{"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/31","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":31,"pages_in_order":148,"rows_per_page":100,"rows":[3001,3100],"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/30","next":"/task/semantic-segmentation/papers/32","papers":[{"url":"/paper/neuroquantify-an-image-analysis-software-for","slug":"neuroquantify-an-image-analysis-software-for","title":"NeuroQuantify -- An Image Analysis Software for Detection and Quantification of Neurons and Neurites using Deep Learning","date":"2023-10-16","arxiv_id":"2310.10978","repositories_listed":1,"syntology":null},{"url":"/paper/on-the-transferability-of-learning-models-for","slug":"on-the-transferability-of-learning-models-for","title":"On the Transferability of Learning Models for Semantic Segmentation for Remote Sensing Data","date":"2023-10-16","arxiv_id":"2310.10490","repositories_listed":1,"syntology":null},{"url":"/paper/refconv-re-parameterized-refocusing","slug":"refconv-re-parameterized-refocusing","title":"RefConv: Re-parameterized Refocusing Convolution for Powerful ConvNets","date":"2023-10-16","arxiv_id":"2310.10563","repositories_listed":1,"syntology":null},{"url":"/paper/faster-3d-cardiac-ct-segmentation-with-vision","slug":"faster-3d-cardiac-ct-segmentation-with-vision","title":"Vision Transformers increase efficiency of 3D cardiac CT multi-label segmentation","date":"2023-10-13","arxiv_id":"2310.09099","repositories_listed":1,"syntology":null},{"url":"/paper/uniparser-multi-human-parsing-with-unified","slug":"uniparser-multi-human-parsing-with-unified","title":"UniParser: Multi-Human Parsing with Unified Correlation Representation Learning","date":"2023-10-13","arxiv_id":"2310.08984","repositories_listed":1,"syntology":null},{"url":"/paper/ponderv2-pave-the-way-for-3d-foundataion","slug":"ponderv2-pave-the-way-for-3d-foundataion","title":"PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm","date":"2023-10-12","arxiv_id":"2310.08586","repositories_listed":1,"syntology":null},{"url":"/paper/ssg2-a-new-modelling-paradigm-for-semantic","slug":"ssg2-a-new-modelling-paradigm-for-semantic","title":"SSG2: A new modelling paradigm for semantic segmentation","date":"2023-10-12","arxiv_id":"2310.08671","repositories_listed":1,"syntology":null},{"url":"/paper/unipad-a-universal-pre-training-paradigm-for","slug":"unipad-a-universal-pre-training-paradigm-for","title":"UniPAD: A Universal Pre-training Paradigm for Autonomous Driving","date":"2023-10-12","arxiv_id":"2310.08370","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-learning-of-object-centric-1","slug":"unsupervised-learning-of-object-centric-1","title":"Unsupervised Learning of Object-Centric Embeddings for Cell Instance Segmentation in Microscopy Images","date":"2023-10-12","arxiv_id":"2310.08501","repositories_listed":1,"syntology":null},{"url":"/paper/virtual-augmented-reality-for-atari","slug":"virtual-augmented-reality-for-atari","title":"Virtual Augmented Reality for Atari Reinforcement Learning","date":"2023-10-12","arxiv_id":"2310.08683","repositories_listed":1,"syntology":null},{"url":"/paper/causal-unsupervised-semantic-segmentation","slug":"causal-unsupervised-semantic-segmentation","title":"Causal Unsupervised Semantic Segmentation","date":"2023-10-11","arxiv_id":"2310.07379","repositories_listed":1,"syntology":null},{"url":"/paper/line-detection-and-segmentation-of-annual","slug":"line-detection-and-segmentation-of-annual","title":"Line Detection and Segmentation of Annual Crops Using Hybrid Method","date":"2023-10-11","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/pointhr-exploring-high-resolution","slug":"pointhr-exploring-high-resolution","title":"PointHR: Exploring High-Resolution Architectures for 3D Point Cloud Segmentation","date":"2023-10-11","arxiv_id":"2310.07743","repositories_listed":1,"syntology":null},{"url":"/paper/relational-prior-knowledge-graphs-for","slug":"relational-prior-knowledge-graphs-for","title":"Relational