{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T06:23:25Z","timestamp":1764915805639,"version":"3.46.0"},"reference-count":62,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2025]]},"DOI":"10.1109\/access.2025.3637887","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T18:59:03Z","timestamp":1764269943000},"page":"202086-202102","source":"Crossref","is-referenced-by-count":0,"title":["Spatial Sensitive Grad-CAM: Toward Instance-Specific Explanations for Object Detectors by Incorporating Spatial Sensitivity"],"prefix":"10.1109","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1223-9508","authenticated-orcid":false,"given":"Toshinori","family":"Yamauchi","sequence":"first","affiliation":[{"name":"Research and Development Group, Hitachi Ltd., Yokohama-shi, Kanagawa-Ken, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.l007\/978-3-319-46448-0_2"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00530"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73195-2_25"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2021.3122835"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106144"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s11554-023-01353-0"},{"key":"ref9","first-page":"9505","article-title":"Sanity checks for saliency maps","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"31","author":"Adebayo"},{"key":"ref10","article-title":"SmoothGrad: Removing noise by adding noise","author":"Smilkov","year":"2017","journal-title":"arXiv:1706.03825"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW50498.2020.00020"},{"key":"ref13","first-page":"151","article-title":"RISE: Randomized input sampling for explanation of black-box models","volume-title":"Proc. Brit. Mach. Vis. Conf. (BMVC)","author":"Petsiuk"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19775-8_27"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.5244\/C.34.146"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.00575"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1405.0312"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-009-0275-4"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICIP46576.2022.9897350"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref26","article-title":"YOLOv3: An incremental improvement","author":"Redmon","year":"2018","journal-title":"arXiv:1804.02767"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2004.10934"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33019259"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref32","first-page":"1","article-title":"Deformable DETR: Deformable transformers for end-to-end object detection","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR)","author":"Zhu"},{"key":"ref33","first-page":"6725","article-title":"DETRs with collaborative hybrid assignments training","volume-title":"Proc. IEEE\/CVF Int. Conf. Comput. Vis. (ICCV)","author":"Zong"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00397"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW59228.2023.00376"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2024.3380604"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01128"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3391424"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3369890"},{"key":"ref42","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52734.2025.02796"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.5555\/3104322.3104425"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref45","article-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014","journal-title":"arXiv:1409.1556"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00097"},{"key":"ref47","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-017-1059-x"},{"key":"ref48","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.112145","article-title":"Shrinkage mamba relation network with out-of-distribution data augmentation for rotating machinery fault detection and localization under zero-faulty data","volume":"224","author":"Chen","year":"2025","journal-title":"Mech. Syst. Signal Process."},{"key":"ref49","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109753","article-title":"Zero-faulty sample machinery fault detection via relation network with out-of-distribution data augmentation","volume":"141","author":"Chen","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref50","article-title":"Deep inside convolutional networks: Visualising image classification models and saliency maps","author":"Simonyan","year":"2013","journal-title":"arXiv:1312.6034"},{"key":"ref51","article-title":"Visualizing and understanding convolutional networks","author":"Zeiler","year":"2013","journal-title":"arXiv:1311.2901"},{"key":"ref52","first-page":"1","article-title":"Striving for simplicity: The all convolutional net","volume-title":"Proc. Int. Conf. Learn. Represent. (ICLR) Workshop Track","author":"Springenberg"},{"key":"ref53","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0130140"},{"key":"ref54","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-3020"},{"key":"ref55","first-page":"1","article-title":"A unified approach to interpreting model predictions","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"30","author":"Lundberg"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.371"},{"key":"ref57","first-page":"1","article-title":"Real time image saliency for black box classifiers","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS)","volume":"30","author":"Dabkowski"},{"key":"ref58","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00931"},{"article-title":"FastSHAP: Real-time Shapley value estimation","volume-title":"Proc. Int. Conf. Learn. Represent. (ICRL)","author":"Jethani","key":"ref59"},{"key":"ref60","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref61","first-page":"1","article-title":"Automatic differentiation in PyTorch","volume-title":"Proc. Adv. Neural Inf. Process. Syst. (NeurIPS) Workshop Autodiff","author":"Paszke"},{"key":"ref62","first-page":"265","article-title":"TensorFlow: A system for large-scale machine learning","volume-title":"Proc. 12th USENIX Conf. Operating Syst. Design Implement. (OSDI)","author":"Abadi"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/11270886.pdf?arnumber=11270886","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T06:20:02Z","timestamp":1764915602000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11270886\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":62,"URL":"https:\/\/doi.org\/10.1109\/access.2025.3637887","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2025]]}}}