{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T06:06:28Z","timestamp":1789538788900,"version":"build-2803163510"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032376237","type":"print"},{"value":"9783032376244","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-37624-4_1","type":"book-chapter","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T05:54:05Z","timestamp":1789538045000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Quantization-Aware Adaptation for\u00a0Visual Foundation Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5426-5529","authenticated-orcid":false,"given":"Yinglong","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8725-8212","authenticated-orcid":false,"given":"Xiaoyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1569-783X","authenticated-orcid":false,"given":"Yutong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0788-8826","authenticated-orcid":false,"given":"Yueyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9787-7460","authenticated-orcid":false,"given":"Zhiwei","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"1_CR1","unstructured":"Brown, T., et al.: Language models are few-shot learners. In: NeurIPS (2020)"},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"Chen, M., et al.: EfficientQAT: efficient quantization-aware training for large language models. In: ACL, pp. 10081\u201310100 (2025)","DOI":"10.18653\/v1\/2025.acl-long.498"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Chen, T., et al.: Sam-Adapter: adapting segment anything in underperformed scenes. In: ICCV Workshop, pp. 3367\u20133375 (2023)","DOI":"10.1109\/ICCVW60793.2023.00361"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"Cho, Y., Lee, C., Kim, S., Park, E.: PTQ4VM: post-training quantization for visual mamba. In: WACV, pp. 1176\u20131185 (2025)","DOI":"10.1109\/WACV61041.2025.00122"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Dettmers, T., Pagnoni, A., Holtzman, A., Zettlemoyer, L.: Qlora: efficient finetuning of quantized LLMS. In: NeurIPS (2023)","DOI":"10.52202\/075280-0441"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), pp. 4171\u20134186 (2019)","DOI":"10.18653\/v1\/N19-1423"},{"key":"1_CR7","unstructured":"Ding, X., et al.: CBQ: Cross-Block Quantization for large language models. In: ICLR (2025)"},{"key":"1_CR8","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. In: ICLR (2021)"},{"key":"1_CR9","unstructured":"Esser, S.K., McKinstry, J.L., Bablani, D., Appuswamy, R., Modha, D.S.: Learned step size quantization. In: ICLR (2020)"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Fan, D., Ji, G., Sun, G., Cheng, M., Shen, J., Shao, L.: Camouflaged object detection. In: CVPR, pp. 2774\u20132784 (2020)","DOI":"10.1109\/CVPR42600.2020.00285"},{"key":"1_CR11","unstructured":"Han, S., Pool, J., Tran, J., Dally, W.J.: Learning both weights and connections for efficient neural network. In: NeurIPS. pp, 1135\u20131143 (2015)"},{"key":"1_CR12","unstructured":"Houlsby, N., et al.: Parameter-efficient transfer learning for NLP. In: ICML, pp. 2790\u20132799 (2019)"},{"key":"1_CR13","unstructured":"Hu, E.J., et al.: LoRA: Low-Rank Adaptation of large language models. In: ICLR. OpenReview.net (2022)"},{"key":"1_CR14","doi-asserted-by":"crossref","unstructured":"Jacob, B., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference, pp. 2704\u20132713. CVPR (2018)","DOI":"10.1109\/CVPR.2018.00286"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Jha, D., et al.: KVASIR-SEG: a segmented polyp dataset. In: MultiMedia Modeling - 26th International Conference, MMM 2020, Daejeon, South Korea, January 5-8, 2020, Proceedings, Part II, pp. 451\u2013462 (2020)","DOI":"10.1007\/978-3-030-37734-2_37"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Jia, M., et al.: Visual prompt tuning. In: ECCV, pp. 709\u2013727 (2022)","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"1_CR17","doi-asserted-by":"crossref","unstructured":"Jiang, Z., et al.: Res-tuning: a flexible and efficient tuning paradigm via unbinding tuner from backbone. In: NeurIPS (2023)","DOI":"10.52202\/075280-1850"},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Kim, J., et al.: Memory-efficient fine-tuning of compressed large language models via sub-4-bit integer quantization. In: NeurIPS (2023)","DOI":"10.52202\/075280-1569"},{"key":"1_CR19","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)"},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"Kirillov, A., et al.: Segment anything. In: ICCV, pp. 3992\u20134003 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"1_CR21","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.cviu.2019.04.006","volume":"184","author":"T Le","year":"2019","unstructured":"Le, T., Nguyen, T.V., Nie, Z., Tran, M., Sugimoto, A.: Anabranch network for camouflaged object segmentation. Comput. Vis. Image Underst. 