Improved IEC Performance via Emotional Stimuli-Aware Captioning

Improved IEC Performance via Emotional Stimuli-Aware Captioning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Improved IEC Performance via Emotional Stimuli-Aware Captioning Zibo Zhou, Zhengjun Zhai, Xin Gao, Jiaqi Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6231128/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Image emotion classification (IEC), a crucial task in computer vision, aims to infer the emotional state of subjects in images. Existing techniques have focused on the use of semantic information to support visual features. However, a significant affective gap persists between low-level pixel information and high-level emotions, due to the abstract and complex nature of cognitive processes. This gap limits corresponding semantic representations and hinders the resulting model performance. In this study, we draw inspiration from psychological findings and advances in natural language processing. Specifically, we explore the use of image captions as auxiliary information, combined with visual features, for enhanced emotional discernment. We introduce the emotional stimuli-aware captioning network (ESCNet), which leverages generative captions to augment visual representations. An affective captioning dataset, based on emotional attributes, is also developed to generate emotion-related captions and pre-train the image captioning model. Visual features related to the captions are then generated to highlight emotionally charged words and a fusion module combining cross-attention with self-attention is introduced to learn correlations between images and captions. We also introduce a variable-weight loss function to emphasize hard-to-classify samples. Extensive validation experiments using multiple public datasets demonstrated that our approach outperformed state-of-the-art models. Ablation studies and visualization results further confirmed the effectiveness of our proposed network and its modules. Biological sciences/Psychology Physical sciences/Mathematics and computing/Computer science Image emotion classification Image caption Visual attention Semantic attention Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 16 Apr, 2025 Reviews received at journal 13 Apr, 2025 Reviewers agreed at journal 08 Apr, 2025 Reviews received at journal 08 Apr, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers invited by journal 28 Mar, 2025 Editor assigned by journal 28 Mar, 2025 Editor invited by journal 25 Mar, 2025 Submission checks completed at journal 22 Mar, 2025 First submitted to journal 15 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6231128","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":444016060,"identity":"b83216f6-8881-4744-9cb9-79a25c158cbc","order_by":0,"name":"Zibo Zhou","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Zibo","middleName":"","lastName":"Zhou","suffix":""},{"id":444016061,"identity":"2e308cdf-4f16-4dd0-8d67-abb682fe1310","order_by":1,"name":"Zhengjun Zhai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYDACCRDBA2EcSKiokWNjbz9AgpYHZ44Z8/GcSSBCC5TB+LCNOXGehIMBXh3ys5uPPfwiczgPyHh4IIGNLb1NgiGB4UfFNpxaGOccSzeW4TlcDGQYHEjgkcltk248wNhz5jZOLcwSOWbSEjyHE5slEoBaJNhy22QOJDAztuHWwiaR/w2spU0i/cOBBAPmdDagXrxaeCRy2CQ/ALX0SOQAbUlgTiCoRUIizUyagSc9cYZETsGBhAPHDNuAgXwQn1/kZyQ/k/zZY504f0b65o8//9XIy7e3H3zwowK3FnAQ8PagiRzAqx4IGH/8IKRkFIyCUTAKRjQAAJAUWI2oofE3AAAAAElFTkSuQmCC","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":true,"prefix":"","firstName":"Zhengjun","middleName":"","lastName":"Zhai","suffix":""},{"id":444016062,"identity":"2d304e5d-3544-4d79-a387-352952d65f7e","order_by":2,"name":"Xin Gao","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Gao","suffix":""},{"id":444016063,"identity":"3f5613b6-82ae-4bdc-b0fa-0aeae126fa84","order_by":3,"name":"Jiaqi Zhu","email":"","orcid":"","institution":"Northwestern Polytechnical University","correspondingAuthor":false,"prefix":"","firstName":"Jiaqi","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-03-15 07:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6231128/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6231128/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-06094-7","type":"published","date":"2025-07-01T15:58:03+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86180970,"identity":"0dc49cd9-76a6-4049-8cc1-451d818e06dd","added_by":"auto","created_at":"2025-07-07 16:23:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7072329,"visible":true,"origin":"","legend":"","description":"","filename":"AI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6231128/v1_covered_fe174b1f-74b1-4433-bc94-9d7e3cea2281.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improved IEC Performance via Emotional Stimuli-Aware Captioning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Image emotion classification, Image caption, Visual attention, Semantic attention","lastPublishedDoi":"10.21203/rs.3.rs-6231128/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6231128/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eImage emotion classification (IEC), a crucial task in computer vision, aims to infer the emotional state of subjects in images. Existing techniques have focused on the use of semantic information to support visual features. However, a significant affective gap persists between low-level pixel information and high-level emotions, due to the abstract and complex nature of cognitive processes. This gap limits corresponding semantic representations and hinders the resulting model performance. In this study, we draw inspiration from psychological findings and advances in natural language processing. Specifically, we explore the use of image captions as auxiliary information, combined with visual features, for enhanced emotional discernment. We introduce the emotional stimuli-aware captioning network (ESCNet), which leverages generative captions to augment visual representations. An affective captioning dataset, based on emotional attributes, is also developed to generate emotion-related captions and pre-train the image captioning model. Visual features related to the captions are then generated to highlight emotionally charged words and a fusion module combining cross-attention with self-attention is introduced to learn correlations between images and captions. We also introduce a variable-weight loss function to emphasize hard-to-classify samples. Extensive validation experiments using multiple public datasets demonstrated that our approach outperformed state-of-the-art models. Ablation studies and visualization results further confirmed the effectiveness of our proposed network and its modules.\u003c/p\u003e","manuscriptTitle":"Improved IEC Performance via Emotional Stimuli-Aware Captioning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 09:29:47","doi":"10.21203/rs.3.rs-6231128/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-16T19:17:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-13T07:01:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"247130046706826810781542515216350871536","date":"2025-04-08T12:49:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-08T07:33:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100638237745277473972399882478504916849","date":"2025-03-28T19:56:08+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-28T19:17:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-28T19:10:03+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-03-25T19:05:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-22T04:34:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-15T07:17:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"63f1e810-0ab9-4dfd-b6ef-c6af1cff2e62","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47275053,"name":"Biological sciences/Psychology"},{"id":47275054,"name":"Physical sciences/Mathematics and computing/Computer science"}],"tags":[],"updatedAt":"2025-07-07T16:19:46+00:00","versionOfRecord":{"articleIdentity":"rs-6231128","link":"https://doi.org/10.1038/s41598-025-06094-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-01 15:58:03","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2025-04-17 09:29:47","video":"","vorDoi":"10.1038/s41598-025-06094-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-06094-7","workflowStages":[]},"version":"v1","identity":"rs-6231128","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6231128","identity":"rs-6231128","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}