{"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/weakly-supervised-learning/papers/2","list_of":"/task/weakly-supervised-learning","task":"Weakly-supervised Learning","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":2,"pages_in_order":7,"rows_per_page":100,"rows":[101,200],"of":613,"counts":{"archive_papers_tagged":613,"with_a_code_link":259,"where_syntology_ran_a_sample":51,"not_listed_spam_title":0,"listed":613,"listed_where_code_ran":51,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":41,"every_run_a_failure_of_syntologys_instrument":10,"listed_with_a_run_with_no_instrument_failure":41,"listed_every_run_a_failure_of_syntologys_instrument":10,"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/weakly-supervised-learning","prev":"/task/weakly-supervised-learning","next":"/task/weakly-supervised-learning/papers/3","papers":[{"url":"/paper/exploring-effective-priors-and-efficient","slug":"exploring-effective-priors-and-efficient","title":"Exploring Effective Priors and Efficient Models for Weakly-Supervised Change Detection","date":"2023-07-20","arxiv_id":"2307.10853","repositories_listed":1,"syntology":null},{"url":"/paper/partial-vessels-annotation-based-coronary","slug":"partial-vessels-annotation-based-coronary","title":"Partial Vessels Annotation-based Coronary Artery Segmentation with Self-training and Prototype Learning","date":"2023-07-10","arxiv_id":"2307.04472","repositories_listed":1,"syntology":null},{"url":"/paper/dias-a-comprehensive-benchmark-for-dsa","slug":"dias-a-comprehensive-benchmark-for-dsa","title":"DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequences","date":"2023-06-21","arxiv_id":"2306.12153","repositories_listed":1,"syntology":null},{"url":"/paper/partial-label-regression-1","slug":"partial-label-regression-1","title":"Partial-Label Regression","date":"2023-06-15","arxiv_id":"2306.08968","repositories_listed":1,"syntology":null},{"url":"/paper/cyclic-learning-bridging-image-level-labels","slug":"cyclic-learning-bridging-image-level-labels","title":"Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation","date":"2023-06-05","arxiv_id":"2306.02691","repositories_listed":1,"syntology":null},{"url":"/paper/conformal-prediction-with-partially-labeled","slug":"conformal-prediction-with-partially-labeled","title":"Conformal Prediction with Partially Labeled Data","date":"2023-06-01","arxiv_id":"2306.01191","repositories_listed":1,"syntology":null},{"url":"/paper/a-survey-of-label-efficient-deep-learning-for","slug":"a-survey-of-label-efficient-deep-learning-for","title":"A Survey of Label-Efficient Deep Learning for 3D Point Clouds","date":"2023-05-31","arxiv_id":"2305.19812","repositories_listed":1,"syntology":null},{"url":"/paper/the-rise-of-ai-language-pathologists","slug":"the-rise-of-ai-language-pathologists","title":"The Rise of AI Language Pathologists: Exploring Two-level Prompt Learning for Few-shot Weakly-supervised Whole Slide Image Classification","date":"2023-05-29","arxiv_id":"2305.17891","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":1,"n_no_contract":2,"n_pointer_only":6,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/the-rise-of-ai-language-pathologists#ran","syntology_url":"https://syntology.ai/paper/2305.17891","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.17891"}},"official":{"repos":["miccaiif/top"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":2,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/all-points-matter-entropy-regularized-1","slug":"all-points-matter-entropy-regularized-1","title":"All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation","date":"2023-05-25","arxiv_id":"2305.15832","repositories_listed":1,"syntology":{"n":33,"n_ran":16,"n_constructed":0,"n_ran_checked":3,"n_instrument":13,"n_unverified":17,"n_honours":2,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"16 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 1 violated, 0 with no contract checked; 13 where Syntology's instrument failed) · 17 unverified","sample_list":"/paper/all-points-matter-entropy-regularized-1#ran","syntology_url":"https://syntology.ai/paper/2305.15832","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.15832"}},"official":{"repos":["LiyaoTang/ERDA"],"state":"official (archive's flag): 16 ran","n_ran":16,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":17,"ran_from_kinds":["official"]}}},{"url":"/paper/label-efficient-learning-in-agriculture-a","slug":"label-efficient-learning-in-agriculture-a","title":"Label-Efficient