{"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/no-metrics-are-perfect-adversarial-reward","title":"No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling","arxiv_id":"1804.09160","date":"2018-04-24","proceeding":"ACL 2018 7","authors":["Xin Wang","Wenhu Chen","Yuan-Fang Wang","William Yang Wang"],"abstract":"Though impressive results have been achieved in visual captioning, the task\nof generating abstract stories from photo streams is still a little-tapped\nproblem. Different from captions, stories have more expressive language styles\nand contain many imaginary concepts that do not appear in the images. Thus it\nposes challenges to behavioral cloning algorithms. Furthermore, due to the\nlimitations of automatic metrics on evaluating story quality, reinforcement\nlearning methods with hand-crafted rewards also face difficulties in gaining an\noverall performance boost. Therefore, we propose an Adversarial REward Learning\n(AREL) framework to learn an implicit reward function from human\ndemonstrations, and then optimize policy search with the learned reward\nfunction. Though automatic eval- uation indicates slight performance boost over\nstate-of-the-art (SOTA) methods in cloning expert behaviors, human evaluation\nshows that our approach achieves significant improvement in generating more\nhuman-like stories than SOTA systems.","url_abs":"http://arxiv.org/abs/1804.09160v2","url_pdf":"http://arxiv.org/pdf/1804.09160v2.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":"no-metrics-are-perfect-adversarial-reward","repo_url":"https://github.com/littlekobe/AREL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"no-metrics-are-perfect-adversarial-reward","repo_url":"https://github.com/eric-xw/AREL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"image-captioning","task_name":"Image Captioning"},{"task_slug":"reinforcement-learning","task_name":"Reinforcement Learning"},{"task_slug":"visual-storytelling","task_name":"Visual Storytelling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/visual-storytelling-on-vist","task":"Visual Storytelling","dataset":"VIST","model":"AREL-t-100","rank_in_archive_order":13,"of":33,"metrics":{"BLEU-1":"63.8","BLEU-2":"39.1","BLEU-3":"23.2","BLEU-4":"14.1","CIDEr":"9.4","METEOR":"35","ROUGE-L":"29.5"},"uses_additional_data":false},{"leaderboard":"/sota/visual-storytelling-on-vist","task":"Visual Storytelling","dataset":"VIST","model":"GAN","rank_in_archive_order":16,"of":33,"metrics":{"BLEU-1":"62.8","BLEU-2":"38.8","BLEU-3":"23.0","BLEU-4":"14","CIDEr":"9","METEOR":"35","ROUGE-L":"29.5"},"uses_additional_data":false},{"leaderboard":"/sota/visual-storytelling-on-vist","task":"Visual Storytelling","dataset":"VIST","model":"XE-ss","rank_in_archive_order":17,"of":33,"metrics":{"BLEU-1":"62.3","BLEU-2":"38.2","BLEU-3":"22.5","BLEU-4":"13.7","CIDEr":"8.7","METEOR":"34.8","ROUGE-L":"29.7"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1804.09160","atlas_url":"https://app.syntology.ai/?focus=1804.09160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.09160"}},"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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