{"about":{"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.","site":"https://codewithpapers.app","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","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/semantic-segmentation/papers/47","list_of":"/task/semantic-segmentation","task":"Semantic Segmentation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":47,"pages_in_order":148,"rows_per_page":100,"rows":[4601,4700],"of":14763,"counts":{"archive_papers_tagged":14763,"with_a_code_link":6644,"where_syntology_ran_a_sample":1583,"not_listed_spam_title":0,"listed":14763,"listed_where_code_ran":1583,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":1384,"every_run_a_failure_of_syntologys_instrument":199,"listed_with_a_run_with_no_instrument_failure":1384,"listed_every_run_a_failure_of_syntologys_instrument":199,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/semantic-segmentation","prev":"/task/semantic-segmentation/papers/46","next":"/task/semantic-segmentation/papers/48","papers":[{"url":"/paper/dynamic-multimodal-fusion","slug":"dynamic-multimodal-fusion","title":"Dynamic Multimodal Fusion","date":"2022-03-31","arxiv_id":"2204.00102","repositories_listed":1,"syntology":null},{"url":"/paper/tooth-instance-segmentation-on-panoramic","slug":"tooth-instance-segmentation-on-panoramic","title":"Tooth Instance Segmentation on Panoramic Dental Radiographs Using U-Nets and Morphological Processing","date":"2022-03-31","arxiv_id":"2204.00095","repositories_listed":1,"syntology":null},{"url":"/paper/image-to-lidar-self-supervised-distillation","slug":"image-to-lidar-self-supervised-distillation","title":"Image-to-Lidar Self-Supervised Distillation for Autonomous Driving Data","date":"2022-03-30","arxiv_id":"2203.16258","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/image-to-lidar-self-supervised-distillation#ran","syntology_url":"https://syntology.ai/paper/2203.16258","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16258"}},"official":{"repos":["valeoai/slidr"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-road-to-online-adaptation-for-semantic","slug":"on-the-road-to-online-adaptation-for-semantic","title":"On the Road to Online Adaptation for Semantic Image Segmentation","date":"2022-03-30","arxiv_id":"2203.16195","repositories_listed":1,"syntology":null},{"url":"/paper/threshold-matters-in-wsss-manipulating-the","slug":"threshold-matters-in-wsss-manipulating-the","title":"Threshold Matters in WSSS: Manipulating the Activation for the Robust and Accurate Segmentation Model Against Thresholds","date":"2022-03-30","arxiv_id":"2203.16045","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/threshold-matters-in-wsss-manipulating-the#ran","syntology_url":"https://syntology.ai/paper/2203.16045","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.16045"}},"official":{"repos":["gaviotas/amn"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/abstract-flow-for-temporal-semantic","slug":"abstract-flow-for-temporal-semantic","title":"Abstract Flow for Temporal Semantic Segmentation on the Permutohedral Lattice","date":"2022-03-29","arxiv_id":"2203.15469","repositories_listed":1,"syntology":null},{"url":"/paper/chex-channel-exploration-for-cnn-model","slug":"chex-channel-exploration-for-cnn-model","title":"CHEX: CHannel EXploration for CNN Model Compression","date":"2022-03-29","arxiv_id":"2203.15794","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/chex-channel-exploration-for-cnn-model#ran","syntology_url":"https://syntology.ai/paper/2203.15794","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15794"}},"official":null}},{"url":"/paper/harmonizing-pathological-and-normal-pixels","slug":"harmonizing-pathological-and-normal-pixels","title":"Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis","date":"2022-03-29","arxiv_id":"2203.15347","repositories_listed":1,"syntology":null},{"url":"/paper/in-n-out-generative-learning-for-dense","slug":"in-n-out-generative-learning-for-dense","title":"In-N-Out Generative Learning for Dense Unsupervised Video Segmentation","date":"2022-03-29","arxiv_id":"2203.15312","repositories_listed":1,"syntology":null},{"url":"/paper/mc-beit-multi-choice-discretization-for-image","slug":"mc-beit-multi-choice-discretization-for-image","title":"mc-BEiT: Multi-choice Discretization for Image BERT Pre-training","date":"2022-03-29","arxiv_id":"2203.15371","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/mc-beit-multi-choice-discretization-for-image#ran","syntology_url":"https://syntology.ai/paper/2203.15371","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.15371"}},"official":{"repos":["lixiaotong97/mc-beit"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/panoptic-nerf-3d-to-2d-label-transfer-for","slug":"panoptic-nerf-3d-to-2d-label-transfer-for","title":"Panoptic NeRF: 3D-to-2D Label Transfer for Panoptic Urban Scene Segmentation","date":"2022-03-29","arxiv_id":"2203.15224","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-leaf-segmentation-under","slug":"self-supervised-leaf-segmentation-under","title":"Self-Supervised Leaf Segmentation under Complex Lighting