{"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/what-s-there-in-the-dark","title":"What's There in the Dark","arxiv_id":null,"date":"2019-09-24","proceeding":null,"authors":["Sauradip Nag","Saptakatha Adak","Sukhendu Das"],"abstract":"Scene  Parsing  is  an  important  cog  for  modern  autonomousdriving systems.   Most of the works in semantic segmenta-tion pertains to day-time scenes with favourable weather andillumination  conditions.   In  this  paper,  we  propose  a  noveldeep architecture, NiSeNet, that performs semantic segmen-tation of night scenes using a domain mapping approach ofsynthetic  to  real  data.   It  is  a  dual-channel  network,  wherewe designed a Real channel using DeepLabV3+ coupled withan MSE loss to preserve the spatial information.  In addition,we used an Adaptive channel reducing the domain gap be-tween  synthetic  and  real  night  images,  which  also  comple-ments  the  failures  of  Real  channel  output.   Apart  from  thedual channel, we introduced a novel fusion scheme to fuse theoutputs of two channels.  In addition to that, we compiled anew dataset Urban Night Driving Dataset (UNDD); it consistsof7125unlabelled day and night images; additionally, it has75night images with pixel-level annotations having classesequivalent to Cityscapes dataset.  We evaluated our approachon  the  Berkley  Deep  Drive  dataset,  the  challenging  Mapil-lary dataset and UNDD dataset to exhibit that the proposedmethod outperforms the state-of-the-art techniques in termsof accuracy and visual quality","url_abs":"https://ieeexplore.ieee.org/document/8803299","url_pdf":"https://ieeexplore.ieee.org/document/8803299","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":"what-s-there-in-the-dark","repo_url":"https://github.com/sauradip/night_image_semantic_segmentation","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"scene-parsing","task_name":"Scene Parsing"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"aspp","method_name":"ASPP"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"deeplabv3","method_name":"DeepLabv3"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"},{"method_slug":"spatial-pyramid-pooling","method_name":"Spatial Pyramid Pooling"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/semantic-segmentation-on-bdd100k-val","task":"Semantic Segmentation","dataset":"BDD100K val","model":"NiseNet","rank_in_archive_order":9,"of":24,"metrics":{"mIoU":"53.52"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-mapillary-val","task":"Semantic Segmentation","dataset":"Mapillary val","model":"NiseNet","rank_in_archive_order":5,"of":8,"metrics":{"mIoU":"48.32"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}