{"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/superpixel-based-semantic-segmentation","title":"Superpixel-based Semantic Segmentation Trained by Statistical Process Control","arxiv_id":"1706.10071","date":"2017-06-30","proceeding":null,"authors":["Hyojin Park","Jisoo Jeong","Youngjoon Yoo","Nojun Kwak"],"abstract":"Semantic segmentation, like other fields of computer vision, has seen a\nremarkable performance advance by the use of deep convolution neural networks.\nHowever, considering that neighboring pixels are heavily dependent on each\nother, both learning and testing of these methods have a lot of redundant\noperations. To resolve this problem, the proposed network is trained and tested\nwith only 0.37% of total pixels by superpixel-based sampling and largely\nreduced the complexity of upsampling calculation. The hypercolumn feature maps\nare constructed by pyramid module in combination with the convolution layers of\nthe base network. Since the proposed method uses a very small number of sampled\npixels, the end-to-end learning of the entire network is difficult with a\ncommon learning rate for all the layers. In order to resolve this problem, the\nlearning rate after sampling is controlled by statistical process control (SPC)\nof gradients in each layer. The proposed method performs better than or equal\nto the conventional methods that use much more samples on Pascal Context,\nSUN-RGBD dataset.","url_abs":"http://arxiv.org/abs/1706.10071v2","url_pdf":"http://arxiv.org/pdf/1706.10071v2.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":"superpixel-based-semantic-segmentation","repo_url":"https://github.com/HYOJINPARK/HP-SPS","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"caffe2","reach":null}],"tasks":[{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1706.10071","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}