{"id":"https://openalex.org/W2962858109","doi":"https://doi.org/10.1109/iccv.2017.74","title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","display_name":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2962858109","doi":"https://doi.org/10.1109/iccv.2017.74","mag":"2962858109"},"language":"en","primary_location":{"id":"doi:10.1109/iccv.2017.74","doi":"https://doi.org/10.1109/iccv.2017.74","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv.2017.74","pdf_url":null,"source":{"id":"https://openalex.org/S4363607764","display_name":"IEEE/CVF International Conference on Computer Vision (ICCV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5046238088","display_name":"Ramprasaath R. Selvaraju","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ramprasaath R. Selvaraju","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052890596","display_name":"Michael Cogswell","orcid":"https://orcid.org/0000-0003-1647-0325","observed_orcids":["https://orcid.org/0000-0003-1647-0325"]},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael Cogswell","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102972112","display_name":"Abhishek Das","orcid":"https://orcid.org/0000-0002-4718-4316","observed_orcids":["https://orcid.org/0000-0002-4718-4316"]},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Abhishek Das","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041797310","display_name":"Ramakrishna Vedantam","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ramakrishna Vedantam","raw_affiliation_strings":["Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050342343","display_name":"Devi Parikh","orcid":"https://orcid.org/0000-0002-3779-6706","observed_orcids":["https://orcid.org/0000-0002-3779-6706"]},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]},{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Devi Parikh","raw_affiliation_strings":["Facebook AI Research","Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Facebook AI Research","institution_ids":["https://openalex.org/I4210114444"]},{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5014035752","display_name":"Dhruv Batra","orcid":"https://orcid.org/0000-0002-1358-0011","observed_orcids":["https://orcid.org/0000-0002-1358-0011"]},"institutions":[{"id":"https://openalex.org/I130701444","display_name":"Georgia Institute of Technology","ror":"https://ror.org/01zkghx44","country_code":"US","type":"education","lineage":["https://openalex.org/I130701444"]},{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dhruv Batra","raw_affiliation_strings":["Facebook AI Research","Georgia Institute of Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Facebook AI Research","institution_ids":["https://openalex.org/I4210114444"]},{"raw_affiliation_string":"Georgia Institute of Technology","institution_ids":["https://openalex.org/I130701444"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":264.7099,"has_fulltext":false,"cited_by_count":24804,"citation_normalized_percentile":{"value":0.9999637,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"618","last_page":"626"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9980000257492065,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.0013000000035390258,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.00019999999494757503,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/grad-cam","display_name":"Grad-CAM","score":0.9929999709129333},{"id":"https://openalex.org/keywords/visual-explanations","display_name":"visual explanations","score":0.7870000004768372},{"id":"https://openalex.org/keywords/class-activation-mapping","display_name":"class activation mapping","score":0.7950000166893005},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.8040000200271606},{"id":"https://openalex.org/keywords/explainable-ai","display_name":"explainable AI","score":0.7409999966621399},{"id":"https://openalex.org/keywords/image-classification","display_name":"image classification","score":0.6660000085830688},{"id":"https://openalex.org/keywords/image-captioning","display_name":"image captioning","score":0.9879999756813049},{"id":"https://openalex.org/keywords/visual-question-answering","display_name":"visual question