Prior Knowledge Graphs for Detection and Instance Segmentation","date":"2023-10-11","arxiv_id":"2310.07573","repositories_listed":1,"syntology":null},{"url":"/paper/coinseg-contrast-inter-and-intra-class-1","slug":"coinseg-contrast-inter-and-intra-class-1","title":"CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation","date":"2023-10-10","arxiv_id":"2310.06368","repositories_listed":1,"syntology":{"n":15,"n_ran":11,"n_constructed":0,"n_ran_checked":7,"n_instrument":4,"n_unverified":4,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":15,"phrase":"11 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; 4 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/coinseg-contrast-inter-and-intra-class-1#ran","syntology_url":"https://syntology.ai/paper/2310.06368","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.06368"}},"official":{"repos":["zkzhang98/coinseg"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/evit-an-eagle-vision-transformer-with-bi","slug":"evit-an-eagle-vision-transformer-with-bi","title":"EViT: An Eagle Vision Transformer with Bi-Fovea Self-Attention","date":"2023-10-10","arxiv_id":"2310.06629","repositories_listed":1,"syntology":null},{"url":"/paper/utilizing-synthetic-data-for-medical-vision","slug":"utilizing-synthetic-data-for-medical-vision","title":"Utilizing Synthetic Data for Medical Vision-Language Pre-training: Bypassing the Need for Real Images","date":"2023-10-10","arxiv_id":"2310.07027","repositories_listed":1,"syntology":null},{"url":"/paper/a-critical-look-at-classic-test-time","slug":"a-critical-look-at-classic-test-time","title":"From Question to Exploration: Test-Time Adaptation in Semantic Segmentation?","date":"2023-10-09","arxiv_id":"2310.05341","repositories_listed":1,"syntology":null},{"url":"/paper/rethinking-dual-stream-super-resolution","slug":"rethinking-dual-stream-super-resolution","title":"Rethinking Dual-Stream Super-Resolution Semantic Learning in Medical Image Segmentation","date":"2023-10-09","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/winsyn-a-high-resolution-testbed-for","slug":"winsyn-a-high-resolution-testbed-for","title":"WinSyn: A High Resolution Testbed for Synthetic Data","date":"2023-10-09","arxiv_id":"2310.08471","repositories_listed":1,"syntology":null},{"url":"/paper/cross-head-mutual-mean-teaching-for-semi","slug":"cross-head-mutual-mean-teaching-for-semi","title":"Cross-head mutual Mean-Teaching for semi-supervised medical image segmentation","date":"2023-10-08","arxiv_id":"2310.05082","repositories_listed":1,"syntology":null},{"url":"/paper/low-resolution-self-attention-for-semantic","slug":"low-resolution-self-attention-for-semantic","title":"Low-Resolution Self-Attention for Semantic Segmentation","date":"2023-10-08","arxiv_id":"2310.05026","repositories_listed":1,"syntology":null},{"url":"/paper/ov-parts-towards-open-vocabulary-part","slug":"ov-parts-towards-open-vocabulary-part","title":"OV-PARTS: Towards Open-Vocabulary Part Segmentation","date":"2023-10-08","arxiv_id":"2310.05107","repositories_listed":1,"syntology":{"n":14,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":14,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/ov-parts-towards-open-vocabulary-part#ran","syntology_url":"https://syntology.ai/paper/2310.05107","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.05107"}},"official":{"repos":["openrobotlab/ov_parts"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/ag-crc-anatomy-guided-colorectal-cancer","slug":"ag-crc-anatomy-guided-colorectal-cancer","title":"AG-CRC: Anatomy-Guided Colorectal Cancer Segmentation in CT with Imperfect Anatomical Knowledge","date":"2023-10-07","arxiv_id":"2310.04677","repositories_listed":1,"syntology":null},{"url":"/paper/convnextv2-fusion-with-mask-r-cnn-for","slug":"convnextv2-fusion-with-mask-r-cnn-for","title":"ConvNeXtv2 Fusion with Mask R-CNN for Automatic Region Based Coronary Artery Stenosis Detection for Disease