{"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/30","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":30,"pages_in_order":148,"rows_per_page":100,"rows":[2901,3000],"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/29","next":"/task/semantic-segmentation/papers/31","papers":[{"url":"/paper/fusenet-self-supervised-dual-path-network-for","slug":"fusenet-self-supervised-dual-path-network-for","title":"FuseNet: Self-Supervised Dual-Path Network for Medical Image Segmentation","date":"2023-11-22","arxiv_id":"2311.13069","repositories_listed":1,"syntology":null},{"url":"/paper/sam4udass-when-sam-meets-unsupervised-domain","slug":"sam4udass-when-sam-meets-unsupervised-domain","title":"SAM4UDASS: When SAM Meets Unsupervised Domain Adaptive Semantic Segmentation in Intelligent Vehicles","date":"2023-11-22","arxiv_id":"2401.08604","repositories_listed":1,"syntology":null},{"url":"/paper/segvol-universal-and-interactive-volumetric","slug":"segvol-universal-and-interactive-volumetric","title":"SegVol: Universal and Interactive Volumetric Medical Image Segmentation","date":"2023-11-22","arxiv_id":"2311.13385","repositories_listed":1,"syntology":{"n":17,"n_ran":11,"n_constructed":0,"n_ran_checked":7,"n_instrument":4,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 4 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/segvol-universal-and-interactive-volumetric#ran","syntology_url":"https://syntology.ai/paper/2311.13385","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.13385"}},"official":{"repos":["baai-dcai/segvol"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/hover-unet-accelerating-hovernet-with-unet","slug":"hover-unet-accelerating-hovernet-with-unet","title":"HoVer-UNet: Accelerating HoVerNet with UNet-based multi-class nuclei segmentation via knowledge distillation","date":"2023-11-21","arxiv_id":"2311.12553","repositories_listed":1,"syntology":null},{"url":"/paper/mobile-seed-joint-semantic-segmentation-and","slug":"mobile-seed-joint-semantic-segmentation-and","title":"Mobile-Seed: Joint Semantic Segmentation and Boundary Detection for Mobile Robots","date":"2023-11-21","arxiv_id":"2311.12651","repositories_listed":1,"syntology":null},{"url":"/paper/q-seg-quantum-annealing-based-unsupervised","slug":"q-seg-quantum-annealing-based-unsupervised","title":"Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentation","date":"2023-11-21","arxiv_id":"2311.12912","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-medical-image-segmentation-5","slug":"semi-supervised-medical-image-segmentation-5","title":"Semi-supervised Medical Image Segmentation via Query Distribution Consistency","date":"2023-11-21","arxiv_id":"2311.12364","repositories_listed":1,"syntology":null},{"url":"/paper/generalized-category-discovery-in-semantic","slug":"generalized-category-discovery-in-semantic","title":"Generalized Category Discovery in Semantic Segmentation","date":"2023-11-20","arxiv_id":"2311.11525","repositories_listed":1,"syntology":null},{"url":"/paper/kandinsky-conformal-prediction-efficient","slug":"kandinsky-conformal-prediction-efficient","title":"Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithms","date":"2023-11-20","arxiv_id":"2311.11837","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/kandinsky-conformal-prediction-efficient#ran","syntology_url":"https://syntology.ai/paper/2311.11837","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.11837"}},"official":{"repos":["NKI-AI/kandinsky-calibration"],"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/labelmaker-automatic-semantic-label","slug":"labelmaker-automatic-semantic-label","title":"LABELMAKER: Automatic Semantic Label Generation from RGB-D Trajectories","date":"2023-11-20","arxiv_id":"2311.12174","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":0,"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/labelmaker-automatic-semantic-label#ran","syntology_url":"https://syntology.ai/paper/2311.12174","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.12174"}},"official":{"repos":["cvg/labelmaker"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/model-aware-3d-eye-gaze-from-weak-and-few","slug":"model-aware-3d-eye-gaze-from-weak-and-few","title":"Model-aware 3D Eye Gaze from Weak and Few-shot