answering","score":1.0},{"id":"https://openalex.org/keywords/weakly-supervised-localization","display_name":"weakly supervised localization","score":0.8949999809265137},{"id":"https://openalex.org/keywords/dataset-bias","display_name":"data bias","score":0.7120000123977661},{"id":"https://openalex.org/keywords/human-subjects-research","display_name":"human subjects research","score":0.3059999942779541}],"concepts":[{"id":"https://openalex.org/C157657479","wikidata":"https://www.wikidata.org/wiki/Q2367247","display_name":"Closed captioning","level":3,"score":0.9521337747573853},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8256241083145142},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7431842088699341},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7098740339279175},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6668828725814819},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.6584084630012512},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5860918760299683},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.5738685131072998},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5616480112075806},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5176787376403809},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.48347049951553345},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4703029990196228},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4666447937488556},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.46557992696762085},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43765756487846375},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccv.2017.74","doi":"https://doi.org/10.1109/iccv.2017.74","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv.2017.74","pdf_url":null,"source":{"id":"https://openalex.org/S4363607764","display_name":"IEEE/CVF International Conference on Computer Vision (ICCV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"},{"id":"https://openalex.org/F4320306216","display_name":"Paul G. Allen Family Foundation","ror":"https://ror.org/01degd278"},{"id":"https://openalex.org/F4320310812","display_name":"Allen Foundation","ror":"https://ror.org/036jq7b41"},{"id":"https://openalex.org/F4320337345","display_name":"Office of Naval Research","ror":"https://ror.org/00rk2pe57"},{"id":"https://openalex.org/F4320338281","display_name":"Army Research Office","ror":"https://ror.org/05epdh915"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W1503233963","https://openalex.org/W1575833922","https://openalex.org/W1586079445","https://openalex.org/W1686810756","https://openalex.org/W1832500336","https://openalex.org/W1849277567","https://openalex.org/W1889081078","https://openalex.org/W1895577753","https://openalex.org/W1900913856","https://openalex.org/W1903029394","https://openalex.org/W1905882502","https://openalex.org/W1931639407","https://openalex.org/W1933349210","https://openalex.org/W1941636933","https://openalex.org/W1945608308","https://openalex.org/W1965482737","https://openalex.org/W1982428585","https://openalex.org/W1994488211","https://openalex.org/W2102605133","https://openalex.org/W2108598243","https://openalex.org/W2123045220","https://openalex.org/W2133324800","https://openalex.org/W2142192571","https://openalex.org/W2155893237","https://openalex.org/W2161381512","https://openalex.org/W2163605009","https://openalex.org/W2163922914","https://openalex.org/W2169393322","https://openalex.org/W2189070436","https://openalex.org/W2190008860","https://openalex.org/W2194775991","https://openalex.org/W2257979135","https://openalex.org/W2273348943","https://openalex.org/W2282821441","https://openalex.org/W2295107390","https://openalex.org/W2424413254","https://openalex.org/W2439568532","https://openalex.org/W2463267937","https://openalex.org/W2463565445","https://openalex.org/W2503388974","https://openalex.org/W2518775244","https://openalex.org/W2776207810","https://openalex.org/W2963668159","https://openalex.org/W2963733622","https://openalex.org/W2963758027","https://openalex.org/W2963911037","https://openalex.org/W3145057472","https://openalex.org/W4239072543","https://openalex.org/W6638444622"],"related_works":["https://openalex.org/W2530010084","https://openalex.org/W2549418531","https://openalex.org/W2765793020","https://openalex