{"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/learning-conditioned-graph-structures-for","title":"Learning Conditioned Graph Structures for Interpretable Visual Question Answering","arxiv_id":"1806.07243","date":"2018-06-19","proceeding":"NeurIPS 2018 12","authors":["Will Norcliffe-Brown","Efstathios Vafeias","Sarah Parisot"],"abstract":"Visual Question answering is a challenging problem requiring a combination of\nconcepts from Computer Vision and Natural Language Processing. Most existing\napproaches use a two streams strategy, computing image and question features\nthat are consequently merged using a variety of techniques. Nonetheless, very\nfew rely on higher level image representations, which can capture semantic and\nspatial relationships. In this paper, we propose a novel graph-based approach\nfor Visual Question Answering. Our method combines a graph learner module,\nwhich learns a question specific graph representation of the input image, with\nthe recent concept of graph convolutions, aiming to learn image representations\nthat capture question specific interactions. We test our approach on the VQA v2\ndataset using a simple baseline architecture enhanced by the proposed graph\nlearner module. We obtain promising results with 66.18% accuracy and\ndemonstrate the interpretability of the proposed method. Code can be found at\ngithub.com/aimbrain/vqa-project.","url_abs":"http://arxiv.org/abs/1806.07243v6","url_pdf":"http://arxiv.org/pdf/1806.07243v6.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":"learning-conditioned-graph-structures-for","repo_url":"https://github.com/aimbrain/vqa-project","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"question-answering","task_name":"Question Answering"},{"task_slug":"visual-question-answering-1","task_name":"Visual Question Answering"},{"task_slug":"visual-question-answering","task_name":"Visual Question Answering (VQA)"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.07243","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.07243"}},"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/aimbrain/vqa-project","reach":{"status":"ok","spdx":"Apache-2.0"}}],"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":"736573dfab51b4df","entry":"batch_to_cuda","repo":"aimbrain/vqa-project","repo_kind":"official","path":"utils.py","file_url":"https://github.com/aimbrain/vqa-project/blob/HEAD/utils.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":"736573dfab51b4df"}},{"code_sha256_prefix":"88f937451ae9b61b","entry":"collate_fn","repo":"aimbrain/vqa-project","repo_kind":"official","path":"torch_dataset.py","file_url":"https://github.com/aimbrain/vqa-project/blob/HEAD/torch_dataset.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":"88f937451ae9b61b"}},{"code_sha256_prefix":"0436718db3a26f78","entry":"total_vqa_score","repo":"aimbrain/vqa-project","repo_kind":"official","path":"utils.py","file_url":"https://github.com/aimbrain/vqa-project/blob/HEAD/utils.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":"0436718db3a26f78"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}