Supervisions","date":"2023-11-20","arxiv_id":"2311.12157","repositories_listed":1,"syntology":null},{"url":"/paper/sa-med2d-20m-dataset-segment-anything-in-2d","slug":"sa-med2d-20m-dataset-segment-anything-in-2d","title":"SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks","date":"2023-11-20","arxiv_id":"2311.11969","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":2,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/sa-med2d-20m-dataset-segment-anything-in-2d#ran","syntology_url":"https://syntology.ai/paper/2311.11969","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.11969"}},"official":{"repos":["OpenGVLab/SAM-Med2D"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/geosam-fine-tuning-sam-with-sparse-and-dense","slug":"geosam-fine-tuning-sam-with-sparse-and-dense","title":"GeoSAM: Fine-tuning SAM with Multi-Modal Prompts for Mobility Infrastructure Segmentation","date":"2023-11-19","arxiv_id":"2311.11319","repositories_listed":1,"syntology":null},{"url":"/paper/open-vocabulary-camouflaged-object","slug":"open-vocabulary-camouflaged-object","title":"Open-Vocabulary Camouflaged Object Segmentation","date":"2023-11-19","arxiv_id":"2311.11241","repositories_listed":1,"syntology":null},{"url":"/paper/pseudo-label-guided-data-fusion-and-output","slug":"pseudo-label-guided-data-fusion-and-output","title":"Pseudo Label-Guided Data Fusion and Output Consistency for Semi-Supervised Medical Image Segmentation","date":"2023-11-17","arxiv_id":"2311.10349","repositories_listed":1,"syntology":null},{"url":"/paper/shifting-to-machine-supervision-annotation","slug":"shifting-to-machine-supervision-annotation","title":"Shifting to Machine Supervision: Annotation-Efficient Semi and Self-Supervised Learning for Automatic Medical Image Segmentation and Classification","date":"2023-11-17","arxiv_id":"2311.10319","repositories_listed":1,"syntology":null},{"url":"/paper/unimos-a-universal-framework-for-multi-organ","slug":"unimos-a-universal-framework-for-multi-organ","title":"UniMOS: A Universal Framework For Multi-Organ Segmentation Over Label-Constrained Datasets","date":"2023-11-17","arxiv_id":"2311.10251","repositories_listed":1,"syntology":null},{"url":"/paper/versatile-medical-image-segmentation-learned","slug":"versatile-medical-image-segmentation-learned","title":"Versatile Medical Image Segmentation Learned from Multi-Source Datasets via Model Self-Disambiguation","date":"2023-11-17","arxiv_id":"2311.10696","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-overconfidence-problem-in-semantic-3d","slug":"on-the-overconfidence-problem-in-semantic-3d","title":"On the Overconfidence Problem in Semantic 3D Mapping","date":"2023-11-16","arxiv_id":"2311.10018","repositories_listed":1,"syntology":null},{"url":"/paper/pwiseg-point-based-weakly-supervised-instance","slug":"pwiseg-point-based-weakly-supervised-instance","title":"PWISeg: Point-based Weakly-supervised Instance Segmentation for Surgical Instruments","date":"2023-11-16","arxiv_id":"2311.09819","repositories_listed":1,"syntology":null},{"url":"/paper/samihs-adaptation-of-segment-anything-model","slug":"samihs-adaptation-of-segment-anything-model","title":"SAMIHS: Adaptation of Segment Anything Model for Intracranial Hemorrhage Segmentation","date":"2023-11-14","arxiv_id":"2311.08190","repositories_listed":1,"syntology":null},{"url":"/paper/test-time-training-for-semantic-segmentation","slug":"test-time-training-for-semantic-segmentation","title":"Test-Time Training for Semantic Segmentation with Output Contrastive Loss","date":"2023-11-14","arxiv_id":"2311.07877","repositories_listed":1,"syntology":null},{"url":"/paper/uslr-an-open-source-tool-for-unbiased-and","slug":"uslr-an-open-source-tool-for-unbiased-and","title":"USLR: an open-source tool for unbiased and smooth longitudinal registration of brain MR","date":"2023-11-14","arxiv_id":"2311.08371","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-segmentation-of-eye-features-using","slug":"zero-shot-segmentation-of-eye-features-using","title":"Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)","date":"2023-11-14","arxiv_id":"2311.08077","repositories_listed":1,"syntology":null},{"url":"/paper/assessing-test-time-variability-for","slug":"assessing-test-time-variability-for","title":"Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts","date":"2023-11-13","arxiv_id":"2311.07806","repositories_listed":1,"syntology":null},{"url":"/paper/spectralgpt-spectral-foundation-model","slug":"spectralgpt-spectral-foundation-model","title":"SpectralGPT: Spectral Remote Sensing Foundation