Learning in Agriculture: A Comprehensive Review","date":"2023-05-24","arxiv_id":"2305.14691","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-learning-of-visual-1","slug":"weakly-supervised-learning-of-visual-1","title":"Weakly-Supervised Learning of Visual Relations in Multimodal Pretraining","date":"2023-05-23","arxiv_id":"2305.14281","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/weakly-supervised-learning-of-visual-1#ran","syntology_url":"https://syntology.ai/paper/2305.14281","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.14281"}},"official":{"repos":["e-bug/weak-relation-vlm"],"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/scribble-supervised-target-extraction-method","slug":"scribble-supervised-target-extraction-method","title":"Scribble-Supervised Target Extraction Method Based on Inner Structure-Constraint for Remote Sensing Images","date":"2023-05-18","arxiv_id":"2305.10661","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-roi-extraction-method-based","slug":"weakly-supervised-roi-extraction-method-based","title":"Weakly-supervised ROI extraction method based on contrastive learning for remote sensing images","date":"2023-05-10","arxiv_id":"2305.05887","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-segmentation-with-point","slug":"weakly-supervised-segmentation-with-point","title":"Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model","date":"2023-04-07","arxiv_id":"2304.03572","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/weakly-supervised-segmentation-with-point#ran","syntology_url":"https://syntology.ai/paper/2304.03572","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.03572"}},"official":{"repos":["hrzhang1123/cvm_ws_segmentation"],"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/weaktr-exploring-plain-vision-transformer-for","slug":"weaktr-exploring-plain-vision-transformer-for","title":"WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation","date":"2023-04-03","arxiv_id":"2304.01184","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/weaktr-exploring-plain-vision-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2304.01184","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.01184"}},"official":{"repos":["hustvl/weaktr"],"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/physics-informed-pointnet-on-how-many","slug":"physics-informed-pointnet-on-how-many","title":"Physics-informed PointNet: On how many irregular geometries can it solve an inverse problem simultaneously? Application to linear elasticity","date":"2023-03-22","arxiv_id":"2303.13634","repositories_listed":1,"syntology":null},{"url":"/paper/neural-pre-processing-a-learning-framework","slug":"neural-pre-processing-a-learning-framework","title":"Neural Pre-Processing: A Learning Framework for End-to-end Brain MRI Pre-processing","date":"2023-03-21","arxiv_id":"2303.12148","repositories_listed":1,"syntology":null},{"url":"/paper/hyperbolic-self-paced-learning-for-self","slug":"hyperbolic-self-paced-learning-for-self","title":"HYperbolic Self-Paced Learning for Self-Supervised Skeleton-based Action Representations","date":"2023-03-10","arxiv_id":"2303.06242","repositories_listed":1,"syntology":{"n":13,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":9,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 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) · 9 unverified","sample_list":"/paper/hyperbolic-self-paced-learning-for-self#ran","syntology_url":"https://syntology.ai/paper/2303.06242","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.06242"}},"official":{"repos":["paolomandica/hysp"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/reliability-adaptive-consistency","slug":"reliability-adaptive-consistency","title":"Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentation","date":"2023-03-09","arxiv_id":"2303.05164","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-label-learning-flows-1","slug":"weakly-supervised-label-learning-flows-1","title":"Weakly Supervised Label Learning Flows","date":"2023-02-19","arxiv_id":"2302.09649","repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-attention-based-on-gaussian","slug":"probabilistic-attention-based-on-gaussian","title":"Probabilistic Attention based on Gaussian Processes for Deep Multiple Instance Learning","date":"2023-02-08","arxiv_id":"2302.04061","repositories_listed":1,"syntology":null},{"url":"/paper/ovarnet-towards-open-vocabulary-object","slug":"ovarnet-towards-open-vocabulary-object","title":"OvarNet: Towards Open-vocabulary Object Attribute Recognition","date":"2023-01-23","arxiv_id":"2301.09506","repositories_listed":1,"syntology":null},{"url":"/paper/deep-statistical-solver-for-distribution","slug":"deep-statistical-solver-for-distribution","title":"Deep Statistical Solver for Distribution System State Estimation","date":"2023-01-04","arxiv_id":"2301.01835","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-video-as-stochastic-processes-for","slug":"modeling-video-as-stochastic-processes-for","title":"Modeling Video As Stochastic Processes for Fine-Grained Video Representation Learning","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/rankmix-data-augmentation-for-weakly","slug":"rankmix-data-augmentation-for-weakly","title":"RankMix: Data Augmentation for Weakly Supervised Learning of Classifying Whole Slide Images With Diverse Sizes and Imbalanced Categories","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-learning-of-semantic-1","slug":"weakly-supervised-learning-of-semantic-1","title":"Weakly Supervised Learning of Semantic Correspondence through Cascaded Online Correspondence Refinement","date":"2023-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/wecheck-strong-factual-consistency-checker","slug":"wecheck-strong-factual-consistency-checker","title":"WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning","date":"2022-12-20","arxiv_id":"2212.10057","repositories_listed":1,"syntology":null},{"url":"/paper/losses-over-labels-weakly-supervised-learning","slug":"losses-over-labels-weakly-supervised-learning","title":"Losses over Labels: Weakly Supervised Learning via Direct Loss Construction","date":"2022-12-13","arxiv_id":"2212.06921","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":0,"n_instrument":5,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":6,"phrase":"5 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; 5 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/losses-over-labels-weakly-supervised-learning#ran","syntology_url":"https://syntology.ai/paper/2212.06921","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.06921"}},"official":{"repos":["dsam99/lol"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/yolocurvseg-you-only-label-one-noisy-skeleton","slug":"yolocurvseg-you-only-label-one-noisy-skeleton","title":"YoloCurvSeg: You Only Label One Noisy Skeleton for Vessel-style Curvilinear Structure Segmentation","date":"2022-12-11","arxiv_id":"2212.05566","repositories_listed":1,"syntology":null},{"url":"/paper/towards-interpreting-vulnerability-of-multi","slug":"towards-interpreting-vulnerability-of-multi","title":"Interpreting Vulnerabilities of Multi-Instance Learning to Adversarial Perturbations","date":"2022-11-30","arxiv_id":"2211.17071","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-learning-significantly","slug":"weakly-supervised-learning-significantly","title":"Weakly Supervised Learning Significantly Reduces the Number of Labels Required for Intracranial Hemorrhage Detection on Head CT","date":"2022-11-29","arxiv_id":"2211.15924","repositories_listed":1,"syntology":null},{"url":"/paper/arnet-automatic-refinement-network-for-noisy","slug":"arnet-automatic-refinement-network-for-noisy","title":"IRNet: Iterative Refinement Network for Noisy Partial Label Learning","date":"2022-11-09","arxiv_id":"2211.04774","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/arnet-automatic-refinement-network-for-noisy#ran","syntology_url":"https://syntology.ai/paper/2211.04774","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.04774"}},"official":{"repos":["zeroqiaoba/irnet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/single-image-hdr-reconstruction-by-multi","slug":"single-image-hdr-reconstruction-by-multi","title":"Single-Image HDR Reconstruction by Multi-Exposure Generation","date":"2022-10-28","arxiv_id":"2210.15897","repositories_listed":1,"syntology":null},{"url":"/paper/sepll-separating-latent-class-labels-from","slug":"sepll-separating-latent-class-labels-from","title":"SepLL: Separating Latent Class Labels from Weak Supervision Noise","date":"2022-10-25","arxiv_id":"2210.13898","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-learning-for-analyzing","slug":"weakly-supervised-learning-for-analyzing","title":"Weakly Supervised Learning for Analyzing Political Campaigns on