{"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/resolving-references-to-objects-in","title":"Resolving References to Objects in Photographs using the Words-As-Classifiers Model","arxiv_id":"1510.02125","date":"2015-10-07","proceeding":"ACL 2016 8","authors":["David Schlangen","Sina Zarriess","Casey Kennington"],"abstract":"A common use of language is to refer to visually present objects. Modelling\nit in computers requires modelling the link between language and perception.\nThe \"words as classifiers\" model of grounded semantics views words as\nclassifiers of perceptual contexts, and composes the meaning of a phrase\nthrough composition of the denotations of its component words. It was recently\nshown to perform well in a game-playing scenario with a small number of object\ntypes. We apply it to two large sets of real-world photographs that contain a\nmuch larger variety of types and for which referring expressions are available.\nUsing a pre-trained convolutional neural network to extract image features, and\naugmenting these with in-picture positional information, we show that the model\nachieves performance competitive with the state of the art in a reference\nresolution task (given expression, find bounding box of its referent), while,\nas we argue, being conceptually simpler and more flexible.","url_abs":"http://arxiv.org/abs/1510.02125v3","url_pdf":"http://arxiv.org/pdf/1510.02125v3.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":"resolving-references-to-objects-in","repo_url":"https://github.com/dsg-bielefeld/image_wac","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1510.02125","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1510.02125"}},"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/dsg-bielefeld/image_wac","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"summary":{"unverified":3},"by_repo_kind":{"official":{"samples":3,"ran":0,"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":"2337c1f8731c4a35","entry":"apply_iou_to_refdf_row","repo":"dsg-bielefeld/image_wac","repo_kind":"official","path":"ApplyModels/apply_model.py","file_url":"https://github.com/dsg-bielefeld/image_wac/blob/HEAD/ApplyModels/apply_model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"2337c1f8731c4a35"}},{"code_sha256_prefix":"1a73cf4b7410e871","entry":"get_gold_bbs","repo":"dsg-bielefeld/image_wac","repo_kind":"official","path":"ApplyModels/apply_model.py","file_url":"https://github.com/dsg-bielefeld/image_wac/blob/HEAD/ApplyModels/apply_model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"1a73cf4b7410e871"}},{"code_sha256_prefix":"004b9432a13530e0","entry":"get_rprp_bbs","repo":"dsg-bielefeld/image_wac","repo_kind":"official","path":"ApplyModels/apply_model.py","file_url":"https://github.com/dsg-bielefeld/image_wac/blob/HEAD/ApplyModels/apply_model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"mcp_get_code":{"code_sha256":"004b9432a13530e0"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}