Model","date":"2023-11-13","arxiv_id":"2311.07113","repositories_listed":1,"syntology":null},{"url":"/paper/automatic-report-generation-for","slug":"automatic-report-generation-for","title":"Automatic Report Generation for Histopathology images using pre-trained Vision Transformers","date":"2023-11-10","arxiv_id":"2311.06176","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-segmentation-with-texture-in-ore","slug":"efficient-segmentation-with-texture-in-ore","title":"Efficient Segmentation with Texture in Ore Images Based on Box-supervised Approach","date":"2023-11-10","arxiv_id":"2311.05929","repositories_listed":1,"syntology":null},{"url":"/paper/polymax-general-dense-prediction-with-mask","slug":"polymax-general-dense-prediction-with-mask","title":"PolyMaX: General Dense Prediction with Mask Transformer","date":"2023-11-09","arxiv_id":"2311.05770","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"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 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) · 0 unverified","sample_list":"/paper/polymax-general-dense-prediction-with-mask#ran","syntology_url":"https://syntology.ai/paper/2311.05770","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.05770"}},"official":{"repos":["google-research/deeplab2"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/single-shot-tomography-of-discrete-dynamic","slug":"single-shot-tomography-of-discrete-dynamic","title":"Single-shot Tomography of Discrete Dynamic Objects","date":"2023-11-09","arxiv_id":"2311.05269","repositories_listed":1,"syntology":null},{"url":"/paper/csam-a-2-5d-cross-slice-attention-module-for","slug":"csam-a-2-5d-cross-slice-attention-module-for","title":"CSAM: A 2.5D Cross-Slice Attention Module for Anisotropic Volumetric Medical Image Segmentation","date":"2023-11-08","arxiv_id":"2311.04942","repositories_listed":1,"syntology":null},{"url":"/paper/lidar-annotation-is-all-you-need","slug":"lidar-annotation-is-all-you-need","title":"Lidar Annotation Is All You Need","date":"2023-11-08","arxiv_id":"2311.04777","repositories_listed":1,"syntology":null},{"url":"/paper/data-exploitation-multi-task-learning-of","slug":"data-exploitation-multi-task-learning-of","title":"Data exploitation: multi-task learning of object detection and semantic segmentation on partially annotated data","date":"2023-11-07","arxiv_id":"2311.04040","repositories_listed":1,"syntology":null},{"url":"/paper/video-instance-matting","slug":"video-instance-matting","title":"Video Instance Matting","date":"2023-11-07","arxiv_id":"2311.04212","repositories_listed":1,"syntology":null},{"url":"/paper/glamm-pixel-grounding-large-multimodal-model","slug":"glamm-pixel-grounding-large-multimodal-model","title":"GLaMM: Pixel Grounding Large Multimodal Model","date":"2023-11-06","arxiv_id":"2311.03356","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/glamm-pixel-grounding-large-multimodal-model#ran","syntology_url":"https://syntology.ai/paper/2311.03356","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.03356"}},"official":{"repos":["mbzuai-oryx/groundingLMM"],"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/masking-hyperspectral-imaging-data-with","slug":"masking-hyperspectral-imaging-data-with","title":"Masking Hyperspectral Imaging Data with Pretrained Models","date":"2023-11-06","arxiv_id":"2311.03053","repositories_listed":1,"syntology":null},{"url":"/paper/truly-scale-equivariant-deep-nets-with-1","slug":"truly-scale-equivariant-deep-nets-with-1","title":"Truly Scale-Equivariant Deep Nets with Fourier Layers","date":"2023-11-06","arxiv_id":"2311.02922","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/truly-scale-equivariant-deep-nets-with-1#ran","syntology_url":"https://syntology.ai/paper/2311.02922","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.02922"}},"official":{"repos":["ashiq24/scale_equivarinat_fourier_layer"],"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/tsp-transformer-task-specific-prompts-boosted","slug":"tsp-transformer-task-specific-prompts-boosted","title":"TSP-Transformer: Task-Specific Prompts Boosted Transformer for Holistic Scene Understanding","date":"2023-11-06","arxiv_id":"2311.03427","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-enhancement-of-low-light-image","slug":"zero-shot-enhancement-of-low-light-image","title":"Zero-Shot Enhancement of Low-Light Image Based on Retinex Decomposition","date":"2023-11-06","arxiv_id":"2311.02995","repositories_listed":1,"syntology":null},{"url":"/paper/adapting-segment-anything-model-sam-through","slug":"adapting-segment-anything-model-sam-through","title":"Adapting