Conditions","date":"2022-03-29","arxiv_id":"2203.15943","repositories_listed":1,"syntology":null},{"url":"/paper/simt-handling-open-set-noise-for-domain","slug":"simt-handling-open-set-noise-for-domain","title":"SimT: Handling Open-set Noise for Domain Adaptive Semantic Segmentation","date":"2022-03-29","arxiv_id":"2203.15202","repositories_listed":1,"syntology":null},{"url":"/paper/target-and-task-specific-source-free-domain","slug":"target-and-task-specific-source-free-domain","title":"Target and Task specific Source-Free Domain Adaptive Image Segmentation","date":"2022-03-29","arxiv_id":"2203.15792","repositories_listed":1,"syntology":null},{"url":"/paper/a-distribution-dependent-mumford-shah-model","slug":"a-distribution-dependent-mumford-shah-model","title":"A distribution-dependent Mumford-Shah model for unsupervised hyperspectral image segmentation","date":"2022-03-28","arxiv_id":"2203.15058","repositories_listed":1,"syntology":null},{"url":"/paper/learning-where-to-learn-in-cross-view-self","slug":"learning-where-to-learn-in-cross-view-self","title":"Learning Where to Learn in Cross-View Self-Supervised Learning","date":"2022-03-28","arxiv_id":"2203.14898","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/learning-where-to-learn-in-cross-view-self#ran","syntology_url":"https://syntology.ai/paper/2203.14898","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14898"}},"official":{"repos":["LayneH/LEWEL"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-semantic-segmentation-a-prototype","slug":"rethinking-semantic-segmentation-a-prototype","title":"Rethinking Semantic Segmentation: A Prototype View","date":"2022-03-28","arxiv_id":"2203.15102","repositories_listed":1,"syntology":null},{"url":"/paper/mugs-a-multi-granular-self-supervised","slug":"mugs-a-multi-granular-self-supervised","title":"Mugs: A Multi-Granular Self-Supervised Learning Framework","date":"2022-03-27","arxiv_id":"2203.14415","repositories_listed":1,"syntology":{"n":21,"n_ran":19,"n_constructed":0,"n_ran_checked":19,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":19,"n_pointer_only":3,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 0 violated, 19 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mugs-a-multi-granular-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2203.14415","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14415"}},"official":{"repos":["sail-sg/mugs"],"state":"official (archive's flag): 19 ran","n_ran":19,"n_constructed":0,"n_ran_no_instrument_failure":19,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/temporal-transductive-inference-for-few-shot","slug":"temporal-transductive-inference-for-few-shot","title":"Temporal Transductive Inference for Few-Shot Video Object Segmentation","date":"2022-03-27","arxiv_id":"2203.14308","repositories_listed":1,"syntology":null},{"url":"/paper/does-monocular-depth-estimation-provide","slug":"does-monocular-depth-estimation-provide","title":"On the Viability of Monocular Depth Pre-training for Semantic Segmentation","date":"2022-03-26","arxiv_id":"2203.13987","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-segmentation-by-early-region-proxy","slug":"semantic-segmentation-by-early-region-proxy","title":"Semantic Segmentation by Early Region Proxy","date":"2022-03-26","arxiv_id":"2203.14043","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/semantic-segmentation-by-early-region-proxy#ran","syntology_url":"https://syntology.ai/paper/2203.14043","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14043"}},"official":{"repos":["yif-zhang/regionproxy"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/uncertainty-aware-contrastive-distillation","slug":"uncertainty-aware-contrastive-distillation","title":"Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation","date":"2022-03-26","arxiv_id":"2203.14098","repositories_listed":1,"syntology":null},{"url":"/paper/a-stitch-in-time-saves-nine-a-train-time","slug":"a-stitch-in-time-saves-nine-a-train-time","title":"A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration","date":"2022-03-25","arxiv_id":"2203.13834","repositories_listed":1,"syntology":{"n":5,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/a-stitch-in-time-saves-nine-a-train-time#ran","syntology_url":"https://syntology.ai/paper/2203.13834","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.13834"}},"official":{"repos":["mdca-loss/mdca-calibration"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-scale-and-cross-scale-contrastive","slug":"multi-scale-and-cross-scale-contrastive","title":"Multi-scale and Cross-scale Contrastive Learning for Semantic Segmentation","date":"2022-03-25","arxiv_id":"2203.13409","repositories_listed":1,"syntology":null},{"url":"/paper/neural-networks-with-divisive-normalization","slug":"neural-networks-with-divisive-normalization","title":"Neural Networks with Divisive normalization for image segmentation with application in cityscapes dataset","date":"2022-03-25","arxiv_id":"2203.13558","repositories_listed":1,"syntology":null},{"url":"/paper/noisy-boundaries-lemon-or-lemonade-for-semi","slug":"noisy-boundaries-lemon-or-lemonade-for-semi","title":"Noisy