Diagnosis","date":"2023-10-07","arxiv_id":"2310.04749","repositories_listed":1,"syntology":null},{"url":"/paper/sub-token-vit-embedding-via-stochastic","slug":"sub-token-vit-embedding-via-stochastic","title":"Sub-token ViT Embedding via Stochastic Resonance Transformers","date":"2023-10-06","arxiv_id":"2310.03967","repositories_listed":1,"syntology":null},{"url":"/paper/vton-it-virtual-try-on-using-image","slug":"vton-it-virtual-try-on-using-image","title":"VTON-IT: Virtual Try-On using Image Translation","date":"2023-10-06","arxiv_id":"2310.04558","repositories_listed":1,"syntology":null},{"url":"/paper/certification-of-deep-learning-models-for","slug":"certification-of-deep-learning-models-for","title":"Certification of Deep Learning Models for Medical Image Segmentation","date":"2023-10-05","arxiv_id":"2310.03664","repositories_listed":1,"syntology":null},{"url":"/paper/fnoseg3d-resolution-robust-3d-image","slug":"fnoseg3d-resolution-robust-3d-image","title":"FNOSeg3D: Resolution-Robust 3D Image Segmentation with Fourier Neural Operator","date":"2023-10-05","arxiv_id":"2310.03872","repositories_listed":1,"syntology":null},{"url":"/paper/hartleymha-self-attention-in-frequency-domain","slug":"hartleymha-self-attention-in-frequency-domain","title":"HartleyMHA: Self-Attention in Frequency Domain for Resolution-Robust and Parameter-Efficient 3D Image Segmentation","date":"2023-10-05","arxiv_id":"2310.04466","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-model-learning-heterogeneity-for","slug":"exploring-model-learning-heterogeneity-for","title":"Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness","date":"2023-10-03","arxiv_id":"2310.02237","repositories_listed":1,"syntology":null},{"url":"/paper/extending-cam-based-xai-methods-for-remote","slug":"extending-cam-based-xai-methods-for-remote","title":"Extending CAM-based XAI methods for Remote Sensing Imagery Segmentation","date":"2023-10-03","arxiv_id":"2310.01837","repositories_listed":1,"syntology":null},{"url":"/paper/trainable-noise-model-as-an-xai-evaluation","slug":"trainable-noise-model-as-an-xai-evaluation","title":"Trainable Noise Model as an XAI evaluation method: application on Sobol for remote sensing image segmentation","date":"2023-10-03","arxiv_id":"2310.01828","repositories_listed":1,"syntology":null},{"url":"/paper/transradar-adaptive-directional-transformer","slug":"transradar-adaptive-directional-transformer","title":"TransRadar: Adaptive-Directional Transformer for Real-Time Multi-View Radar Semantic Segmentation","date":"2023-10-03","arxiv_id":"2310.02260","repositories_listed":1,"syntology":null},{"url":"/paper/zero-shot-refinement-of-buildings","slug":"zero-shot-refinement-of-buildings","title":"Zero-Shot Refinement of Buildings' Segmentation Models using SAM","date":"2023-10-03","arxiv_id":"2310.01845","repositories_listed":1,"syntology":null},{"url":"/paper/baaf-a-benchmark-attention-adaptive-framework","slug":"baaf-a-benchmark-attention-adaptive-framework","title":"A simple thinking about the application of the attention mechanism in medical ultrasound image segmentation task","date":"2023-10-02","arxiv_id":"2310.00919","repositories_listed":1,"syntology":null},{"url":"/paper/clipself-vision-transformer-distills-itself","slug":"clipself-vision-transformer-distills-itself","title":"CLIPSelf: Vision Transformer Distills Itself for Open-Vocabulary Dense Prediction","date":"2023-10-02","arxiv_id":"2310.01403","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"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 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) · 2 unverified","sample_list":"/paper/clipself-vision-transformer-distills-itself#ran","syntology_url":"https://syntology.ai/paper/2310.01403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.01403"}},"official":{"repos":["wusize/clipself"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-task-learning-with-3d-aware","slug":"multi-task-learning-with-3d-aware","title":"Multi-task