Segment Anything Model (SAM) through Prompt-based Learning for Enhanced Protein Identification in Cryo-EM Micrographs","date":"2023-11-04","arxiv_id":"2311.16140","repositories_listed":1,"syntology":null},{"url":"/paper/emernerf-emergent-spatial-temporal-scene","slug":"emernerf-emergent-spatial-temporal-scene","title":"EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision","date":"2023-11-03","arxiv_id":"2311.02077","repositories_listed":1,"syntology":null},{"url":"/paper/fairseg-a-large-scale-medical-image","slug":"fairseg-a-large-scale-medical-image","title":"FairSeg: A Large-Scale Medical Image Segmentation Dataset for Fairness Learning Using Segment Anything Model with Fair Error-Bound Scaling","date":"2023-11-03","arxiv_id":"2311.02189","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"5 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fairseg-a-large-scale-medical-image#ran","syntology_url":"https://syntology.ai/paper/2311.02189","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.02189"}},"official":{"repos":["harvard-ophthalmology-ai-lab/fairseg"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/minesegsat-an-automated-system-to-evaluate","slug":"minesegsat-an-automated-system-to-evaluate","title":"MineSegSAT: An automated system to evaluate mining disturbed area extents from Sentinel-2 imagery","date":"2023-11-03","arxiv_id":"2311.01676","repositories_listed":1,"syntology":null},{"url":"/paper/ailurus-a-scalable-vit-framework-for-dense-1","slug":"ailurus-a-scalable-vit-framework-for-dense-1","title":"AiluRus: A Scalable ViT Framework for Dense Prediction","date":"2023-11-02","arxiv_id":"2311.01197","repositories_listed":1,"syntology":null},{"url":"/paper/hybrid-fusion-transformer-for-multisequence","slug":"hybrid-fusion-transformer-for-multisequence","title":"Hybrid-Fusion Transformer for Multisequence MRI","date":"2023-11-02","arxiv_id":"2311.01308","repositories_listed":1,"syntology":null},{"url":"/paper/continual-atlas-based-segmentation-of","slug":"continual-atlas-based-segmentation-of","title":"Continual atlas-based segmentation of prostate MRI","date":"2023-11-01","arxiv_id":"2311.00548","repositories_listed":1,"syntology":null},{"url":"/paper/bilateral-network-with-residual-u-blocks-and","slug":"bilateral-network-with-residual-u-blocks-and","title":"Bilateral Network with Residual U-blocks and Dual-Guided Attention for Real-time Semantic Segmentation","date":"2023-10-31","arxiv_id":"2310.20305","repositories_listed":1,"syntology":null},{"url":"/paper/from-denoising-training-to-test-time","slug":"from-denoising-training-to-test-time","title":"From Denoising Training to Test-Time Adaptation: Enhancing Domain Generalization for Medical Image Segmentation","date":"2023-10-31","arxiv_id":"2310.20271","repositories_listed":1,"syntology":null},{"url":"/paper/raising-the-class-of-streaming-time-series","slug":"raising-the-class-of-streaming-time-series","title":"Raising the ClaSS of Streaming Time Series Segmentation","date":"2023-10-31","arxiv_id":"2310.20431","repositories_listed":1,"syntology":null},{"url":"/paper/mist-medical-image-segmentation-transformer","slug":"mist-medical-image-segmentation-transformer","title":"MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder","date":"2023-10-30","arxiv_id":"2310.19898","repositories_listed":1,"syntology":null},{"url":"/paper/modular-anti-noise-deep-learning-network-for","slug":"modular-anti-noise-deep-learning-network-for","title":"Modular Anti-noise Deep Learning Network for Robotic Grasp Detection Based on RGB Images","date":"2023-10-30","arxiv_id":"2310.19223","repositories_listed":1,"syntology":null},{"url":"/paper/promise-prompt-driven-3d-medical-image","slug":"promise-prompt-driven-3d-medical-image","title":"Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models","date":"2023-10-30","arxiv_id":"2310.19721","repositories_listed":1,"syntology":null},{"url":"/paper/resource-constrained-semantic-segmentation","slug":"resource-constrained-semantic-segmentation","title":"Resource Constrained Semantic Segmentation for Waste Sorting","date":"2023-10-30","arxiv_id":"2310.19407","repositories_listed":1,"syntology":null},{"url":"/paper/transxnet-learning-both-global-and-local","slug":"transxnet-learning-both-global-and-local","title":"TransXNet: Learning Both Global and Local Dynamics with a Dual Dynamic Token Mixer for Visual