{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T06:19:09Z","timestamp":1778048349575,"version":"3.51.4"},"reference-count":78,"publisher":"IEEE","license":[{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T00:00:00Z","timestamp":1772755200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,6]]},"DOI":"10.1109\/wacv61042.2026.00473","type":"proceedings-article","created":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T19:59:32Z","timestamp":1778011172000},"page":"4869-4880","source":"Crossref","is-referenced-by-count":0,"title":["CoL\n                    <sup>2<\/sup>\n                    A: Convolution-free Local Linear Attention for SpatioTemporal Event Processing"],"prefix":"10.1109","author":[{"given":"Yusuke","family":"Sekikawa","sequence":"first","affiliation":[{"name":"DENSO IT Lab., Inc."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Nagata","sequence":"additional","affiliation":[{"name":"DENSO IT Lab., Inc."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Itsumi","family":"Araki","sequence":"additional","affiliation":[{"name":"DENSO IT Lab., Inc."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andreu","family":"Girbau","sequence":"additional","affiliation":[{"name":"DENSO IT Lab., Inc."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2015.2474396"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2849882"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.781"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-27272-2_35"},{"key":"ref5","article-title":"Delving deeper into convolutional networks for learning video representations","volume-title":"4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings","author":"Ballas"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00058"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2020.3023597"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58565-5_9"},{"key":"ref9","volume-title":"Openeb-core: Generic algorithms for visualization, event stream manipulation","author":"Charles","year":"2025"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW53098.2021.00153"},{"key":"ref11","author":"Chiberre","year":"2022","journal-title":"Long-lived accurate keypoints in event streams"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref13","volume-title":"Introduction to algorithms","author":"Cormen","year":"2009"},{"key":"ref14","author":"Dao","year":"2024","journal-title":"Transformers are ssms: Generalized models and efficient algorithms through structured state space duality"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/MM.2018.112130359"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref17","author":"Dosovitskiy","year":"2020","journal-title":"An image is worth 16x16 words: Transformers for image recognition at scale"},{"key":"ref18","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","volume-title":"ICLR","author":"Dosovitskiy"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1109\/34.3909"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00437"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00573"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3060707"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.01334"},{"key":"ref24","author":"Gu","year":"2024","journal-title":"Mamba: Linear-time sequence modeling with selective state spaces"},{"key":"ref25","author":"Gu","year":"2021","journal-title":"Efficiently modeling long sequences with structured state spaces"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02190"},{"key":"ref27","author":"Heinsen","year":"2023","journal-title":"Efficient parallelization of a ubiquitous sequential computation"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-72684-2_17"},{"key":"ref29","author":"Jiazhou","year":"2024","journal-title":"Path-adaptive spatio-temporal state space model for event-based recognition with arbitrary duration"},{"key":"ref30","article-title":"Associative memory augmented asynchronous spatiotemporal representation learning for event-based perception","volume-title":"The Eleventh International Conference on Learning Representations","author":"Kamal"},{"key":"ref31","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-73007-8_5","article-title":"Efficient learning of event-based dense representation using hierarchical memories with adaptive update","volume-title":"European Conference on Computer Vision","author":"Kamal"},{"key":"ref32","first-page":"5156","article-title":"Transformers are rnns: Fast autoregressive transformers with linear attention","volume-title":"International conference on machine learning","author":"Katharopoulos"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00097"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"ref36","first-page":"5","author":"Loshchilov","year":"2017","journal-title":"Fixing weight decay regularization in adam"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1016\/S0893-6080(97)00011-7"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.01049"},{"key":"ref39","author":"Mehta","year":"2021","journal-title":"Mobilevit: lightweight, general-purpose, and mobile-friendly vision transformer"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.5244\/C.31.33"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA48891.2023.10161164"},{"key":"ref42","article-title":"Sigma delta quantized networks","volume-title":"International Conference on Learning Representations","author":"O\u2019Connor"},{"key":"ref43","first-page":"8024","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume-title":"Advances in Neural Information Processing Systems 32","author":"Paszke","year":"2019"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-emnlp.936"},{"key":"ref45","first-page":"652","article-title":"Pointnet: Deep learning on point sets for 3d classification and segmentation","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Qi"},{"key":"ref46","article-title":"Pointnet++: Deep hierarchical feature learning on point sets in a metric space","volume-title":"Advances in Neural Information Processing Systems","volume":"30","author":"Qi","year":"2017"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1145\/3571155"},{"key":"ref48","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2019.2963386"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2025.3556561"},{"key":"ref50","author":"Rossi","year":"2020","journal-title":"Temporal graph networks for deep learning on dynamic graphs"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01205"},{"key":"ref52","doi-asserted-by":"publisher","DOI":"10.1109\/ICONS62911.2024.00026"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1109\/wacv45572.2020.9093366"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093366"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00401"},{"key":"ref56","first-page":"802","article-title":"Convolutional lstm network: a machine learning approach for precipitation nowcasting","volume-title":"Proceedings of the 29th International Conference on Neural Information Processing Systems-Volume 1","author":"Shi"},{"key":"ref57","author":"Sim\u00e9oni","year":"2025","journal-title":"DINOv3"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00186"},{"key":"ref59","author":"Smith","year":"2023","journal-title":"Simplified state space layers for sequence modeling"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58583-9_32"},{"key":"ref61","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.127063"},{"key":"ref62","author":"Sun","year":"2023","journal-title":"Retentive network: A successor to transformer for large language models"},{"key":"ref63","author":"Sun","year":"2023","journal-title":"Retentive network: A successor to transformer for large language models"},{"key":"ref64","first-page":"10347","article-title":"Training data-efficient image transformers distillation through attention","volume-title":"Proceedings of the 38th International Conference on Machine Learning, volume 139 of Proceedings of Machine Learning Research","author":"Touvron"},{"key":"ref65","article-title":"Alert-transformer: Bridging asynchronous and synchronous machine learning for real-time event-based spatio-temporal data","volume-title":"Forty-first International Conference on Machine Learning","author":"Turrero"},{"key":"ref66","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1706.03762"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2019.00199"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1007\/s41095-022-0274-8"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00585"},{"key":"ref71","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00256"},{"key":"ref72","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"ref73","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00062"},{"key":"ref74","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref75","doi-asserted-by":"publisher","DOI":"10.1109\/VLSM.2001.938900"},{"key":"ref76","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3228168"},{"key":"ref77","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00108"},{"key":"ref78","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00556"}],"event":{"name":"2026 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)","location":"Tucson, AZ, USA","start":{"date-parts":[[2026,3,6]]},"end":{"date-parts":[[2026,3,10]]}},"container-title":["2026 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11491838\/11491925\/11492663.pdf?arnumber=11492663","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T05:59:24Z","timestamp":1778047164000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11492663\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,6]]},"references-count":78,"URL":"https:\/\/doi.org\/10.1109\/wacv61042.2026.00473","relation":{},"subject":[],"published":{"date-parts":[[2026,3,6]]}}}