Facebook","date":"2022-10-19","arxiv_id":"2210.10669","repositories_listed":1,"syntology":null},{"url":"/paper/injecting-domain-knowledge-from-empirical","slug":"injecting-domain-knowledge-from-empirical","title":"Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties","date":"2022-10-14","arxiv_id":"2210.08047","repositories_listed":1,"syntology":null},{"url":"/paper/label-propagation-with-weak-supervision","slug":"label-propagation-with-weak-supervision","title":"Label Propagation with Weak Supervision","date":"2022-10-07","arxiv_id":"2210.03594","repositories_listed":1,"syntology":null},{"url":"/paper/coarse3d-class-prototypes-for-contrastive","slug":"coarse3d-class-prototypes-for-contrastive","title":"COARSE3D: Class-Prototypes for Contrastive Learning in Weakly-Supervised 3D Point Cloud Segmentation","date":"2022-10-04","arxiv_id":"2210.01784","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":2,"n_no_contract":3,"n_pointer_only":2,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 2 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/coarse3d-class-prototypes-for-contrastive#ran","syntology_url":"https://syntology.ai/paper/2210.01784","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.01784"}},"official":{"repos":["cv-rits/coarse3d"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/weakly-supervised-formula-learner-for-solving","slug":"weakly-supervised-formula-learner-for-solving","title":"Weakly Supervised Formula Learner for Solving Mathematical Problems","date":"2022-10-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/solar-sinkhorn-label-refinery-for-imbalanced","slug":"solar-sinkhorn-label-refinery-for-imbalanced","title":"SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning","date":"2022-09-21","arxiv_id":"2209.10365","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":1,"n_instrument":3,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/solar-sinkhorn-label-refinery-for-imbalanced#ran","syntology_url":"https://syntology.ai/paper/2209.10365","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.10365"}},"official":{"repos":["hbzju/solar"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/quasi-supervised-learning-for-super","slug":"quasi-supervised-learning-for-super","title":"Quasi-supervised Learning for Super-resolution PET","date":"2022-09-03","arxiv_id":"2209.01325","repositories_listed":1,"syntology":null},{"url":"/paper/tracking-by-weakly-supervised-learning-and","slug":"tracking-by-weakly-supervised-learning-and","title":"Tracking by weakly-supervised learning and graph optimization for whole-embryo C. elegans lineages","date":"2022-08-24","arxiv_id":"2208.11467","repositories_listed":1,"syntology":null},{"url":"/paper/pa-seg-learning-from-point-annotations-for-3d","slug":"pa-seg-learning-from-point-annotations-for-3d","title":"PA-Seg: Learning from Point Annotations for 3D Medical Image Segmentation using Contextual Regularization and Cross Knowledge Distillation","date":"2022-08-11","arxiv_id":"2208.05669","repositories_listed":1,"syntology":null},{"url":"/paper/dilated-context-integrated-network-with-cross","slug":"dilated-context-integrated-network-with-cross","title":"Dilated Context Integrated Network with Cross-Modal Consensus for Temporal Emotion Localization in Videos","date":"2022-08-03","arxiv_id":"2208.01954","repositories_listed":1,"syntology":null},{"url":"/paper/octave-2d-en-face-optical-coherence","slug":"octave-2d-en-face-optical-coherence","title":"OCTAve: 2D en face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning with Locality Augmentation","date":"2022-07-25","arxiv_id":"2207.12238","repositories_listed":1,"syntology":null},{"url":"/paper/computer-aided-tuberculosis-diagnosis-with","slug":"computer-aided-tuberculosis-diagnosis-with","title":"Computer-aided Tuberculosis Diagnosis with Attribute Reasoning Assistance","date":"2022-07-01","arxiv_id":"2207.00251","repositories_listed":1,"syntology":null},{"url":"/paper/feature-re-calibration-based-mil-for-whole","slug":"feature-re-calibration-based-mil-for-whole","title":"Feature Re-calibration based Multiple Instance Learning for Whole Slide Image Classification","date":"2022-06-22","arxiv_id":"2206.10878","repositories_listed":1,"syntology":null},{"url":"/paper/balancing-bias-and-variance-for-active-weakly","slug":"balancing-bias-and-variance-for-active-weakly","title":"Balancing Bias and Variance for Active Weakly Supervised Learning","date":"2022-06-12","arxiv_id":"2206.05682","repositories_listed":1,"syntology":null},{"url":"/paper/domain-adaptation-with-pre-trained-1","slug":"domain-adaptation-with-pre-trained-1","title":"Domain