{"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/pixmim-rethinking-pixel-reconstruction-in","title":"PixMIM: Rethinking Pixel Reconstruction in Masked Image Modeling","arxiv_id":"2303.02416","date":"2023-03-04","proceeding":null,"authors":["YuAn Liu","Songyang Zhang","Jiacheng Chen","Kai Chen","Dahua Lin"],"abstract":"Masked Image Modeling (MIM) has achieved promising progress with the advent of Masked Autoencoders (MAE) and BEiT. However, subsequent works have complicated the framework with new auxiliary tasks or extra pre-trained models, inevitably increasing computational overhead. This paper undertakes a fundamental analysis of MIM from the perspective of pixel reconstruction, which examines the input image patches and reconstruction target, and highlights two critical but previously overlooked bottlenecks. Based on this analysis, we propose a remarkably simple and effective method, {\\ourmethod}, that entails two strategies: 1) filtering the high-frequency components from the reconstruction target to de-emphasize the network's focus on texture-rich details and 2) adopting a conservative data transform strategy to alleviate the problem of missing foreground in MIM training. {\\ourmethod} can be easily integrated into most existing pixel-based MIM approaches (\\ie, using raw images as reconstruction target) with negligible additional computation. Without bells and whistles, our method consistently improves three MIM approaches, MAE, ConvMAE, and LSMAE, across various downstream tasks. We believe this effective plug-and-play method will serve as a strong baseline for self-supervised learning and provide insights for future improvements of the MIM framework. Code and models are available at \\url{https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/pixmim}.","url_abs":"https://arxiv.org/abs/2303.02416v2","url_pdf":"https://arxiv.org/pdf/2303.02416v2.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":"pixmim-rethinking-pixel-reconstruction-in","repo_url":"https://github.com/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/pixmim","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"}],"methods":[{"method_slug":"mae","method_name":"MAE"},{"method_slug":"mim","method_name":"MIM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2303.02416","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.02416"}},"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/open-mmlab/mmselfsup/tree/dev-1.x/configs/selfsup/pixmim","reach":null}],"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":"0a5a9a399ebed617","entry":"get_layer_id_for_mixmim","repo":"open-mmlab/mmselfsup","repo_kind":"official","path":"mmselfsup/engine/optimizers/layer_decay_optim_wrapper_constructor.py","file_url":"https://github.com/open-mmlab/mmselfsup/blob/HEAD/mmselfsup/engine/optimizers/layer_decay_optim_wrapper_constructor.py","link_basis":"harvester_set","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":"0a5a9a399ebed617"}},{"code_sha256_prefix":"b1b070deaf6df7cd","entry":"get_layer_id_for_swin","repo":"open-mmlab/mmselfsup","repo_kind":"official","path":"mmselfsup/engine/optimizers/layer_decay_optim_wrapper_constructor.py","file_url":"https://github.com/open-mmlab/mmselfsup/blob/HEAD/mmselfsup/engine/optimizers/layer_decay_optim_wrapper_constructor.py","link_basis":"harvester_set","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":"b1b070deaf6df7cd"}},{"code_sha256_prefix":"ce4ee2e9f80b87bf","entry":"get_layer_id_for_vit","repo":"open-mmlab/mmselfsup","repo_kind":"official","path":"mmselfsup/engine/optimizers/layer_decay_optim_wrapper_constructor.py","file_url":"https://github.com/open-mmlab/mmselfsup/blob/HEAD/mmselfsup/engine/optimizers/layer_decay_optim_wrapper_constructor.py","link_basis":"harvester_set","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":"ce4ee2e9f80b87bf"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}