Recognition","date":"2023-10-30","arxiv_id":"2310.19380","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-task-and-weight-prioritization","slug":"dynamic-task-and-weight-prioritization","title":"Dynamic Task and Weight Prioritization Curriculum Learning for Multimodal Imagery","date":"2023-10-29","arxiv_id":"2310.19109","repositories_listed":1,"syntology":null},{"url":"/paper/mask-propagation-for-efficient-video-semantic-1","slug":"mask-propagation-for-efficient-video-semantic-1","title":"Mask Propagation for Efficient Video Semantic Segmentation","date":"2023-10-29","arxiv_id":"2310.18954","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"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 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) · 1 unverified","sample_list":"/paper/mask-propagation-for-efficient-video-semantic-1#ran","syntology_url":"https://syntology.ai/paper/2310.18954","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.18954"}},"official":{"repos":["ziplab/mpvss"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/uncovering-prototypical-knowledge-for-weakly","slug":"uncovering-prototypical-knowledge-for-weakly","title":"Uncovering Prototypical Knowledge for Weakly Open-Vocabulary Semantic Segmentation","date":"2023-10-29","arxiv_id":"2310.19001","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":6,"phrase":"5 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/uncovering-prototypical-knowledge-for-weakly#ran","syntology_url":"https://syntology.ai/paper/2310.19001","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.19001"}},"official":null}},{"url":"/paper/audio-visual-instance-segmentation","slug":"audio-visual-instance-segmentation","title":"Audio-Visual Instance Segmentation","date":"2023-10-28","arxiv_id":"2310.18709","repositories_listed":1,"syntology":null},{"url":"/paper/caris-context-augmented-referring-image","slug":"caris-context-augmented-referring-image","title":"CARIS: Context-Augmented Referring Image Segmentation","date":"2023-10-27","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/instance-segmentation-under-occlusions-via","slug":"instance-segmentation-under-occlusions-via","title":"Instance Segmentation under Occlusions via Location-aware Copy-Paste Data Augmentation","date":"2023-10-27","arxiv_id":"2310.17949","repositories_listed":1,"syntology":null},{"url":"/paper/smooseg-smoothness-prior-for-unsupervised-1","slug":"smooseg-smoothness-prior-for-unsupervised-1","title":"SmooSeg: Smoothness Prior for Unsupervised Semantic Segmentation","date":"2023-10-27","arxiv_id":"2310.17874","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":7,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/smooseg-smoothness-prior-for-unsupervised-1#ran","syntology_url":"https://syntology.ai/paper/2310.17874","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.17874"}},"official":{"repos":["mc-lan/smooseg"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-deep-learning-approach-to-teeth","slug":"a-deep-learning-approach-to-teeth","title":"A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-rays","date":"2023-10-26","arxiv_id":"2310.17176","repositories_listed":1,"syntology":null},{"url":"/paper/bevcontrast-self-supervision-in-bev-space-for","slug":"bevcontrast-self-supervision-in-bev-space-for","title":"BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds","date":"2023-10-26","arxiv_id":"2310.17281","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/bevcontrast-self-supervision-in-bev-space-for#ran","syntology_url":"https://syntology.ai/paper/2310.17281","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.17281"}},"official":{"repos":["valeoai/bevcontrast"],"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/enhancing-sea-ice-segmentation-in-sentinel-1","slug":"enhancing-sea-ice-segmentation-in-sentinel-1","title":"Enhancing sea ice segmentation in Sentinel-1 images with atrous convolutions","date":"2023-10-26","arxiv_id":"2310.17122","repositories_listed":1,"syntology":null},{"url":"/paper/revisiting-the-distillation-of-image","slug":"revisiting-the-distillation-of-image","title":"Three Pillars improving Vision Foundation Model Distillation for Lidar","date":"2023-10-26","arxiv_id":"2310.17504","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"5 