{"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/pythia-v01-the-winning-entry-to-the-vqa","title":"Pythia v0.1: the Winning Entry to the VQA Challenge 2018","arxiv_id":"1807.09956","date":"2018-07-26","proceeding":null,"authors":["Yu Jiang","Vivek Natarajan","Xinlei Chen","Marcus Rohrbach","Dhruv Batra","Devi Parikh"],"abstract":"This document describes Pythia v0.1, the winning entry from Facebook AI\nResearch (FAIR)'s A-STAR team to the VQA Challenge 2018.\n  Our starting point is a modular re-implementation of the bottom-up top-down\n(up-down) model. We demonstrate that by making subtle but important changes to\nthe model architecture and the learning rate schedule, fine-tuning image\nfeatures, and adding data augmentation, we can significantly improve the\nperformance of the up-down model on VQA v2.0 dataset -- from 65.67% to 70.22%.\n  Furthermore, by using a diverse ensemble of models trained with different\nfeatures and on different datasets, we are able to significantly improve over\nthe 'standard' way of ensembling (i.e. same model with different random seeds)\nby 1.31%. Overall, we achieve 72.27% on the test-std split of the VQA v2.0\ndataset. Our code in its entirety (training, evaluation, data-augmentation,\nensembling) and pre-trained models are publicly available at:\nhttps://github.com/facebookresearch/pythia","url_abs":"http://arxiv.org/abs/1807.09956v2","url_pdf":"http://arxiv.org/pdf/1807.09956v2.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":"pythia-v01-the-winning-entry-to-the-vqa","repo_url":"https://github.com/facebookresearch/pythia","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"pythia-v01-the-winning-entry-to-the-vqa","repo_url":"https://github.com/ZephyrZhuQi/ssbaseline","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"pythia-v01-the-winning-entry-to-the-vqa","repo_url":"https://github.com/allenai/pythia","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"pythia-v01-the-winning-entry-to-the-vqa","repo_url":"https://github.com/facebookresearch/mmf","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"pythia-v01-the-winning-entry-to-the-vqa","repo_url":"https://github.com/gabegrand/adversarial-vqa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"pythia-v01-the-winning-entry-to-the-vqa","repo_url":"https://github.com/jackroos/pythia","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"pythia-v01-the-winning-entry-to-the-vqa","repo_url":"https://github.com/ronghanghu/pythia","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"pythia-v01-the-winning-entry-to-the-vqa","repo_url":"https://github.com/songhe17/pythia-clone","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"pythia-v01-the-winning-entry-to-the-vqa","repo_url":"https://github.com/zwxalgorithm/pythia","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"visual-question-answering","task_name":"Visual Question Answering (VQA)"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-question-answering-on-a-okvqa","task":"Visual Question Answering (VQA)","dataset":"A-OKVQA","model":"Pythia","rank_in_archive_order":12,"of":15,"metrics":{"DA VQA Score":"21.9","MC Accuracy":"40.1"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1807.09956","atlas_url":"https://app.syntology.ai/?focus=1807.09956","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.09956"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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. 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