Boundaries: Lemon or Lemonade for Semi-supervised Instance Segmentation?","date":"2022-03-25","arxiv_id":"2203.13427","repositories_listed":1,"syntology":null},{"url":"/paper/azinorm-exploiting-the-radial-symmetry-of","slug":"azinorm-exploiting-the-radial-symmetry-of","title":"AziNorm: Exploiting the Radial Symmetry of Point Cloud for Azimuth-Normalized 3D Perception","date":"2022-03-24","arxiv_id":"2203.13090","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/azinorm-exploiting-the-radial-symmetry-of#ran","syntology_url":"https://syntology.ai/paper/2203.13090","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.13090"}},"official":{"repos":["hustvl/azinorm"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/cross-domain-few-shot-semantic-segmentation","slug":"cross-domain-few-shot-semantic-segmentation","title":"Cross-Domain Few-Shot Semantic Segmentation","date":"2022-03-24","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/hmfs-hybrid-masking-for-few-shot-segmentation","slug":"hmfs-hybrid-masking-for-few-shot-segmentation","title":"HM: Hybrid Masking for Few-Shot Segmentation","date":"2022-03-24","arxiv_id":"2203.12826","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/hmfs-hybrid-masking-for-few-shot-segmentation#ran","syntology_url":"https://syntology.ai/paper/2203.12826","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.12826"}},"official":{"repos":["moonsh/hm-hybrid-masking"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/make-a-scene-scene-based-text-to-image","slug":"make-a-scene-scene-based-text-to-image","title":"Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors","date":"2022-03-24","arxiv_id":"2203.13131","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":2,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/make-a-scene-scene-based-text-to-image#ran","syntology_url":"https://syntology.ai/paper/2203.13131","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.13131"}},"official":null}},{"url":"/paper/using-orientation-to-distinguish-overlapping","slug":"using-orientation-to-distinguish-overlapping","title":"Using Orientation to Distinguish Overlapping Chromosomes","date":"2022-03-24","arxiv_id":"2203.13004","repositories_listed":1,"syntology":null},{"url":"/paper/video-instance-segmentation-via-multi-scale","slug":"video-instance-segmentation-via-multi-scale","title":"Video Instance Segmentation via Multi-scale Spatio-temporal Split Attention Transformer","date":"2022-03-24","arxiv_id":"2203.13253","repositories_listed":1,"syntology":null},{"url":"/paper/activation-based-sampling-for-pixel-to-image","slug":"activation-based-sampling-for-pixel-to-image","title":"Importance Sampling CAMs for Weakly-Supervised Segmentation","date":"2022-03-23","arxiv_id":"2203.12459","repositories_listed":1,"syntology":null},{"url":"/paper/dynamicearthnet-daily-multi-spectral","slug":"dynamicearthnet-daily-multi-spectral","title":"DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation","date":"2022-03-23","arxiv_id":"2203.12560","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dynamicearthnet-daily-multi-spectral#ran","syntology_url":"https://syntology.ai/paper/2203.12560","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.12560"}},"official":null}},{"url":"/paper/mt-uda-towards-unsupervised-cross-modality","slug":"mt-uda-towards-unsupervised-cross-modality","title":"MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels","date":"2022-03-23","arxiv_id":"2203.12454","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-salient-object-detection-with","slug":"unsupervised-salient-object-detection-with","title":"Unsupervised Salient Object Detection with Spectral Cluster Voting","date":"2022-03-23","arxiv_id":"2203.12614","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/unsupervised-salient-object-detection-with#ran","syntology_url":"https://syntology.ai/paper/2203.12614","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.12614"}},"official":{"repos":["noelshin/selfmask"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/cp2-copy-paste-contrastive-pretraining-for","slug":"cp2-copy-paste-contrastive-pretraining-for","title":"CP2: Copy-Paste Contrastive Pretraining for Semantic Segmentation","date":"2022-03-22","arxiv_id":"2203.11709","repositories_listed":1,"syntology":null},{"url":"/paper/improving-generalization-in-federated","slug":"improving-generalization-in-federated","title":"Improving Generalization in Federated Learning by Seeking Flat Minima","date":"2022-03-22","arxiv_id":"2203.11834","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/improving-generalization-in-federated#ran","syntology_url":"https://syntology.ai/paper/2203.11834","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11834"}},"official":{"repos":["debcaldarola/fedsam"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-patch-to-cluster-attention-in-vision","slug":"learning-patch-to-cluster-attention-in-vision","title":"PaCa-ViT: Learning Patch-to-Cluster Attention in Vision