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/revisiting-the-distillation-of-image#ran","syntology_url":"https://syntology.ai/paper/2310.17504","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.17504"}},"official":{"repos":["valeoai/scalr"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/torchdistill-meets-hugging-face-libraries-for","slug":"torchdistill-meets-hugging-face-libraries-for","title":"torchdistill Meets Hugging Face Libraries for Reproducible, Coding-Free Deep Learning Studies: A Case Study on NLP","date":"2023-10-26","arxiv_id":"2310.17644","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-weighted-loss-functions-for","slug":"uncertainty-weighted-loss-functions-for","title":"Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation","date":"2023-10-26","arxiv_id":"2310.17436","repositories_listed":1,"syntology":null},{"url":"/paper/robust-source-free-domain-adaptation-for","slug":"robust-source-free-domain-adaptation-for","title":"Robust Source-Free Domain Adaptation for Fundus Image Segmentation","date":"2023-10-25","arxiv_id":"2310.16665","repositories_listed":1,"syntology":null},{"url":"/paper/using-diffusion-models-to-generate-synthetic","slug":"using-diffusion-models-to-generate-synthetic","title":"Using Diffusion Models to Generate Synthetic Labelled Data for Medical Image Segmentation","date":"2023-10-25","arxiv_id":"2310.16794","repositories_listed":1,"syntology":null},{"url":"/paper/anatomically-aware-uncertainty-for-semi","slug":"anatomically-aware-uncertainty-for-semi","title":"Anatomically-aware Uncertainty for Semi-supervised Image Segmentation","date":"2023-10-24","arxiv_id":"2310.16099","repositories_listed":1,"syntology":null},{"url":"/paper/g-cascade-efficient-cascaded-graph","slug":"g-cascade-efficient-cascaded-graph","title":"G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation","date":"2023-10-24","arxiv_id":"2310.16175","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":9,"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) · 3 unverified","sample_list":"/paper/g-cascade-efficient-cascaded-graph#ran","syntology_url":"https://syntology.ai/paper/2310.16175","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.16175"}},"official":{"repos":["SLDGroup/G-CASCADE"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/gnesf-generalizable-neural-semantic-fields","slug":"gnesf-generalizable-neural-semantic-fields","title":"GNeSF: Generalizable Neural Semantic Fields","date":"2023-10-24","arxiv_id":"2310.15712","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":2,"n_instrument":5,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":9,"phrase":"7 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; 5 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/gnesf-generalizable-neural-semantic-fields#ran","syntology_url":"https://syntology.ai/paper/2310.15712","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.15712"}},"official":null}},{"url":"/paper/sea-land-cloud-segmentation-in-satellite","slug":"sea-land-cloud-segmentation-in-satellite","title":"Semantic Segmentation in Satellite Hyperspectral Imagery by Deep Learning","date":"2023-10-24","arxiv_id":"2310.16210","repositories_listed":1,"syntology":null},{"url":"/paper/freemask-synthetic-images-with-dense-1","slug":"freemask-synthetic-images-with-dense-1","title":"FreeMask: Synthetic Images with Dense Annotations Make Stronger Segmentation Models","date":"2023-10-23","arxiv_id":"2310.15160","repositories_listed":1,"syntology":null},{"url":"/paper/p2at-pyramid-pooling-axial-transformer-for","slug":"p2at-pyramid-pooling-axial-transformer-for","title":"P2AT: Pyramid Pooling Axial Transformer for Real-time Semantic Segmentation","date":"2023-10-23","arxiv_id":"2310.15025","repositories_listed":1,"syntology":null},{"url":"/paper/sam-med3d","slug":"sam-med3d","title":"SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images","date":"2023-10-23","arxiv_id":"2310.15161","repositories_listed":1,"syntology":{"n":14,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/sam-med3d#ran","syntology_url":"https://syntology.ai/paper/2310.15161","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.15161"}},"official":{"repos":["uni-medical/sam-med3d"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/a-survey-on-continual-semantic-segmentation","slug":"a-survey-on-continual-semantic-segmentation","title":"A Survey on Continual Semantic Segmentation: Theory, Challenge, Method and Application","date":"2023-10-22","arxiv_id":"2310.14277","repositories_listed":1,"syntology":null},{"url":"/paper/diffusion-based-data-augmentation-for-nuclei","slug":"diffusion-based-data-augmentation-for-nuclei","title":"Diffusion-based