Adaptation with Pre-trained Transformers for Query-Focused Abstractive Text Summarization","date":"2022-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cigmo-categorical-invariant-representations","slug":"cigmo-categorical-invariant-representations","title":"CIGMO: Categorical invariant representations in a deep generative framework","date":"2022-05-27","arxiv_id":"2205.13758","repositories_listed":1,"syntology":null},{"url":"/paper/transformer-based-multiple-instance-learning","slug":"transformer-based-multiple-instance-learning","title":"Transformer based multiple instance learning for weakly supervised histopathology image segmentation","date":"2022-05-18","arxiv_id":"2205.08878","repositories_listed":1,"syntology":null},{"url":"/paper/prompt-based-rule-discovery-and-boosting-for","slug":"prompt-based-rule-discovery-and-boosting-for","title":"Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning","date":"2022-05-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ulf-unsupervised-labeling-function-correction","slug":"ulf-unsupervised-labeling-function-correction","title":"ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision","date":"2022-04-14","arxiv_id":"2204.06863","repositories_listed":1,"syntology":null},{"url":"/paper/decomposition-based-generation-process-for","slug":"decomposition-based-generation-process-for","title":"Decompositional Generation Process for Instance-Dependent Partial Label Learning","date":"2022-04-08","arxiv_id":"2204.03845","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":1,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"phrase":"4 ran (of which 1 constructed an object rather than computing a result; 4 with no instrument failure: 3 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/decomposition-based-generation-process-for#ran","syntology_url":"https://syntology.ai/paper/2204.03845","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.03845"}},"official":{"repos":["palm-ml/idgp"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/from-2d-images-to-3d-model-weakly-supervised","slug":"from-2d-images-to-3d-model-weakly-supervised","title":"From 2D Images to 3D Model:Weakly Supervised Multi-View Face Reconstruction with Deep Fusion","date":"2022-04-08","arxiv_id":"2204.03842","repositories_listed":1,"syntology":null},{"url":"/paper/semi-weakly-supervised-object-detection-by","slug":"semi-weakly-supervised-object-detection-by","title":"Semi-Weakly Supervised Object Detection by Sampling Pseudo Ground-Truth Boxes","date":"2022-04-01","arxiv_id":"2204.00147","repositories_listed":1,"syntology":null},{"url":"/paper/acknowledging-the-unknown-for-multi-label","slug":"acknowledging-the-unknown-for-multi-label","title":"Acknowledging the Unknown for Multi-label Learning with Single Positive Labels","date":"2022-03-30","arxiv_id":"2203.16219","repositories_listed":1,"syntology":null},{"url":"/paper/risk-consistent-multi-class-learning-from","slug":"risk-consistent-multi-class-learning-from","title":"Learning from Label Proportions with Instance-wise Consistency","date":"2022-03-24","arxiv_id":"2203.12836","repositories_listed":1,"syntology":null},{"url":"/paper/prboost-prompt-based-rule-discovery-and","slug":"prboost-prompt-based-rule-discovery-and","title":"PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning","date":"2022-03-18","arxiv_id":"2203.09735","repositories_listed":1,"syntology":null},{"url":"/paper/probabilistic-warp-consistency-for-weakly","slug":"probabilistic-warp-consistency-for-weakly","title":"Probabilistic Warp Consistency for Weakly-Supervised Semantic Correspondences","date":"2022-03-08","arxiv_id":"2203.04279","repositories_listed":1,"syntology":{"n":15,"n_ran":13,"n_constructed":1,"n_ran_checked":7,"n_instrument":6,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":15,"phrase":"13 ran (of which 1 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 6 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/probabilistic-warp-consistency-for-weakly#ran","syntology_url":"https://syntology.ai/paper/2203.04279","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.04279"}},"official":{"repos":["PruneTruong/DenseMatching"],"state":"official (archive's flag): 13 ran","n_ran":13,"n_constructed":1,"n_ran_no_instrument_failure":7,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/comparison-of-spatio-temporal-models-for","slug":"comparison-of-spatio-temporal-models-for","title":"Comparison of Spatio-Temporal Models for