Learning with 3D-Aware Regularization","date":"2023-10-02","arxiv_id":"2310.00986","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-of-contextualized","slug":"self-supervised-learning-of-contextualized","title":"Self-supervised Learning of Contextualized Local Visual Embeddings","date":"2023-10-01","arxiv_id":"2310.00527","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":6,"n_pointer_only":2,"phrase":"8 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/self-supervised-learning-of-contextualized#ran","syntology_url":"https://syntology.ai/paper/2310.00527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.00527"}},"official":{"repos":["sthalles/clove"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/an-easy-zero-shot-learning-combination","slug":"an-easy-zero-shot-learning-combination","title":"An easy zero-shot learning combination: Texture Sensitive Semantic Segmentation IceHrNet and Advanced Style Transfer Learning Strategy","date":"2023-09-30","arxiv_id":"2310.00310","repositories_listed":1,"syntology":null},{"url":"/paper/deformux-net-exploring-a-3d-foundation","slug":"deformux-net-exploring-a-3d-foundation","title":"DeformUX-Net: Exploring a 3D Foundation Backbone for Medical Image Segmentation with Depthwise Deformable Convolution","date":"2023-09-30","arxiv_id":"2310.00199","repositories_listed":1,"syntology":null},{"url":"/paper/a-foundation-model-for-general-moving-object","slug":"a-foundation-model-for-general-moving-object","title":"A Foundation Model for General Moving Object Segmentation in Medical Images","date":"2023-09-29","arxiv_id":"2309.17264","repositories_listed":1,"syntology":null},{"url":"/paper/apnet-urban-level-scene-segmentation-of","slug":"apnet-urban-level-scene-segmentation-of","title":"APNet: Urban-level Scene Segmentation of Aerial Images and Point Clouds","date":"2023-09-29","arxiv_id":"2309.17162","repositories_listed":1,"syntology":null},{"url":"/paper/nnsam-plug-and-play-segment-anything-model","slug":"nnsam-plug-and-play-segment-anything-model","title":"nnSAM: Plug-and-play Segment Anything Model Improves nnUNet Performance","date":"2023-09-29","arxiv_id":"2309.16967","repositories_listed":1,"syntology":null},{"url":"/paper/segrcdb-semantic-segmentation-via-formula-1","slug":"segrcdb-semantic-segmentation-via-formula-1","title":"SegRCDB: Semantic Segmentation via Formula-Driven Supervised Learning","date":"2023-09-29","arxiv_id":"2309.17083","repositories_listed":1,"syntology":null},{"url":"/paper/latent-noise-segmentation-how-neural-noise","slug":"latent-noise-segmentation-how-neural-noise","title":"Latent Noise Segmentation: How Neural Noise Leads to the Emergence of Segmentation and Grouping","date":"2023-09-28","arxiv_id":"2309.16515","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":11,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":9,"n_pointer_only":13,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 1 honoured, 1 violated, 9 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/latent-noise-segmentation-how-neural-noise#ran","syntology_url":"https://syntology.ai/paper/2309.16515","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.16515"}},"official":{"repos":["zhengqinguuu/latentnoisesegmentation"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/mask4d-mask-transformer-for-4d-panoptic","slug":"mask4d-mask-transformer-for-4d-panoptic","title":"Mask4Former: Mask Transformer for 4D Panoptic Segmentation","date":"2023-09-28","arxiv_id":"2309.16133","repositories_listed":1,"syntology":null},{"url":"/paper/sa2-net-scale-aware-attention-network-for","slug":"sa2-net-scale-aware-attention-network-for","title":"SA2-Net: Scale-aware Attention Network for Microscopic Image Segmentation","date":"2023-09-28","arxiv_id":"2309.16661","repositories_listed":1,"syntology":null},{"url":"/paper/infraparis-a-multi-modal-and-multi-task","slug":"infraparis-a-multi-modal-and-multi-task","title":"InfraParis: A multi-modal and