Transformers","date":"2022-03-22","arxiv_id":"2203.11987","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/learning-patch-to-cluster-attention-in-vision#ran","syntology_url":"https://syntology.ai/paper/2203.11987","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11987"}},"official":{"repos":["ivmcl/pacavit"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dense-siamese-network","slug":"dense-siamese-network","title":"Dense Siamese Network for Dense Unsupervised Learning","date":"2022-03-21","arxiv_id":"2203.11075","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dense-siamese-network#ran","syntology_url":"https://syntology.ai/paper/2203.11075","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11075"}},"official":{"repos":["zwwwayne/densesiam"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/drive-segment-unsupervised-semantic","slug":"drive-segment-unsupervised-semantic","title":"Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-modal Distillation","date":"2022-03-21","arxiv_id":"2203.11160","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/drive-segment-unsupervised-semantic#ran","syntology_url":"https://syntology.ai/paper/2203.11160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11160"}},"official":{"repos":["vobecant/DriveAndSegment"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/generating-fast-and-slow-scene-decomposition","slug":"generating-fast-and-slow-scene-decomposition","title":"Test-time Adaptation with Slot-Centric Models","date":"2022-03-21","arxiv_id":"2203.11194","repositories_listed":1,"syntology":{"n":14,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":3,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":14,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/generating-fast-and-slow-scene-decomposition#ran","syntology_url":"https://syntology.ai/paper/2203.11194","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.11194"}},"official":{"repos":["mihirp1998/Slot-TTA"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/generative-adversarial-network-for-future","slug":"generative-adversarial-network-for-future","title":"Generative Adversarial Network for Future Hand Segmentation from Egocentric Video","date":"2022-03-21","arxiv_id":"2203.11305","repositories_listed":1,"syntology":null},{"url":"/paper/tree-energy-loss-towards-sparsely-annotated","slug":"tree-energy-loss-towards-sparsely-annotated","title":"Tree Energy Loss: Towards Sparsely Annotated Semantic Segmentation","date":"2022-03-21","arxiv_id":"2203.10739","repositories_listed":1,"syntology":null},{"url":"/paper/tvconv-efficient-translation-variant","slug":"tvconv-efficient-translation-variant","title":"TVConv: Efficient Translation Variant Convolution for Layout-aware Visual Processing","date":"2022-03-20","arxiv_id":"2203.10489","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/tvconv-efficient-translation-variant#ran","syntology_url":"https://syntology.ai/paper/2203.10489","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.10489"}},"official":{"repos":["jierunchen/tvconv"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-robust-semantic-segmentation-of","slug":"towards-robust-semantic-segmentation-of","title":"Towards Robust Semantic Segmentation of Accident Scenes via Multi-Source Mixed Sampling and Meta-Learning","date":"2022-03-19","arxiv_id":"2203.10395","repositories_listed":1,"syntology":null},{"url":"/paper/class-balanced-pixel-level-self-labeling-for","slug":"class-balanced-pixel-level-self-labeling-for","title":"Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic Segmentation","date":"2022-03-18","arxiv_id":"2203.09744","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/class-balanced-pixel-level-self-labeling-for#ran","syntology_url":"https://syntology.ai/paper/2203.09744","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09744"}},"official":{"repos":["lslrh/cpsl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/ess-learning-event-based-semantic","slug":"ess-learning-event-based-semantic","title":"ESS: Learning Event-based Semantic Segmentation from Still Images","date":"2022-03-18","arxiv_id":"2203.10016","repositories_listed":1,"syntology":null},{"url":"/paper/local-global-context-aware-transformer-for","slug":"local-global-context-aware-transformer-for","title":"Local-Global Context Aware Transformer for Language-Guided Video Segmentation","date":"2022-03-18","arxiv_id":"2203.09773","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/local-global-context-aware-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2203.09773","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09773"}},"official":{"repos":["leonnnop/locater"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/unbiased-subclass-regularization-for-semi","slug":"unbiased-subclass-regularization-for-semi","title":"Unbiased Subclass Regularization for Semi-Supervised Semantic Segmentation","date":"2022-03-18","arxiv_id":"2203.10026","repositories_listed":1,"syntology":null},{"url":"/paper/data-domain-aware-and-task-aware-pre-training","slug":"data-domain-aware-and-task-aware-pre-training","title":"DATA: Domain-Aware and Task-Aware Self-supervised