{"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/dae-gan-dynamic-aspect-aware-gan-for-text-to","title":"DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis","arxiv_id":"2108.12141","date":"2021-08-27","proceeding":"ICCV 2021 10","authors":["Shulan Ruan","Yong Zhang","Kun Zhang","Yanbo Fan","Fan Tang","Qi Liu","Enhong Chen"],"abstract":"Text-to-image synthesis refers to generating an image from a given text description, the key goal of which lies in photo realism and semantic consistency. Previous methods usually generate an initial image with sentence embedding and then refine it with fine-grained word embedding. Despite the significant progress, the 'aspect' information (e.g., red eyes) contained in the text, referring to several words rather than a word that depicts 'a particular part or feature of something', is often ignored, which is highly helpful for synthesizing image details. How to make better utilization of aspect information in text-to-image synthesis still remains an unresolved challenge. To address this problem, in this paper, we propose a Dynamic Aspect-awarE GAN (DAE-GAN) that represents text information comprehensively from multiple granularities, including sentence-level, word-level, and aspect-level. Moreover, inspired by human learning behaviors, we develop a novel Aspect-aware Dynamic Re-drawer (ADR) for image refinement, in which an Attended Global Refinement (AGR) module and an Aspect-aware Local Refinement (ALR) module are alternately employed. AGR utilizes word-level embedding to globally enhance the previously generated image, while ALR dynamically employs aspect-level embedding to refine image details from a local perspective. Finally, a corresponding matching loss function is designed to ensure the text-image semantic consistency at different levels. Extensive experiments on two well-studied and publicly available datasets (i.e., CUB-200 and COCO) demonstrate the superiority and rationality of our method.","url_abs":"https://arxiv.org/abs/2108.12141v1","url_pdf":"https://arxiv.org/pdf/2108.12141v1.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":"dae-gan-dynamic-aspect-aware-gan-for-text-to","repo_url":"https://github.com/hiarsal/dae-gan","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"sentence-embedding","task_name":"Sentence Embedding"},{"task_slug":"sentence-embedding-1","task_name":"Sentence-Embedding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2108.12141","atlas_url":"https://app.syntology.ai/?focus=2108.12141","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2108.12141"}},"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. 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/hiarsal/dae-gan","reach":null},{"provenance":"deterministic:regex_extraction","url":"https://github.com/hiarsal/DAE-GAN","reach":null}],"summary":{"ran":2,"ran_draft_wrong":2,"unverified":3},"by_repo_kind":{"official":{"samples":7,"ran":4,"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":7,"samples":[{"code_sha256_prefix":"77718674e841bb8c","entry":"GET_IMAGE_G","repo":"hiarsal/DAE-GAN","repo_kind":"official","path":"code/model.py","file_url":"https://github.com/hiarsal/DAE-GAN/blob/HEAD/code/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"77718674e841bb8c"}},{"code_sha256_prefix":"bbebf7ea44c0927f","entry":"ResBlock","repo":"hiarsal/DAE-GAN","repo_kind":"official","path":"code/model.py","file_url":"https://github.com/hiarsal/DAE-GAN/blob/HEAD/code/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"bbebf7ea44c0927f"}},{"code_sha256_prefix":"d33e18153be6aeed","entry":"conv3x3","repo":"hiarsal/DAE-GAN","repo_kind":"official","path":"code/model.py","file_url":"https://github.com/hiarsal/DAE-GAN/blob/HEAD/code/model.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"d33e18153be6aeed"}},{"code_sha256_prefix":"2eb31309daac5776","entry":"upBlock","repo":"hiarsal/DAE-GAN","repo_kind":"official","path":"code/model.py","file_url":"https://github.com/hiarsal/DAE-GAN/blob/HEAD/code/model.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"2eb31309daac5776"}},{"code_sha256_prefix":"6ffac4c84d7efa3d","entry":"G_NET","repo":"hiarsal/DAE-GAN","repo_kind":"official","path":"code/model.py","file_url":"https://github.com/hiarsal/DAE-GAN/blob/HEAD/code/model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"6ffac4c84d7efa3d"}},{"code_sha256_prefix":"cc9db9ff8ee98c63","entry":"INIT_STAGE_G","repo":"hiarsal/DAE-GAN","repo_kind":"official","path":"code/model.py","file_url":"https://github.com/hiarsal/DAE-GAN/blob/HEAD/code/model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"cc9db9ff8ee98c63"}},{"code_sha256_prefix":"a6c8097f7506949c","entry":"NEXT_STAGE_G","repo":"hiarsal/DAE-GAN","repo_kind":"official","path":"code/model.py","file_url":"https://github.com/hiarsal/DAE-GAN/blob/HEAD/code/model.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"a6c8097f7506949c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}