multi-task autonomous driving dataset","date":"2023-09-27","arxiv_id":"2309.15751","repositories_listed":1,"syntology":null},{"url":"/paper/investigating-the-changes-in-bold-responses","slug":"investigating-the-changes-in-bold-responses","title":"Investigating the changes in BOLD responses during viewing of images with varied complexity: An fMRI time-series based analysis on human vision","date":"2023-09-27","arxiv_id":"2309.15495","repositories_listed":1,"syntology":null},{"url":"/paper/discrepancy-matters-learning-from","slug":"discrepancy-matters-learning-from","title":"Discrepancy Matters: Learning from Inconsistent Decoder Features for Consistent Semi-supervised Medical Image Segmentation","date":"2023-09-26","arxiv_id":"2309.14819","repositories_listed":1,"syntology":null},{"url":"/paper/mocae-mixture-of-calibrated-experts","slug":"mocae-mixture-of-calibrated-experts","title":"MoCaE: Mixture of Calibrated Experts Significantly Improves Object Detection","date":"2023-09-26","arxiv_id":"2309.14976","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/mocae-mixture-of-calibrated-experts#ran","syntology_url":"https://syntology.ai/paper/2309.14976","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.14976"}},"official":{"repos":["fiveai/MoCaE"],"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/treating-motion-as-option-with-output","slug":"treating-motion-as-option-with-output","title":"Treating Motion as Option with Output Selection for Unsupervised Video Object Segmentation","date":"2023-09-26","arxiv_id":"2309.14786","repositories_listed":1,"syntology":null},{"url":"/paper/3d-indoor-instance-segmentation-in-an-open-1","slug":"3d-indoor-instance-segmentation-in-an-open-1","title":"3D Indoor Instance Segmentation in an Open-World","date":"2023-09-25","arxiv_id":"2309.14338","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"6 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/3d-indoor-instance-segmentation-in-an-open-1#ran","syntology_url":"https://syntology.ai/paper/2309.14338","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.14338"}},"official":{"repos":["aminebdj/3d-owis"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/asymformer-asymmetrical-cross-modal","slug":"asymformer-asymmetrical-cross-modal","title":"AsymFormer: Asymmetrical Cross-Modal Representation Learning for Mobile Platform Real-Time RGB-D Semantic Segmentation","date":"2023-09-25","arxiv_id":"2309.14065","repositories_listed":1,"syntology":null},{"url":"/paper/calibration-based-dual-prototypical","slug":"calibration-based-dual-prototypical","title":"Calibration-based Dual Prototypical Contrastive Learning Approach for Domain Generalization Semantic Segmentation","date":"2023-09-25","arxiv_id":"2309.14282","repositories_listed":1,"syntology":null},{"url":"/paper/clip-diy-clip-dense-inference-yields-open","slug":"clip-diy-clip-dense-inference-yields-open","title":"CLIP-DIY: CLIP Dense Inference Yields Open-Vocabulary Semantic Segmentation For-Free","date":"2023-09-25","arxiv_id":"2309.14289","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":7,"phrase":"7 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; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/clip-diy-clip-dense-inference-yields-open#ran","syntology_url":"https://syntology.ai/paper/2309.14289","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.14289"}},"official":{"repos":["wysoczanska/clip-diy"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dataset-diffusion-diffusion-based-synthetic","slug":"dataset-diffusion-diffusion-based-synthetic","title":"Dataset Diffusion: Diffusion-based Synthetic Dataset Generation for Pixel-Level Semantic Segmentation","date":"2023-09-25","arxiv_id":"2309.14303","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":1,"n_ran_checked":8,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":10,"phrase":"8 ran (of which 1 