{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/onenet-a-channel-wise-1d-convolutional-u-net","title":"OneNet: A Channel-Wise 1D Convolutional U-Net","arxiv_id":"2411.09838","date":"2024-11-14","proceeding":null,"authors":["Sanghyun Byun","Kayvan Shah","Ayushi Gang","Christopher Apton","Jacob Song","Woo Seong Chung"],"abstract":"Many state-of-the-art computer vision architectures leverage U-Net for its adaptability and efficient feature extraction. However, the multi-resolution convolutional design often leads to significant computational demands, limiting deployment on edge devices. We present a streamlined alternative: a 1D convolutional encoder that retains accuracy while enhancing its suitability for edge applications. Our novel encoder architecture achieves semantic segmentation through channel-wise 1D convolutions combined with pixel-unshuffle operations. By incorporating PixelShuffle, known for improving accuracy in super-resolution tasks while reducing computational load, OneNet captures spatial relationships without requiring 2D convolutions, reducing parameters by up to 47%. Additionally, we explore a fully 1D encoder-decoder that achieves a 71% reduction in size, albeit with some accuracy loss. We benchmark our approach against U-Net variants across diverse mask-generation tasks, demonstrating that it preserves accuracy effectively. Although focused on image segmentation, this architecture is adaptable to other convolutional applications. Code for the project is available at https://github.com/shbyun080/OneNet .","url_abs":"https://arxiv.org/abs/2411.09838v1","url_pdf":"https://arxiv.org/pdf/2411.09838v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"onenet-a-channel-wise-1d-convolutional-u-net","repo_url":"https://github.com/shbyun080/onenet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"pixelshuffle","method_name":"PixelShuffle"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-segmentation-on-msd-heart","task":"Image Segmentation","dataset":"MSD Heart","model":"OneNete,4","rank_in_archive_order":1,"of":1,"metrics":{"mIoU":"6.6"},"uses_additional_data":false},{"leaderboard":"/sota/image-segmentation-on-oxfordpets","task":"Image Segmentation","dataset":"OxfordPets","model":"OneNete,4-C","rank_in_archive_order":1,"of":1,"metrics":{"Dice Score":"0.967"},"uses_additional_data":false},{"leaderboard":"/sota/image-segmentation-on-pascal-voc","task":"Image Segmentation","dataset":"PASCAL VOC","model":"OneNete,4-C","rank_in_archive_order":1,"of":3,"metrics":{"mIoU":"63.6"},"uses_additional_data":false},{"leaderboard":"/sota/image-segmentation-on-pascal-voc","task":"Image Segmentation","dataset":"PASCAL VOC","model":"OneNeted,4","rank_in_archive_order":2,"of":3,"metrics":{"mIoU":"14.9"},"uses_additional_data":false},{"leaderboard":"/sota/image-segmentation-on-pascal-voc","task":"Image Segmentation","dataset":"PASCAL VOC","model":"OneNete,4-S","rank_in_archive_order":3,"of":3,"metrics":{"mAP0.5":"52.75"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}