.org/W3010219363","https://openalex.org/W2965696320","https://openalex.org/W3127066701","https://openalex.org/W3132394427","https://openalex.org/W3121469726","https://openalex.org/W4293464704","https://openalex.org/W7131427089"],"abstract_inverted_index":{"We":[0,119],"propose":[1],"a":[2,11,45,55,78,128,251,256,274],"technique":[3],"for":[4,8,41,66,94],"producing":[5],"`visual":[6],"explanations'":[7],"decisions":[9],"from":[10,239,255],"large":[12],"class":[13],"of":[14,35,81,154,166],"Convolutional":[15],"Neural":[16],"Network":[17],"(CNN)-based":[18],"models,":[19,147,157],"making":[20],"them":[21],"more":[22,189],"transparent.":[23],"Our":[24,265],"approach":[25],"-":[26],"Gradient-weighted":[27],"Class":[28],"Activation":[29],"Mapping":[30],"(Grad-CAM),":[31],"uses":[32],"the":[33,49,60,64,68,152,182,192],"gradients":[34],"any":[36],"target":[37],"concept":[38],"(say":[39],"logits":[40],"`dog'":[42],"or":[43,111,117],"even":[44,213,259],"caption),":[46],"flowing":[47],"into":[48,163],"final":[50],"convolutional":[51],"layer":[52],"to":[53,77,126,137,191,227],"produce":[54],"coarse":[56],"localization":[57,185],"map":[58],"highlighting":[59],"important":[61],"regions":[62],"in":[63,102,237],"image":[65,138,140,155,206],"predicting":[67],"concept.":[69],"Unlike":[70],"previous":[71,179],"approaches,":[72],"Grad-":[73],"CAM":[74],"is":[75,267],"applicable":[76],"wide":[79],"variety":[80],"CNN":[82],"model-families:":[83],"(1)":[84],"CNNs":[85,92,100],"with":[86,104,122,273],"fully-connected":[87],"layers":[88],"(e.g.":[89,97,107],"VGG),":[90],"(2)":[91],"used":[93,101],"structured":[95],"outputs":[96],"captioning),":[98],"(3)":[99],"tasks":[103],"multi-modal":[105],"inputs":[106],"visual":[108,143],"question":[109,144],"answering)":[110],"reinforcement":[112],"learning,":[113],"without":[114],"architectural":[115],"changes":[116],"re-training.":[118],"combine":[120],"Grad-CAM":[121,230,245],"existing":[123],"fine-grained":[124],"visualizations":[125,159,211],"create":[127],"high-resolution":[129],"class-discriminative":[130],"visualization,":[131],"Guided":[132],"Grad-CAM,":[133],"and":[134,142,195,208,223,242,279],"apply":[135],"it":[136],"classification,":[139],"captioning,":[141],"answering":[145],"(VQA)":[146],"including":[148],"ResNet-based":[149],"architectures.":[150],"In":[151],"context":[153],"classification":[156],"our":[158,210],"(a)":[160],"lend":[161],"insights":[162],"failure":[164],"modes":[165],"these":[167],"models":[168,216],"(showing":[169],"that":[170,244],"seemingly":[171],"unreasonable":[172],"predictions":[173,238],"have":[174],"reasonable":[175],"explanations),":[176],"(b)":[177],"outperform":[178],"methods":[180],"on":[181,276],"ILSVRC-15":[183],"weakly-supervised":[184],"task,":[186],"(c)":[187],"are":[188],"faithful":[190],"underlying":[193],"model,":[194],"(d)":[196],"help":[197,232],"achieve":[198],"model":[199],"generalization":[200],"by":[201],"identifying":[202],"dataset":[203],"bias.":[204],"For":[205],"captioning":[207],"VQA,":[209],"show":[212,243],"non-attention":[214],"based":[215],"can":[217],"localize":[218],"inputs.":[219],"Finally,":[220],"we":[221],"design":[222],"conduct":[224],"human":[225],"studies":[226],"measure":[228],"if":[229],"explanations":[231],"users":[233,248],"establish":[234],"appropriate":[235],"trust":[236],"deep":[240,253],"networks":[241],"helps":[246],"untrained":[247],"successfully":[249],"discern":[250],"`stronger'":[252],"network":[254],"`weaker'":[257],"one":[258],"when":[260],"both":[261],"make":[262],"identical":[263],"predictions.":[264],"code":[266],"available":[268],"at":[269,281],"https:":[270],"//github.com/ramprs/grad-cam/":[271],"along":[272],"demo":[275],"CloudCV":[277],"[2]":[278],"video":[280],"youtu.be/COjUB9Izk6E.":[282]},"counts_by_year":[{"year":2026,"cited_by_count":4192},{"year":2025,"cited_by_count":4911},{"year":2024,"cited_by_count":4369},{"year":2023,"cited_by_count":3656},{"year":2022,"cited_by_count":2863},{"year":2021,"cited_by_count":2442},{"year":2020,"cited_by_count":1413},{"year":2019,"cited_by_count":721},{"year":2018,"cited_by_count":208},{"year":2017,"cited_by_count":19},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-11-06T12:05:46.655746","created_date":"2019-07-30T00:00:00"}