Human Motion and Pose Forecasting in Face-to-Face Interaction Scenarios","date":"2022-03-07","arxiv_id":"2203.03245","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/continuous-relaxation-for-the-multivariate","slug":"continuous-relaxation-for-the-multivariate","title":"Learning Group Importance using the Differentiable Hypergeometric Distribution","date":"2022-03-03","arxiv_id":"2203.01629","repositories_listed":1,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/continuous-relaxation-for-the-multivariate#ran","syntology_url":"https://syntology.ai/paper/2203.01629","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.01629"}},"official":{"repos":["thomassutter/mvhg"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/bag-graph-multiple-instance-learning-using","slug":"bag-graph-multiple-instance-learning-using","title":"Bag Graph: Multiple Instance Learning using Bayesian Graph Neural Networks","date":"2022-02-22","arxiv_id":"2202.11132","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/bag-graph-multiple-instance-learning-using#ran","syntology_url":"https://syntology.ai/paper/2202.11132","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.11132"}},"official":{"repos":["networkslab/baggraph"],"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/label-propagation-for-annotation-efficient","slug":"label-propagation-for-annotation-efficient","title":"Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training","date":"2022-02-16","arxiv_id":"2202.08195","repositories_listed":1,"syntology":null},{"url":"/paper/decoupling-makes-weakly-supervised-local","slug":"decoupling-makes-weakly-supervised-local","title":"Decoupling Makes Weakly Supervised Local Feature Better","date":"2022-01-08","arxiv_id":"2201.02861","repositories_listed":1,"syntology":null},{"url":"/paper/negative-evidence-matters-in-interpretable","slug":"negative-evidence-matters-in-interpretable","title":"Negative Evidence Matters in Interpretable Histology Image Classification","date":"2022-01-07","arxiv_id":"2201.02445","repositories_listed":1,"syntology":null},{"url":"/paper/semi-weakly-supervised-learning-of-complex","slug":"semi-weakly-supervised-learning-of-complex","title":"Semi-Weakly-Supervised Learning of Complex Actions From Instructional Task Videos","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-segmentation-on-outdoor-4d","slug":"weakly-supervised-segmentation-on-outdoor-4d","title":"Weakly Supervised Segmentation on Outdoor 4D Point Clouds With Temporal Matching and Spatial Graph Propagation","date":"2022-01-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/domain-adaptation-with-pre-trained","slug":"domain-adaptation-with-pre-trained","title":"Domain Adaptation with Pre-trained Transformers for Query Focused Abstractive Text Summarization","date":"2021-12-22","arxiv_id":"2112.11670","repositories_listed":1,"syntology":null},{"url":"/paper/evaluation-and-comparison-of-deep-learning","slug":"evaluation-and-comparison-of-deep-learning","title":"Evaluation and Comparison of Deep Learning Methods for Pavement Crack Identification with Visual Images","date":"2021-12-20","arxiv_id":"2112.10390","repositories_listed":1,"syntology":null},{"url":"/paper/deciphering-antibody-affinity-maturation-with","slug":"deciphering-antibody-affinity-maturation-with","title":"Deciphering antibody affinity maturation with language models and weakly supervised learning","date":"2021-12-14","arxiv_id":"2112.07782","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":1,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 1 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) · 0 unverified","sample_list":"/paper/deciphering-antibody-affinity-maturation-with#ran","syntology_url":"https://syntology.ai/paper/2112.07782","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.07782"}},"official":null}},{"url":"/paper/learning-to-generate-visual-questions-with","slug":"learning-to-generate-visual-questions-with","title":"Learning to Generate Visual Questions with Noisy Supervision","date":"2021-12-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/cerberus-transformer-joint-semantic","slug":"cerberus-transformer-joint-semantic","title":"Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing","date":"2021-11-24","arxiv_id":"2111.12608","repositories_listed":1,"syntology":null},{"url":"/paper/one-shot-weakly-supervised-segmentation-in","slug":"one-shot-weakly-supervised-segmentation-in","title":"One-shot Weakly-Supervised Segmentation in