Data Augmentation for Nuclei Image Segmentation","date":"2023-10-22","arxiv_id":"2310.14197","repositories_listed":1,"syntology":null},{"url":"/paper/partition-speeds-up-learning-implicit-neural-1","slug":"partition-speeds-up-learning-implicit-neural-1","title":"Partition Speeds Up Learning Implicit Neural Representations Based on Exponential-Increase Hypothesis","date":"2023-10-22","arxiv_id":"2310.14184","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":4,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"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) · 2 unverified; every one of the 4 samples that ran constructed an object rather than computing a result","sample_list":"/paper/partition-speeds-up-learning-implicit-neural-1#ran","syntology_url":"https://syntology.ai/paper/2310.14184","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.14184"}},"official":{"repos":["1999kevin/inr-partition"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":4,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/accelerated-sparse-kernel-spectral-clustering","slug":"accelerated-sparse-kernel-spectral-clustering","title":"Accelerated sparse Kernel Spectral Clustering for large scale data clustering problems","date":"2023-10-20","arxiv_id":"2310.13381","repositories_listed":1,"syntology":null},{"url":"/paper/deepfdr-a-deep-learning-based-false-discovery","slug":"deepfdr-a-deep-learning-based-false-discovery","title":"DeepFDR: A Deep Learning-based False Discovery Rate Control Method for Neuroimaging Data","date":"2023-10-20","arxiv_id":"2310.13349","repositories_listed":1,"syntology":null},{"url":"/paper/flair-a-country-scale-land-cover-semantic","slug":"flair-a-country-scale-land-cover-semantic","title":"FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical Imagery","date":"2023-10-20","arxiv_id":"2310.13336","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/flair-a-country-scale-land-cover-semantic#ran","syntology_url":"https://syntology.ai/paper/2310.13336","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.13336"}},"official":{"repos":["ignf/flair-2-ai-challenge"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/longer-range-contextualized-masked","slug":"longer-range-contextualized-masked","title":"Learning with Unmasked Tokens Drives Stronger Vision Learners","date":"2023-10-20","arxiv_id":"2310.13593","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/longer-range-contextualized-masked#ran","syntology_url":"https://syntology.ai/paper/2310.13593","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.13593"}},"official":{"repos":["naver-ai/lut"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/segment-select-correct-a-framework-for-weakly","slug":"segment-select-correct-a-framework-for-weakly","title":"Segment, Select, Correct: A Framework for Weakly-Supervised Referring Segmentation","date":"2023-10-20","arxiv_id":"2310.13479","repositories_listed":1,"syntology":null},{"url":"/paper/skin-lesion-segmentation-improved-by","slug":"skin-lesion-segmentation-improved-by","title":"Skin Lesion Segmentation Improved by Transformer-based Networks with Inter-scale Dependency Modeling","date":"2023-10-20","arxiv_id":"2310.13604","repositories_listed":1,"syntology":null},{"url":"/paper/cross-attention-spatio-temporal-context","slug":"cross-attention-spatio-temporal-context","title":"Cross-attention Spatio-temporal Context Transformer for Semantic Segmentation of Historical Maps","date":"2023-10-19","arxiv_id":"2310.12616","repositories_listed":1,"syntology":null},{"url":"/paper/da-transunet-integrating-spatial-and-channel","slug":"da-transunet-integrating-spatial-and-channel","title":"DA-TransUNet: Integrating Spatial and Channel Dual Attention with Transformer U-Net for Medical Image Segmentation","date":"2023-10-19","arxiv_id":"2310.12570","repositories_listed":1,"syntology":null},{"url":"/paper/emit-diff-enhancing-medical-image","slug":"emit-diff-enhancing-medical-image","title":"DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model","date":"2023-10-19","arxiv_id":"2310.12868","repositories_listed":1,"syntology":null},{"url":"/paper/letfuser-light-weight-end-to-end-transformer","slug":"letfuser-light-weight-end-to-end-transformer","title":"LeTFuser: Light-weight End-to-end Transformer-Based Sensor Fusion for Autonomous Driving with Multi-Task