{"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/transformer-based-multi-modal-proposal-and-re","title":"Transformer-Based Multi-modal Proposal and Re-Rank for Wikipedia Image-Caption Matching","arxiv_id":"2206.10436","date":"2022-06-21","proceeding":null,"authors":["Nicola Messina","Davide Alessandro Coccomini","Andrea Esuli","Fabrizio Falchi"],"abstract":"With the increased accessibility of web and online encyclopedias, the amount of data to manage is constantly increasing. In Wikipedia, for example, there are millions of pages written in multiple languages. These pages contain images that often lack the textual context, remaining conceptually floating and therefore harder to find and manage. In this work, we present the system we designed for participating in the Wikipedia Image-Caption Matching challenge on Kaggle, whose objective is to use data associated with images (URLs and visual data) to find the correct caption among a large pool of available ones. A system able to perform this task would improve the accessibility and completeness of multimedia content on large online encyclopedias. Specifically, we propose a cascade of two models, both powered by the recent Transformer model, able to efficiently and effectively infer a relevance score between the query image data and the captions. We verify through extensive experimentation that the proposed two-model approach is an effective way to handle a large pool of images and captions while maintaining bounded the overall computational complexity at inference time. Our approach achieves remarkable results, obtaining a normalized Discounted Cumulative Gain (nDCG) value of 0.53 on the private leaderboard of the Kaggle challenge.","url_abs":"https://arxiv.org/abs/2206.10436v1","url_pdf":"https://arxiv.org/pdf/2206.10436v1.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":"transformer-based-multi-modal-proposal-and-re","repo_url":"https://github.com/mesnico/wiki-image-caption-matching","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"transformer-based-multi-modal-proposal-and-re","repo_url":"https://github.com/towhee-io/towhee","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2206.10436","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2206.10436"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/mesnico/wiki-image-caption-matching","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/towhee-io/towhee","reach":null}],"summary":{"ran":4,"ran_draft_wrong":1,"unverified":1},"by_repo_kind":{"listed":{"samples":6,"ran":5,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"e8e30cc7f67b29f5","entry":"TransformerDecoder","repo":"towhee-io/towhee","repo_kind":"listed","path":"towhee/models/coformer/transformer.py","file_url":"https://github.com/towhee-io/towhee/blob/HEAD/towhee/models/coformer/transformer.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"e8e30cc7f67b29f5"}},{"code_sha256_prefix":"1b28a7326df88e10","entry":"TransformerDecoderLayer","repo":"towhee-io/towhee","repo_kind":"listed","path":"towhee/models/coformer/transformer.py","file_url":"https://github.com/towhee-io/towhee/blob/HEAD/towhee/models/coformer/transformer.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"1b28a7326df88e10"}},{"code_sha256_prefix":"bdcda6c39e35013d","entry":"TransformerEncoder","repo":"towhee-io/towhee","repo_kind":"listed","path":"towhee/models/coformer/transformer.py","file_url":"https://github.com/towhee-io/towhee/blob/HEAD/towhee/models/coformer/transformer.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"bdcda6c39e35013d"}},{"code_sha256_prefix":"5f606c3ea722baea","entry":"TransformerEncoderLayer","repo":"towhee-io/towhee","repo_kind":"listed","path":"towhee/models/coformer/transformer.py","file_url":"https://github.com/towhee-io/towhee/blob/HEAD/towhee/models/coformer/transformer.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"5f606c3ea722baea"}},{"code_sha256_prefix":"695bfb488e490407","entry":"_get_clones","repo":"towhee-io/towhee","repo_kind":"listed","path":"towhee/models/coformer/transformer.py","file_url":"https://github.com/towhee-io/towhee/blob/HEAD/towhee/models/coformer/transformer.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"695bfb488e490407"}},{"code_sha256_prefix":"12a4f89ae433d0da","entry":"Transformer","repo":"towhee-io/towhee","repo_kind":"listed","path":"towhee/models/coformer/transformer.py","file_url":"https://github.com/towhee-io/towhee/blob/HEAD/towhee/models/coformer/transformer.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"12a4f89ae433d0da"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}