Learning","date":"2022-03-17","arxiv_id":"2203.09041","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/data-domain-aware-and-task-aware-pre-training#ran","syntology_url":"https://syntology.ai/paper/2203.09041","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09041"}},"official":{"repos":["gaia-vision/gaia-ssl"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/delta-distillation-for-efficient-video","slug":"delta-distillation-for-efficient-video","title":"Delta Distillation for Efficient Video Processing","date":"2022-03-17","arxiv_id":"2203.09594","repositories_listed":1,"syntology":null},{"url":"/paper/label-conditioned-segmentation","slug":"label-conditioned-segmentation","title":"Label conditioned segmentation","date":"2022-03-17","arxiv_id":"2203.10091","repositories_listed":1,"syntology":null},{"url":"/paper/panoformer-panorama-transformer-for-indoor","slug":"panoformer-panorama-transformer-for-indoor","title":"PanoFormer: Panorama Transformer for Indoor 360 Depth Estimation","date":"2022-03-17","arxiv_id":"2203.09283","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/panoformer-panorama-transformer-for-indoor#ran","syntology_url":"https://syntology.ai/paper/2203.09283","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09283"}},"official":{"repos":["zhijieshen-bjtu/panoformer"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/regional-semantic-contrast-and-aggregation","slug":"regional-semantic-contrast-and-aggregation","title":"Regional Semantic Contrast and Aggregation for Weakly Supervised Semantic Segmentation","date":"2022-03-17","arxiv_id":"2203.09653","repositories_listed":1,"syntology":null},{"url":"/paper/to-scene-a-large-scale-dataset-for","slug":"to-scene-a-large-scale-dataset-for","title":"TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes","date":"2022-03-17","arxiv_id":"2203.09440","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":3,"n_pointer_only":1,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 1 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/to-scene-a-large-scale-dataset-for#ran","syntology_url":"https://syntology.ai/paper/2203.09440","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.09440"}},"official":{"repos":["GAP-LAB-CUHK-SZ/TO-Scene"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/data-efficient-3d-learner-via-knowledge","slug":"data-efficient-3d-learner-via-knowledge","title":"Data Efficient 3D Learner via Knowledge Transferred from 2D Model","date":"2022-03-16","arxiv_id":"2203.08479","repositories_listed":1,"syntology":null},{"url":"/paper/graph-flow-cross-layer-graph-flow","slug":"graph-flow-cross-layer-graph-flow","title":"Graph Flow: Cross-layer Graph Flow Distillation for Dual Efficient Medical Image Segmentation","date":"2022-03-16","arxiv_id":"2203.08667","repositories_listed":1,"syntology":null},{"url":"/paper/object-discovery-and-representation-networks","slug":"object-discovery-and-representation-networks","title":"Object discovery and representation networks","date":"2022-03-16","arxiv_id":"2203.08777","repositories_listed":1,"syntology":null},{"url":"/paper/point-unet-a-context-aware-point-based-neural","slug":"point-unet-a-context-aware-point-based-neural","title":"Point-Unet: A Context-aware Point-based Neural Network for Volumetric Segmentation","date":"2022-03-16","arxiv_id":"2203.08964","repositories_listed":1,"syntology":null},{"url":"/paper/deep-learning-for-radar-data-exploitation-of","slug":"deep-learning-for-radar-data-exploitation-of","title":"Deep learning for radar data exploitation of autonomous vehicle","date":"2022-03-15","arxiv_id":"2203.08038","repositories_listed":1,"syntology":null},{"url":"/paper/inverted-pyramid-multi-task-transformer-for","slug":"inverted-pyramid-multi-task-transformer-for","title":"InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene Understanding","date":"2022-03-15","arxiv_id":"2203.07997","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/inverted-pyramid-multi-task-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2203.07997","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.07997"}},"official":{"repos":["prismformore/InvPT"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-what-not-to-segment-a-new","slug":"learning-what-not-to-segment-a-new","title":"Learning What Not to Segment: A New Perspective on Few-Shot Segmentation","date":"2022-03-15","arxiv_id":"2203.07615","repositories_listed":1,"syntology":{"n":17,"n_ran":16,"n_constructed":0,"n_ran_checked":9,"n_instrument":7,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":11,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 7 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-what-not-to-segment-a-new#ran","syntology_url":"https://syntology.ai/paper/2203.07615","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.07615"}},"official":{"repos":["chunbolang/BAM"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/sats-self-attention-transfer-for-continual","slug":"sats-self-attention-transfer-for-continual","title":"SATS: Self-Attention Transfer for Continual Semantic