Learning","date":"2023-10-19","arxiv_id":"2310.13135","repositories_listed":1,"syntology":null},{"url":"/paper/lidar-panoptic-segmentation-and-tracking","slug":"lidar-panoptic-segmentation-and-tracking","title":"Lidar Panoptic Segmentation and Tracking without Bells and Whistles","date":"2023-10-19","arxiv_id":"2310.12464","repositories_listed":1,"syntology":null},{"url":"/paper/minimalist-and-high-performance-semantic","slug":"minimalist-and-high-performance-semantic","title":"Minimalist and High-Performance Semantic Segmentation with Plain Vision Transformers","date":"2023-10-19","arxiv_id":"2310.12755","repositories_listed":1,"syntology":null},{"url":"/paper/not-just-learning-from-others-but-relying-on","slug":"not-just-learning-from-others-but-relying-on","title":"Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing","date":"2023-10-19","arxiv_id":"2310.12452","repositories_listed":1,"syntology":null},{"url":"/paper/putting-the-object-back-into-video-object","slug":"putting-the-object-back-into-video-object","title":"Putting the Object Back into Video Object Segmentation","date":"2023-10-19","arxiv_id":"2310.12982","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/putting-the-object-back-into-video-object#ran","syntology_url":"https://syntology.ai/paper/2310.12982","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.12982"}},"official":{"repos":["hkchengrex/Cutie"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/weakly-supervised-semantic-segmentation-with-2","slug":"weakly-supervised-semantic-segmentation-with-2","title":"Weakly-Supervised Semantic Segmentation with Image-Level Labels: from Traditional Models to Foundation Models","date":"2023-10-19","arxiv_id":"2310.13026","repositories_listed":1,"syntology":{"n":13,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":3,"phrase":"7 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; 2 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/weakly-supervised-semantic-segmentation-with-2#ran","syntology_url":"https://syntology.ai/paper/2310.13026","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.13026"}},"official":{"repos":["zhaozhengchen/sam_wsss"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/segmatron-embodied-adaptive-semantic","slug":"segmatron-embodied-adaptive-semantic","title":"SegmATRon: Embodied Adaptive Semantic Segmentation for Indoor Environment","date":"2023-10-18","arxiv_id":"2310.12031","repositories_listed":1,"syntology":null},{"url":"/paper/fusionu-net-u-net-with-enhanced-skip","slug":"fusionu-net-u-net-with-enhanced-skip","title":"FusionU-Net: U-Net with Enhanced Skip Connection for Pathology Image Segmentation","date":"2023-10-17","arxiv_id":"2310.10951","repositories_listed":1,"syntology":null},{"url":"/paper/towards-generic-semi-supervised-framework-for-1","slug":"towards-generic-semi-supervised-framework-for-1","title":"Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation","date":"2023-10-17","arxiv_id":"2310.11320","repositories_listed":1,"syntology":{"n":19,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":12,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":19,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 1 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 12 unverified","sample_list":"/paper/towards-generic-semi-supervised-framework-for-1#ran","syntology_url":"https://syntology.ai/paper/2310.11320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.11320"}},"official":{"repos":["xmed-lab/GenericSSL"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":12,"ran_from_kinds":["official"]}}},{"url":"/paper/idrnet-intervention-driven-relation-network-1","slug":"idrnet-intervention-driven-relation-network-1","title":"IDRNet: Intervention-Driven Relation Network for Semantic Segmentation","date":"2023-10-16","arxiv_id":"2310.10755","repositories_listed":1,"syntology":null},{"url":"/paper/label-efficient-segmentation-via-affinity-1","slug":"label-efficient-segmentation-via-affinity-1","title":"Label-efficient Segmentation via Affinity Propagation","date":"2023-10-16","arxiv_id":"2310.10533","repositories_listed":1,"syntology":null},{"url":"/paper/motion2language-unsupervised-learning-of","slug":"motion2language-unsupervised-learning-of","title":"Motion2Language, unsupervised learning of synchronized semantic motion segmentation","date":"2023-10-16","arxiv_id":"2310.10594","repositories_listed":1,"syntology":null}],"record_sha256":"2a8fae6c52ce1145a3660be4478342be8a7e4552a0a87602a32637ad714b90ec","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}