{"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/demt-deformable-mixer-transformer-for-multi","title":"DeMT: Deformable Mixer Transformer for Multi-Task Learning of Dense Prediction","arxiv_id":"2301.03461","date":"2023-01-09","proceeding":null,"authors":["Yangyang Xu","Yibo Yang","Lefei Zhang"],"abstract":"Convolution neural networks (CNNs) and Transformers have their own advantages and both have been widely used for dense prediction in multi-task learning (MTL). Most of the current studies on MTL solely rely on CNN or Transformer. In this work, we present a novel MTL model by combining both merits of deformable CNN and query-based Transformer for multi-task learning of dense prediction. Our method, named DeMT, is based on a simple and effective encoder-decoder architecture (i.e., deformable mixer encoder and task-aware transformer decoder). First, the deformable mixer encoder contains two types of operators: the channel-aware mixing operator leveraged to allow communication among different channels ($i.e.,$ efficient channel location mixing), and the spatial-aware deformable operator with deformable convolution applied to efficiently sample more informative spatial locations (i.e., deformed features). Second, the task-aware transformer decoder consists of the task interaction block and task query block. The former is applied to capture task interaction features via self-attention. The latter leverages the deformed features and task-interacted features to generate the corresponding task-specific feature through a query-based Transformer for corresponding task predictions. Extensive experiments on two dense image prediction datasets, NYUD-v2 and PASCAL-Context, demonstrate that our model uses fewer GFLOPs and significantly outperforms current Transformer- and CNN-based competitive models on a variety of metrics. The code are available at https://github.com/yangyangxu0/DeMT .","url_abs":"https://arxiv.org/abs/2301.03461v3","url_pdf":"https://arxiv.org/pdf/2301.03461v3.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":"demt-deformable-mixer-transformer-for-multi","repo_url":"https://github.com/yangyangxu0/demt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"multi-task-learning","task_name":"Multi-Task Learning"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"deformable-convolution","method_name":"Deformable Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2301.03461","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.03461"}},"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/yangyangxu0/demt","reach":null},{"provenance":"deterministic:regex_extraction","url":"https://github.com/yangyangxu0/DeMT","reach":null}],"summary":{"ran":3,"unverified":3},"by_repo_kind":{"official":{"samples":6,"ran":3,"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":6,"samples":[{"code_sha256_prefix":"ee5f2467fb5ee79c","entry":"ChlSpl","repo":"yangyangxu0/DeMT","repo_kind":"official","path":"src/model/heads/demt_head.py","file_url":"https://github.com/yangyangxu0/DeMT/blob/HEAD/src/model/heads/demt_head.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ee5f2467fb5ee79c"}},{"code_sha256_prefix":"7fe9ae2fcbc68d4a","entry":"Offset","repo":"yangyangxu0/DeMT","repo_kind":"official","path":"src/model/heads/demt_head.py","file_url":"https://github.com/yangyangxu0/DeMT/blob/HEAD/src/model/heads/demt_head.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"7fe9ae2fcbc68d4a"}},{"code_sha256_prefix":"617fc5cd462c22cf","entry":"Residual","repo":"yangyangxu0/DeMT","repo_kind":"official","path":"src/model/heads/demt_head.py","file_url":"https://github.com/yangyangxu0/DeMT/blob/HEAD/src/model/heads/demt_head.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"617fc5cd462c22cf"}},{"code_sha256_prefix":"0961199e0f754665","entry":"BaseHead","repo":"yangyangxu0/DeMT","repo_kind":"official","path":"src/model/heads/demt_head.py","file_url":"https://github.com/yangyangxu0/DeMT/blob/HEAD/src/model/heads/demt_head.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":"0961199e0f754665"}},{"code_sha256_prefix":"114b6306d04488af","entry":"DefMixer","repo":"yangyangxu0/DeMT","repo_kind":"official","path":"src/model/heads/demt_head.py","file_url":"https://github.com/yangyangxu0/DeMT/blob/HEAD/src/model/heads/demt_head.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":"114b6306d04488af"}},{"code_sha256_prefix":"ab5ab3c443461ccb","entry":"DemtHead","repo":"yangyangxu0/DeMT","repo_kind":"official","path":"src/model/heads/demt_head.py","file_url":"https://github.com/yangyangxu0/DeMT/blob/HEAD/src/model/heads/demt_head.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":"ab5ab3c443461ccb"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}