Segmentation","date":"2022-03-15","arxiv_id":"2203.07667","repositories_listed":1,"syntology":null},{"url":"/paper/smoothing-matters-momentum-transformer-for","slug":"smoothing-matters-momentum-transformer-for","title":"Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation","date":"2022-03-15","arxiv_id":"2203.07988","repositories_listed":1,"syntology":{"n":8,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/smoothing-matters-momentum-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2203.07988","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.07988"}},"official":{"repos":["alpc91/transda"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/adas-a-direct-adaptation-strategy-for-multi","slug":"adas-a-direct-adaptation-strategy-for-multi","title":"ADAS: A Direct Adaptation Strategy for Multi-Target Domain Adaptive Semantic Segmentation","date":"2022-03-14","arxiv_id":"2203.06811","repositories_listed":1,"syntology":null},{"url":"/paper/car-class-aware-regularizations-for-semantic-1","slug":"car-class-aware-regularizations-for-semantic-1","title":"CAR: Class-aware Regularizations for Semantic Segmentation","date":"2022-03-14","arxiv_id":"2203.07160","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/car-class-aware-regularizations-for-semantic-1#ran","syntology_url":"https://syntology.ai/paper/2203.07160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.07160"}},"official":{"repos":["edwardyehuang/CAR"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/implicit-motion-handling-for-video","slug":"implicit-motion-handling-for-video","title":"Implicit Motion Handling for Video Camouflaged Object Detection","date":"2022-03-14","arxiv_id":"2203.07363","repositories_listed":1,"syntology":null},{"url":"/paper/recursivemix-mixed-learning-with-history","slug":"recursivemix-mixed-learning-with-history","title":"RecursiveMix: Mixed Learning with History","date":"2022-03-14","arxiv_id":"2203.06844","repositories_listed":1,"syntology":null},{"url":"/paper/transcam-transformer-attention-based-cam","slug":"transcam-transformer-attention-based-cam","title":"TransCAM: Transformer Attention-based CAM Refinement for Weakly Supervised Semantic Segmentation","date":"2022-03-14","arxiv_id":"2203.07239","repositories_listed":1,"syntology":null},{"url":"/paper/deformable-vistr-spatio-temporal-deformable","slug":"deformable-vistr-spatio-temporal-deformable","title":"Deformable VisTR: Spatio temporal deformable attention for video instance segmentation","date":"2022-03-12","arxiv_id":"2203.06318","repositories_listed":1,"syntology":null},{"url":"/paper/embedding-earth-self-supervised-contrastive","slug":"embedding-earth-self-supervised-contrastive","title":"Embedding Earth: Self-supervised contrastive pre-training for dense land cover classification","date":"2022-03-11","arxiv_id":"2203.06041","repositories_listed":1,"syntology":null},{"url":"/paper/hyperbolic-image-segmentation","slug":"hyperbolic-image-segmentation","title":"Hyperbolic Image Segmentation","date":"2022-03-11","arxiv_id":"2203.05898","repositories_listed":1,"syntology":null},{"url":"/paper/rood-mri-benchmarking-the-robustness-of-deep","slug":"rood-mri-benchmarking-the-robustness-of-deep","title":"ROOD-MRI: Benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in MRI","date":"2022-03-11","arxiv_id":"2203.06060","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-boundary-learning-for-point-cloud","slug":"contrastive-boundary-learning-for-point-cloud","title":"Contrastive Boundary Learning for Point Cloud Segmentation","date":"2022-03-10","arxiv_id":"2203.05272","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/contrastive-boundary-learning-for-point-cloud#ran","syntology_url":"https://syntology.ai/paper/2203.05272","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.05272"}},"official":{"repos":["liyaotang/contrastboundary"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/intention-aware-feature-propagation-network","slug":"intention-aware-feature-propagation-network","title":"Cascaded Sparse Feature Propagation Network for Interactive Segmentation","date":"2022-03-10","arxiv_id":"2203.05145","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-fly-test-time-adaptation-for-medical","slug":"on-the-fly-test-time-adaptation-for-medical","title":"On-the-Fly Test-time Adaptation for Medical Image Segmentation","date":"2022-03-10","arxiv_id":"2203.05574","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/on-the-fly-test-time-adaptation-for-medical#ran","syntology_url":"https://syntology.ai/paper/2203.05574","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.05574"}},"official":{"repos":["jeya-maria-jose/on-the-fly-adaptation"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/representation-compensation-networks-for","slug":"representation-compensation-networks-for","title":"Representation Compensation Networks for Continual Semantic Segmentation","date":"2022-03-10","arxiv_id":"2203.05402","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/representation-compensation-networks-for#ran","syntology_url":"https://syntology.ai/paper/2203.05402","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.05402"}},"official":{"repos":["zhangchbin/rcil"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/temporal-context-for-robust-maritime-obstacle","slug":"temporal-context-for-robust-maritime-obstacle","title":"Temporal