{"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/exfuse-enhancing-feature-fusion-for-semantic","title":"ExFuse: Enhancing Feature Fusion for Semantic Segmentation","arxiv_id":"1804.03821","date":"2018-04-11","proceeding":"ECCV 2018 9","authors":["Zhenli Zhang","Xiangyu Zhang","Chao Peng","Dazhi Cheng","Jian Sun"],"abstract":"Modern semantic segmentation frameworks usually combine low-level and\nhigh-level features from pre-trained backbone convolutional models to boost\nperformance. In this paper, we first point out that a simple fusion of\nlow-level and high-level features could be less effective because of the gap in\nsemantic levels and spatial resolution. We find that introducing semantic\ninformation into low-level features and high-resolution details into high-level\nfeatures is more effective for the later fusion. Based on this observation, we\npropose a new framework, named ExFuse, to bridge the gap between low-level and\nhigh-level features thus significantly improve the segmentation quality by\n4.0\\% in total. Furthermore, we evaluate our approach on the challenging PASCAL\nVOC 2012 segmentation benchmark and achieve 87.9\\% mean IoU, which outperforms\nthe previous state-of-the-art results.","url_abs":"http://arxiv.org/abs/1804.03821v1","url_pdf":"http://arxiv.org/pdf/1804.03821v1.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":[],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"grouped-convolution","method_name":"Grouped Convolution"},{"method_slug":"kaiming-initialization","method_name":"Kaiming Initialization"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"resnext","method_name":"ResNeXt"},{"method_slug":"resnext-block","method_name":"ResNeXt Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-segmentation-on-pascal-voc-2012-val","task":"Semantic Segmentation","dataset":"PASCAL VOC 2012 val","model":"ExFuse (ResNeXt-131)","rank_in_archive_order":4,"of":29,"metrics":{"mIoU":"85.8%"},"uses_additional_data":true}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.03821","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}