184, 45\u201356 (2019)","journal-title":"Comput. Vis. Image Underst."},{"key":"1_CR22","doi-asserted-by":"crossref","unstructured":"Li, Z., Xiao, J., Yang, L., Gu, Q.: REPQ-VIT: scale reparameterization for post-training quantization of vision transformers. In: ICCV, pp. 17181\u201317190 (2023)","DOI":"10.1109\/ICCV51070.2023.01580"},{"key":"1_CR23","doi-asserted-by":"crossref","unstructured":"Lin, J., et al.: AWQ: activation-aware weight quantization for on-device LLM compression and acceleration. In: MLSys (2024)","DOI":"10.1145\/3714983.3714987"},{"key":"1_CR24","doi-asserted-by":"crossref","unstructured":"Liu, X., et al.: PQ-SAM: post-training quantization for segment anything model. In: ECCV, pp. 420\u2013437 (2024)","DOI":"10.1007\/978-3-031-72684-2_24"},{"key":"1_CR25","unstructured":"Loshchilov, I., Hutter, F.: SGDR: stochastic gradient descent with warm restarts. In: ICLR (2017)"},{"key":"1_CR26","doi-asserted-by":"crossref","unstructured":"Mercea, O., Gritsenko, A.A., Schmid, C., Arnab, A.: Time-, memory- and parameter-efficient visual adaptation. In: CVPR, pp. 5536\u20135545 (2024)","DOI":"10.1109\/CVPR52733.2024.00529"},{"key":"1_CR27","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: ICML, pp. 8748\u20138763 (2021)"},{"key":"1_CR28","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR, pp. 10674\u201310685 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"1_CR29","doi-asserted-by":"crossref","unstructured":"Shin, J., So, J., Park, S., Kang, S., Yoo, S., Park, E.: NIPQ: noise proxy-based integrated pseudo-quantization. In: CVPR, pp. 3852\u20133861 (2023)","DOI":"10.1109\/CVPR52729.2023.00375"},{"issue":"2","key":"1_CR30","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s11548-013-0926-3","volume":"9","author":"J Silva","year":"2014","unstructured":"Silva, J., Histace, A., Romain, O., Dray, X., Granado, B.: Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer. Int. J. Comput. Assist. Radiol. Surg. 9(2), 283\u2013293 (2014)","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"1_CR31","unstructured":"Sun, M., Liu, Z., Bair, A., Kolter, J.Z.: A simple and effective pruning approach for large language models. In: ICLR (2024)"},{"key":"1_CR32","unstructured":"Touvron, H., et al.: Llama: open and efficient foundation language models. arXiv preprint arXiv:2302.13971 (2023)"},{"key":"1_CR33","unstructured":"Touvron, H., et al.: Llama 2: open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)"},{"issue":"1","key":"1_CR34","first-page":"4037190","volume":"2017","author":"D V\u00e1zquez","year":"2017","unstructured":"V\u00e1zquez, D., et al.: A benchmark for endoluminal scene segmentation of colonoscopy images. J. Healthc. Eng. 2017(1), 4037190 (2017)","journal-title":"J. Healthc. Eng."},{"key":"1_CR35","unstructured":"Xi, H., et al.: COAT: compressing optimizer states and activations for memory-efficient FP8 training. In: ICLR (2025)"},{"key":"1_CR36","unstructured":"Xiao, G., Lin, J., Seznec, M., Wu, H., Demouth, J., Han, S.: SmoothQuant: accurate and efficient post-training quantization for large language models. In: ICML, pp. 38087\u201338099 (2023)"},{"key":"1_CR37","unstructured":"Xu, Y., et al.: QA-LORA: quantization-aware low-rank adaptation of large language models. In: ICLR (2024)"},{"key":"1_CR38","unstructured":"Zhai, X., et al.: A large-scale study of representation learning with the visual task adaptation benchmark. arXiv preprint arXiv:1910.04867 (2019)"},{"key":"1_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, Z., et al.: Quantized side tuning: fast and memory-efficient tuning of quantized large language models. In: ACL, pp. 1\u201317 (2024)","DOI":"10.18653\/v1\/2024.acl-long.1"},{"key":"1_CR40","unstructured":"Zhong, Z., Tang, Z., He, T., Fang, H., Yuan, C.: Convolution meets lora: parameter efficient finetuning for segment anything model. In: ICLR (2024)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-37624-4_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T05:54:19Z","timestamp":1789538059000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-37624-4_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032376237","9783032376244"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-37624-4_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"17 September 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malm\u00f6","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}