Context for Robust Maritime Obstacle Detection","date":"2022-03-10","arxiv_id":"2203.05352","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-transformer-framework-for-group","slug":"a-unified-transformer-framework-for-group","title":"A Unified Transformer Framework for Group-based Segmentation: Co-Segmentation, Co-Saliency Detection and Video Salient Object Detection","date":"2022-03-09","arxiv_id":"2203.04708","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/a-unified-transformer-framework-for-group#ran","syntology_url":"https://syntology.ai/paper/2203.04708","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.04708"}},"official":{"repos":["suyukun666/UFO"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/cmx-cross-modal-fusion-for-rgb-x-semantic","slug":"cmx-cross-modal-fusion-for-rgb-x-semantic","title":"CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers","date":"2022-03-09","arxiv_id":"2203.04838","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/cmx-cross-modal-fusion-for-rgb-x-semantic#ran","syntology_url":"https://syntology.ai/paper/2203.04838","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.04838"}},"official":{"repos":["huaaaliu/rgbx_semantic_segmentation"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/e2ec-an-end-to-end-contour-based-method-for","slug":"e2ec-an-end-to-end-contour-based-method-for","title":"E2EC: An End-to-End Contour-based Method for High-Quality High-Speed Instance Segmentation","date":"2022-03-08","arxiv_id":"2203.04074","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":4,"n_ran_checked":5,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":4,"n_pointer_only":10,"phrase":"8 ran (of which 4 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 1 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/e2ec-an-end-to-end-contour-based-method-for#ran","syntology_url":"https://syntology.ai/paper/2203.04074","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.04074"}},"official":{"repos":["zhang-tao-whu/e2ec"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":4,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/end-to-end-semi-supervised-learning-for-video","slug":"end-to-end-semi-supervised-learning-for-video","title":"End-to-End Semi-Supervised Learning for Video Action Detection","date":"2022-03-08","arxiv_id":"2203.04251","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/end-to-end-semi-supervised-learning-for-video#ran","syntology_url":"https://syntology.ai/paper/2203.04251","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.04251"}},"official":{"repos":["AKASH2907/End-to-End-Semi-Supervised-Learning-for-Video-Action-Detection"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/naviairway-a-bronchiole-sensitive-deep","slug":"naviairway-a-bronchiole-sensitive-deep","title":"NaviAirway: a Bronchiole-sensitive Deep Learning-based Airway Segmentation Pipeline","date":"2022-03-08","arxiv_id":"2203.04294","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-semantic-segmentation-using-2","slug":"weakly-supervised-semantic-segmentation-using-2","title":"Weakly Supervised Semantic Segmentation using Out-of-Distribution Data","date":"2022-03-08","arxiv_id":"2203.03860","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/weakly-supervised-semantic-segmentation-using-2#ran","syntology_url":"https://syntology.ai/paper/2203.03860","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.03860"}},"official":{"repos":["naver-ai/w-ood"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/end-to-end-video-instance-segmentation-via-1","slug":"end-to-end-video-instance-segmentation-via-1","title":"End-to-end video instance segmentation via spatial-temporal graph neural networks","date":"2022-03-07","arxiv_id":"2203.03145","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-segmentation-in-art-paintings","slug":"semantic-segmentation-in-art-paintings","title":"Semantic Segmentation in Art Paintings","date":"2022-03-07","arxiv_id":"2203.03238","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/semantic-segmentation-in-art-paintings#ran","syntology_url":"https://syntology.ai/paper/2203.03238","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.03238"}},"official":{"repos":["nadavc220/semanticsegmentationinartpaintings"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/signature-and-log-signature-for-the-study-of","slug":"signature-and-log-signature-for-the-study-of","title":"Signature and Log-signature for the Study of Empirical Distributions Generated with GANs","date":"2022-03-07","arxiv_id":"2203.03226","repositories_listed":1,"syntology":null},{"url":"/paper/stepwise-feature-fusion-local-guides-global","slug":"stepwise-feature-fusion-local-guides-global","title":"Stepwise Feature Fusion: Local Guides Global","date":"2022-03-07","arxiv_id":"2203.03635","repositories_listed":1,"syntology":null},{"url":"/paper/highly-accurate-dichotomous-image","slug":"highly-accurate-dichotomous-image","title":"Highly