Medical Images","date":"2021-11-21","arxiv_id":"2111.10773","repositories_listed":1,"syntology":null},{"url":"/paper/nested-multiple-instance-learning-with","slug":"nested-multiple-instance-learning-with","title":"Nested Multiple Instance Learning with Attention Mechanisms","date":"2021-11-01","arxiv_id":"2111.00947","repositories_listed":1,"syntology":null},{"url":"/paper/instance-dependent-partial-label-learning","slug":"instance-dependent-partial-label-learning","title":"Instance-Dependent Partial Label Learning","date":"2021-10-25","arxiv_id":"2110.12911","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-human-object-interaction-1","slug":"weakly-supervised-human-object-interaction-1","title":"Weakly Supervised Human-Object Interaction Detection in Video via Contrastive Spatiotemporal Regions","date":"2021-10-07","arxiv_id":"2110.03562","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-human-object-interaction-1#ran","syntology_url":"https://syntology.ai/paper/2110.03562","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.03562"}},"official":{"repos":["ShuangLI59/weakly-supervised-human-object-detection-video"],"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/weak-novel-categories-without-tears-a-survey","slug":"weak-novel-categories-without-tears-a-survey","title":"Weak Novel Categories without Tears: A Survey on Weak-Shot Learning","date":"2021-10-06","arxiv_id":"2110.02651","repositories_listed":1,"syntology":null},{"url":"/paper/knowman-weakly-supervised-multinomial","slug":"knowman-weakly-supervised-multinomial","title":"KnowMAN: Weakly Supervised Multinomial Adversarial Networks","date":"2021-09-16","arxiv_id":"2109.07994","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/knowman-weakly-supervised-multinomial#ran","syntology_url":"https://syntology.ai/paper/2109.07994","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.07994"}},"official":{"repos":["luisamaerz/knowman"],"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/a-three-stage-learning-framework-for-low","slug":"a-three-stage-learning-framework-for-low","title":"A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation","date":"2021-09-09","arxiv_id":"2109.04096","repositories_listed":1,"syntology":{"n":27,"n_ran":18,"n_constructed":0,"n_ran_checked":12,"n_instrument":6,"n_unverified":9,"n_honours":0,"n_violates":1,"n_no_contract":11,"n_pointer_only":5,"phrase":"18 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 1 violated, 11 with no contract checked; 6 where Syntology's instrument failed) · 9 unverified","sample_list":"/paper/a-three-stage-learning-framework-for-low#ran","syntology_url":"https://syntology.ai/paper/2109.04096","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.04096"}},"official":{"repos":["neukg/kat-tslf"],"state":"official (archive's flag): 18 ran","n_ran":18,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":9,"ran_from_kinds":["official"]}}},{"url":"/paper/self-supervised-multi-scale-consistency-for","slug":"self-supervised-multi-scale-consistency-for","title":"Self-supervised Multi-scale Consistency for Weakly Supervised Segmentation Learning","date":"2021-08-26","arxiv_id":"2108.11900","repositories_listed":1,"syntology":null},{"url":"/paper/non-i-i-d-multi-instance-learning-for-1","slug":"non-i-i-d-multi-instance-learning-for-1","title":"Non-i.i.d. multi-instance learning for predicting instance and bag labels using variational autoencoder","date":"2021-08-19","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-3d-human-pose-estimation-with","slug":"self-supervised-3d-human-pose-estimation-with","title":"Self-Supervised 3D Human Pose Estimation with Multiple-View Geometry","date":"2021-08-17","arxiv_id":"2108.07777","repositories_listed":1,"syntology":null},{"url":"/paper/eproduct-a-million-scale-visual-search","slug":"eproduct-a-million-scale-visual-search","title":"eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition Challenges","date":"2021-07-13","arxiv_id":"2107.05856","repositories_listed":1,"syntology":null},{"url":"/paper/leveraged-weighted-loss-for-partial-label-1","slug":"leveraged-weighted-loss-for-partial-label-1","title":"Leveraged Weighted Loss for Partial Label Learning","date":"2021-06-10","arxiv_id":"2106.05731","repositories_listed":1,"syntology":null},{"url":"/paper/a-new-gastric-histopathology-subsize-image","slug":"a-new-gastric-histopathology-subsize-image","title":"GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancer","date":"2021-06-04","arxiv_id":"2106.02473","repositories_listed":1,"syntology":null},{"url":"/paper/understanding-the-dynamics-between-vaping-and","slug":"understanding-the-dynamics-between-vaping-and","title":"Understanding