"first","affiliation":[]},{"given":"Marsela","family":"Poli\u0107","sequence":"additional","affiliation":[]},{"given":"Matko","family":"Orsag","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,18]]},"reference":[{"key":"42_CR1","doi-asserted-by":"crossref","unstructured":"Azad, R., Fayjie, A.R., Kauffmann, C., Ben\u00a0Ayed, I., Pedersoli, M., Dolz, J.: On the texture bias for few-shot cnn segmentation. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), pp. 2674\u20132683 (January 2021)","DOI":"10.1109\/WACV48630.2021.00272"},{"key":"42_CR2","doi-asserted-by":"crossref","unstructured":"Baker, N., Lu, H., Erlikhman, G., Kellman, P.: Deep convolutional networks do not classify based on global object shape. PLOS Comput. Biol. 14, e1006613 (12 2018)","DOI":"10.1371\/journal.pcbi.1006613"},{"key":"42_CR3","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.visres.2020.04.003","volume":"172","author":"N Baker","year":"2020","unstructured":"Baker, N., Lu, H., Erlikhman, G., Kellman, P.J.: Local features and global shape information in object classification by deep convolutional neural networks. Vision Res. 172, 46\u201361 (2020)","journal-title":"Vision Res."},{"key":"42_CR4","doi-asserted-by":"crossref","unstructured":"Barth, R., IJsselmuiden, J., Hemming, J., Van\u00a0Henten, E.J.: Data synthesis methods for semantic segmentation in agriculture: a capsicum annuum dataset. Comput. Electronics Agric. 144, 284\u2013296 (2018)","DOI":"10.1016\/j.compag.2017.12.001"},{"key":"42_CR5","unstructured":"Brendel, W., Bethge, M.: Approximating CNNs with bag-of-local-features models works surprisingly well on imagenet. In: International Conference on Learning Representations (2019)"},{"key":"42_CR6","unstructured":"Brochu, F.: Increasing shape bias in imagenet-trained networks using transfer learning and domain-adversarial methods. CoRR abs\/1907.12892 (2019). http:\/\/arxiv.org\/abs\/1907.12892"},{"key":"42_CR7","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV) (September 2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"42_CR8","unstructured":"Co, K.T., Mu\u00f1oz-Gonz\u00e1lez, L., Kanthan, L., Glocker, B., Lupu, E.C.: Universal Adversarial Robustness of Texture and Shape-Biased Models. arXiv e-prints arXiv:1911.10364 (2019)"},{"key":"42_CR9","doi-asserted-by":"crossref","unstructured":"Di\u00a0Cicco, M., Potena, C., Grisetti, G., Pretto, A.: Automatic model based dataset generation for fast and accurate crop and weeds detection. In: 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 5188\u20135195. IEEE (2017)","DOI":"10.1109\/IROS.2017.8206408"},{"key":"42_CR10","unstructured":"Doersch, C., Zisserman, A.: Sim2real transfer learning for 3d human pose estimation: motion to the rescue. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol.\u00a032. Curran Associates, Inc (2019)"},{"key":"42_CR11","doi-asserted-by":"crossref","unstructured":"Geirhos, R., Jacobsen, J.H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., Wichmann, F.: Shortcut learning in deep neural networks. Nat. Mach. Intell. 2, 665\u2013673 (11 2020)","DOI":"10.1038\/s42256-020-00257-z"},{"key":"42_CR12","unstructured":"Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F., Brendel, W.: Imagenet-Trained CNNs are Biased Towards Texture; Increasing Shape Bias Improves Accuracy and Robustness. ArXiv abs\/1811.12231 (2019)"},{"key":"42_CR13","unstructured":"Hermann, K., Chen, T., Kornblith, S.: The origins and prevalence of texture bias in convolutional neural networks. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol.\u00a033, pp. 19000\u201319015. Curran Associates, Inc (2020)"},{"key":"42_CR14","unstructured":"Hess, R.: Blender Foundations: The Essential Guide to Learning Blender 2.6. Focal Press (2010)"},{"key":"42_CR15","unstructured":"Islam, M.A., Kowal, M., Esser, P., Jia, S., Ommer, B., Derpanis, K.G., Bruce, N.D.B.: Shape or Texture: Understanding Discriminative Features in CNNs. CoRR abs\/2101.11604 (2021). https:\/\/arxiv.org\/abs\/2101.11604"},{"key":"42_CR16","doi-asserted-by":"crossref","unstructured":"Kheradpisheh, S.R., Ghodrati, M., Ganjtabesh, M., Masquelier, T.: Deep networks can resemble human feed-forward vision in invariant object recognition. Sci. Rep. 6, 32672 (2016)","DOI":"10.1038\/srep32672"},{"key":"42_CR17","doi-asserted-by":"crossref","unstructured":"Kim, M., Byun, H.: Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation, pp. 12972\u201312981 (2020)","DOI":"10.1109\/CVPR42600.2020.01299"},{"key":"42_CR18","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. Neural Inf. Process. Syst. 25 (2012)"},{"key":"42_CR19","unstructured":"Li, Y., Yu, Q., Tan, M., Mei, J., Tang, P., Shen, W., Yuille, A., Xie, C.: Shape-texture debiased neural network training. In: International Conference on Learning Representations (2021)"},{"key":"42_CR20","unstructured":"Malhotra, G., Bowers, J.: The contrasting roles of shape in human vision and convolutional neural networks. In: Goel, A., Seifert, C., Freksa, C. (eds.) Proceedings of the 41st Annual Conference of the Cognitive Science Society (2019)"},{"key":"42_CR21","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.visres.2020.04.013","volume":"174","author":"G Malhotra","year":"2020","unstructured":"Malhotra, G., Evans, B.D., Bowers, J.S.: Hiding a plane with a pixel: examining shape-bias in CNNs and the benefit of building in biological constraints. Vision Res. 174, 57\u201368 (2020)","journal-title":"Vision Res."},{"key":"42_CR22","unstructured":"Mishra, S.K., Shah, A., Bansal, A., Choi, J., Shrivastava, A., Sharma, A., Jacobs, D.W.: Learning Visual Representations for Transfer Learning by Suppressing Texture. CoRR abs\/2011.01901 (2020). https:\/\/arxiv.org\/abs\/2011.01901"},{"key":"42_CR23","unstructured":"Mohla, S., Nasery, A., Banerjee, B., Chaudhuri, S.: Cognitivecnn: Mimicking Human Cognitive Models to Resolve Texture-Shape Bias. CoRR abs\/2006.14722 (2020). https:\/\/arxiv.org\/abs\/2006.14722"},{"key":"42_CR24","unstructured":"Nam, H., Lee, H., Park, J., Yoon, W., Yoo, D.: Reducing Domain Gap by Reducing Style Bias. arXiv e-prints arXiv:1910.11645 (2019)"},{"key":"42_CR25","doi-asserted-by":"crossref","unstructured":"Pashevich, A., Strudel, R., Kalevatykh, I., Laptev, I., Schmid, C.: Learning to augment synthetic images for sim2real policy transfer. In: 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2651\u20132657 (2019)","DOI":"10.1109\/IROS40897.2019.8967622"},{"key":"42_CR26","doi-asserted-by":"crossref","unstructured":"Polic, M., Ivanovic, A., Maric, B., Arbanas, B., Tabak, J., Orsag, M.: Structured ecological cultivation with autonomous robots in indoor agriculture. In: 2021 16th International Conference on Telecommunications (ConTEL), pp. 189\u2013195 (2021)","DOI":"10.23919\/ConTEL52528.2021.9495963"},{"key":"42_CR27","doi-asserted-by":"crossref","unstructured":"Polic, M., Tabak, J., Orsag, M.: Pepper to fall: a perception method for sweet pepper robotic harvesting. In: Intelligent Service Robotics (2021)","DOI":"10.1007\/s11370-021-00401-7"},{"key":"42_CR28","unstructured":"Ringer, S., Williams, W., Ash, T., Francis, R., MacLeod, D.: Texture Bias of CNNs Limits Few-Shot Classification Performance. CoRR abs\/1910.08519 (2019). http:\/\/arxiv.org\/abs\/1910.08519"},{"key":"42_CR29","unstructured":"Ritter, S., Barrett, D., Santoro, A., Botvinick, M.: Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study (2017)"},{"key":"42_CR30","unstructured":"Tabak, J., Polic, M., Orsag, M.: Synthetic Dataset Generation Pipeline. https:\/\/github.com\/larics\/blender_synthetic_data_generator (2021). Accessed 22 Apr 2022"},{"key":"42_CR31","unstructured":"Tabak, J., Polic, M., Orsag, M.: Synthetic Plant Datasets. https:\/\/www.kaggle.com\/datasets\/jele38\/synthetic-plant-datasets (2022). Accessed 22 Apr 2022"},{"key":"42_CR32","unstructured":"Ward, D., Moghadam, P., Hudson, N.: Deep Leaf Segmentation Using Synthetic Data. CoRR abs\/1807.10931 (2018). http:\/\/arxiv.org\/abs\/1807.10931"},{"key":"42_CR33","doi-asserted-by":"crossref","unstructured":"Zaech, J.N., Dai, D., Hahner, M., Gool, L.V.: Texture underfitting for domain adaptation. In: 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pp. 547\u2013552 (2019)","DOI":"10.1109\/ITSC.2019.8917059"},{"key":"42_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhanga, Y., Xu, Q., Zhang, R.: Learning robust shape-based features for domain generalization. In: IEEE Access, pp. 1\u20131 (2020)","DOI":"10.1109\/ACCESS.2020.2984279"}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Autonomous Systems 17"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-22216-0_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T23:51:33Z","timestamp":1673913093000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-22216-0_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031222153","9783031222160"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-22216-0_42","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"18 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IAS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Autonomous Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zagreb","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Croatia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ias2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.ias-17.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}