Accurate Dichotomous Image Segmentation","date":"2022-03-06","arxiv_id":"2203.03041","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":6,"n_ran_checked":6,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"9 ran (of which 6 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 3 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/highly-accurate-dichotomous-image#ran","syntology_url":"https://syntology.ai/paper/2203.03041","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.03041"}},"official":{"repos":["xuebinqin/DIS"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-class-token-transformer-for-weakly","slug":"multi-class-token-transformer-for-weakly","title":"Multi-class Token Transformer for Weakly Supervised Semantic Segmentation","date":"2022-03-06","arxiv_id":"2203.02891","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":4,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"4 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","sample_list":"/paper/multi-class-token-transformer-for-weakly#ran","syntology_url":"https://syntology.ai/paper/2203.02891","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.02891"}},"official":{"repos":["xulianuwa/mctformer"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-image-specific-prototype","slug":"self-supervised-image-specific-prototype","title":"Self-supervised Image-specific Prototype Exploration for Weakly Supervised Semantic Segmentation","date":"2022-03-06","arxiv_id":"2203.02909","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/self-supervised-image-specific-prototype#ran","syntology_url":"https://syntology.ai/paper/2203.02909","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.02909"}},"official":{"repos":["chenqi1126/sipe"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/adversarial-dual-student-with-differentiable","slug":"adversarial-dual-student-with-differentiable","title":"Adversarial Dual-Student with Differentiable Spatial Warping for Semi-Supervised Semantic Segmentation","date":"2022-03-05","arxiv_id":"2203.02792","repositories_listed":1,"syntology":null},{"url":"/paper/scribble-supervised-medical-image","slug":"scribble-supervised-medical-image","title":"Scribble-Supervised Medical Image Segmentation via Dual-Branch Network and Dynamically Mixed Pseudo Labels Supervision","date":"2022-03-04","arxiv_id":"2203.02106","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/scribble-supervised-medical-image#ran","syntology_url":"https://syntology.ai/paper/2203.02106","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.02106"}},"official":{"repos":["HiLab-git/WSL4MIS"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/anomaly-detection-inspired-few-shot-medical","slug":"anomaly-detection-inspired-few-shot-medical","title":"Anomaly Detection-Inspired Few-Shot Medical Image Segmentation Through Self-Supervision With Supervoxels","date":"2022-03-03","arxiv_id":"2203.02048","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 2 unverified","sample_list":"/paper/anomaly-detection-inspired-few-shot-medical#ran","syntology_url":"https://syntology.ai/paper/2203.02048","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.02048"}},"official":{"repos":["sha168/ADNet"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/curriculum-style-local-to-global-adaptation","slug":"curriculum-style-local-to-global-adaptation","title":"Curriculum-style Local-to-global Adaptation for Cross-domain Remote Sensing Image Segmentation","date":"2022-03-03","arxiv_id":"2203.01539","repositories_listed":1,"syntology":null},{"url":"/paper/cyclemix-a-holistic-strategy-for-medical","slug":"cyclemix-a-holistic-strategy-for-medical","title":"CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision","date":"2022-03-03","arxiv_id":"2203.01475","repositories_listed":1,"syntology":null},{"url":"/paper/hoi4d-a-4d-egocentric-dataset-for-category","slug":"hoi4d-a-4d-egocentric-dataset-for-category","title":"HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interaction","date":"2022-03-03","arxiv_id":"2203.01577","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/hoi4d-a-4d-egocentric-dataset-for-category#ran","syntology_url":"https://syntology.ai/paper/2203.01577","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.01577"}},"official":{"repos":["leolyliu/HOI4D-Instructions"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/instance-segmentation-for-autonomous-log","slug":"instance-segmentation-for-autonomous-log","title":"Instance Segmentation for Autonomous Log Grasping in Forestry Operations","date":"2022-03-03","arxiv_id":"2203.01902","repositories_listed":1,"syntology":null},{"url":"/paper/polar-transformation-based-multiple-instance","slug":"polar-transformation-based-multiple-instance","title":"Polar Transformation Based Multiple Instance Learning Assisting Weakly Supervised Image Segmentation With Loose Bounding Box Annotations","date":"2022-03-03","arxiv_id":"2203.06000","repositories_listed":1,"syntology":null}],"record_sha256":"9dc61ffa34548534d27dbf3d572e178ee7d5f8ea6de0a80a0382f29e22718196","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}