the Dynamics between Vaping and Cannabis Legalization Using Twitter Opinions","date":"2021-06-04","arxiv_id":"2106.11029","repositories_listed":1,"syntology":null},{"url":"/paper/tar-generalized-forensic-framework-to-detect","slug":"tar-generalized-forensic-framework-to-detect","title":"TAR: Generalized Forensic Framework to Detect Deepfakes using Weakly Supervised Learning","date":"2021-05-13","arxiv_id":"2105.06117","repositories_listed":1,"syntology":null},{"url":"/paper/mil-benchmarks-standardized-evaluation-of","slug":"mil-benchmarks-standardized-evaluation-of","title":"mil-benchmarks: Standardized Evaluation of Deep Multiple-Instance Learning Techniques","date":"2021-05-04","arxiv_id":"2105.01443","repositories_listed":1,"syntology":null},{"url":"/paper/non-i-i-d-multi-instance-learning-for","slug":"non-i-i-d-multi-instance-learning-for","title":"Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels using Variational Auto-Encoder","date":"2021-05-04","arxiv_id":"2105.01276","repositories_listed":1,"syntology":null},{"url":"/paper/knodle-modular-weakly-supervised-learning","slug":"knodle-modular-weakly-supervised-learning","title":"Knodle: Modular Weakly Supervised Learning with PyTorch","date":"2021-04-23","arxiv_id":"2104.11557","repositories_listed":1,"syntology":null},{"url":"/paper/seed-word-selection-for-weakly-supervised","slug":"seed-word-selection-for-weakly-supervised","title":"Seed Word Selection for Weakly-Supervised Text Classification with Unsupervised Error Estimation","date":"2021-04-20","arxiv_id":"2104.09765","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-visual-engagement-signals-for","slug":"exploring-visual-engagement-signals-for","title":"Exploring Visual Engagement Signals for Representation Learning","date":"2021-04-15","arxiv_id":"2104.07767","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/exploring-visual-engagement-signals-for#ran","syntology_url":"https://syntology.ai/paper/2104.07767","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.07767"}},"official":{"repos":["KMnP/vise"],"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"]}}},{"url":"/paper/weakly-supervised-segmentation-with-cross","slug":"weakly-supervised-segmentation-with-cross","title":"Weakly supervised segmentation with cross-modality equivariant constraints","date":"2021-04-06","arxiv_id":"2104.02488","repositories_listed":1,"syntology":null},{"url":"/paper/relation-aware-instance-refinement-for-weakly","slug":"relation-aware-instance-refinement-for-weakly","title":"Relation-aware Instance Refinement for Weakly Supervised Visual Grounding","date":"2021-03-24","arxiv_id":"2103.12989","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"3 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; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/relation-aware-instance-refinement-for-weakly#ran","syntology_url":"https://syntology.ai/paper/2103.12989","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.12989"}},"official":{"repos":["youngfly11/ReIR-WeaklyGrounding.pytorch"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-class-agnostic-pseudo-mask","slug":"learning-class-agnostic-pseudo-mask","title":"Learning Class-Agnostic Pseudo Mask Generation for Box-Supervised Semantic Segmentation","date":"2021-03-09","arxiv_id":"2103.05463","repositories_listed":1,"syntology":null},{"url":"/paper/towards-unbiased-covid-19-lesion-localisation","slug":"towards-unbiased-covid-19-lesion-localisation","title":"Towards Unbiased COVID-19 Lesion Localisation and Segmentation via Weakly Supervised Learning","date":"2021-03-01","arxiv_id":"2103.00780","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-learning-of-rigid-3d-scene","slug":"weakly-supervised-learning-of-rigid-3d-scene","title":"Weakly Supervised Learning of Rigid 3D Scene Flow","date":"2021-02-17","arxiv_id":"2102.08945","repositories_listed":1,"syntology":null},{"url":"/paper/disambiguation-of-weak-supervision-with","slug":"disambiguation-of-weak-supervision-with","title":"Disambiguation of weak supervision with exponential convergence rates","date":"2021-02-04","arxiv_id":"2102.02789","repositories_listed":1,"syntology":null}],"record_sha256":"806a9193a8fb459880f096ea407db72356005a4b2f301ccaecd1560144deb249","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}