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/dataset-diffusion-diffusion-based-synthetic#ran","syntology_url":"https://syntology.ai/paper/2309.14303","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.14303"}},"official":{"repos":["vinairesearch/dataset-diffusion"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":1,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/masked-image-residual-learning-for-scaling-1","slug":"masked-image-residual-learning-for-scaling-1","title":"Masked Image Residual Learning for Scaling Deeper Vision Transformers","date":"2023-09-25","arxiv_id":"2309.14136","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-semantic-segmentation-by-4","slug":"weakly-supervised-semantic-segmentation-by-4","title":"Weakly Supervised Semantic Segmentation by Knowledge Graph Inference","date":"2023-09-25","arxiv_id":"2309.14057","repositories_listed":1,"syntology":null},{"url":"/paper/distribution-aware-continual-test-time","slug":"distribution-aware-continual-test-time","title":"Distribution-Aware Continual Test-Time Adaptation for Semantic Segmentation","date":"2023-09-24","arxiv_id":"2309.13604","repositories_listed":1,"syntology":null},{"url":"/paper/medivista-sam-zero-shot-medical-video","slug":"medivista-sam-zero-shot-medical-video","title":"MediViSTA: Medical Video Segmentation via Temporal Fusion SAM Adaptation for Echocardiography","date":"2023-09-24","arxiv_id":"2309.13539","repositories_listed":1,"syntology":null},{"url":"/paper/feddrive-v2-an-analysis-of-the-impact-of","slug":"feddrive-v2-an-analysis-of-the-impact-of","title":"FedDrive v2: an Analysis of the Impact of Label Skewness in Federated Semantic Segmentation for Autonomous Driving","date":"2023-09-23","arxiv_id":"2309.13336","repositories_listed":1,"syntology":null},{"url":"/paper/clusterformer-clustering-as-a-universal","slug":"clusterformer-clustering-as-a-universal","title":"ClusterFormer: Clustering As A Universal Visual Learner","date":"2023-09-22","arxiv_id":"2309.13196","repositories_listed":1,"syntology":null},{"url":"/paper/mosaicfusion-diffusion-models-as-data","slug":"mosaicfusion-diffusion-models-as-data","title":"MosaicFusion: Diffusion Models as Data Augmenters for Large Vocabulary Instance Segmentation","date":"2023-09-22","arxiv_id":"2309.13042","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"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) · 1 unverified","sample_list":"/paper/mosaicfusion-diffusion-models-as-data#ran","syntology_url":"https://syntology.ai/paper/2309.13042","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.13042"}},"official":{"repos":["jiahao000/mosaicfusion"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/moda-leveraging-motion-priors-from-videos-for","slug":"moda-leveraging-motion-priors-from-videos-for","title":"MoDA: Leveraging Motion Priors from Videos for Advancing Unsupervised Domain Adaptation in Semantic Segmentation","date":"2023-09-21","arxiv_id":"2309.11711","repositories_listed":1,"syntology":null},{"url":"/paper/mopa-multi-modal-prior-aided-domain","slug":"mopa-multi-modal-prior-aided-domain","title":"MoPA: Multi-Modal Prior Aided Domain Adaptation for 3D Semantic Segmentation","date":"2023-09-21","arxiv_id":"2309.11839","repositories_listed":1,"syntology":null},{"url":"/paper/neurallabeling-a-versatile-toolset-for","slug":"neurallabeling-a-versatile-toolset-for","title":"NeuralLabeling: A versatile toolset for labeling vision datasets using Neural Radiance Fields","date":"2023-09-21","arxiv_id":"2309.11966","repositories_listed":1,"syntology":null},{"url":"/paper/panovos-bridging-non-panoramic-and-panoramic","slug":"panovos-bridging-non-panoramic-and-panoramic","title":"PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video Segmentation","date":"2023-09-21","arxiv_id":"2309.12303","repositories_listed":1,"syntology":null},{"url":"/paper/spatially-guiding-unsupervised-semantic","slug":"spatially-guiding-unsupervised-semantic","title":"Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling","date":"2023-09-21","arxiv_id":"2309.12378","repositories_listed":1,"syntology":null},{"url":"/paper/tcovis-temporally-consistent-online-video","slug":"tcovis-temporally-consistent-online-video","title":"TCOVIS: Temporally Consistent Online Video Instance Segmentation","date":"2023-09-21","arxiv_id":"2309.11857","repositories_listed":1,"syntology":null},{"url":"/paper/dense-2d-3d-indoor-prediction-with-sound-via","slug":"dense-2d-3d-indoor-prediction-with-sound-via","title":"Dense 2D-3D Indoor Prediction with Sound via Aligned Cross-Modal Distillation","date":"2023-09-20","arxiv_id":"2309.11081","repositories_listed":1,"syntology":null},{"url":"/paper/eptq-enhanced-post-training-quantization-via","slug":"eptq-enhanced-post-training-quantization-via","title":"EPTQ: Enhanced Post-Training Quantization via Hessian-guided Network-wise Optimization","date":"2023-09-20","arxiv_id":"2309.11531","repositories_listed":1,"syntology":null},{"url":"/paper/h2o-heatmap-by-hierarchical-occlusion","slug":"h2o-heatmap-by-hierarchical-occlusion","title":"H²O: Heatmap by Hierarchical Occlusion","date":"2023-09-20","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/more-complex-encoder-is-not-all-you-need","slug":"more-complex-encoder-is-not-all-you-need","title":"More complex encoder is not all you need","date":"2023-09-20","arxiv_id":"2309.11139","repositories_listed":1,"syntology":null},{"url":"/paper/multi-grained-temporal-prototype-learning-for","slug":"multi-grained-temporal-prototype-learning-for","title":"Multi-grained Temporal Prototype Learning for Few-shot Video Object Segmentation","date":"2023-09-20","arxiv_id":"2309.11160","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":5,"n_instrument":3,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":10,"phrase":"8 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; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/multi-grained-temporal-prototype-learning-for#ran","syntology_url":"https://syntology.ai/paper/2309.11160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.11160"}},"official":{"repos":["nankepan/VIPMT"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/partition-a-medical-image-extracting-multiple","slug":"partition-a-medical-image-extracting-multiple","title":"Partition-A-Medical-Image: Extracting Multiple Representative Sub-regions for Few-shot Medical Image Segmentation","date":"2023-09-20","arxiv_id":"2309.11172","repositories_listed":1,"syntology":null},{"url":"/paper/rmt-retentive-networks-meet-vision","slug":"rmt-retentive-networks-meet-vision","title":"RMT: Retentive Networks Meet Vision Transformers","date":"2023-09-20","arxiv_id":"2309.11523","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":3,"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/rmt-retentive-networks-meet-vision#ran","syntology_url":"https://syntology.ai/paper/2309.11523","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.11523"}},"official":{"repos":["qhfan/RMT"],"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/towards-robust-few-shot-point-cloud-semantic","slug":"towards-robust-few-shot-point-cloud-semantic","title":"Towards Robust Few-shot Point Cloud Semantic Segmentation","date":"2023-09-20","arxiv_id":"2309.11228","repositories_listed":1,"syntology":null},{"url":"/paper/few-shot-panoptic-segmentation-with","slug":"few-shot-panoptic-segmentation-with","title":"Few-Shot Panoptic Segmentation With Foundation Models","date":"2023-09-19","arxiv_id":"2309.10726","repositories_listed":1,"syntology":null},{"url":"/paper/spatial-assistant-encoder-decoder-network-for","slug":"spatial-assistant-encoder-decoder-network-for","title":"Spatial-Assistant Encoder-Decoder Network for Real Time Semantic Segmentation","date":"2023-09-19","arxiv_id":"2309.10519","repositories_listed":1,"syntology":null},{"url":"/paper/spot-scalable-3d-pre-training-via-occupancy","slug":"spot-scalable-3d-pre-training-via-occupancy","title":"SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations","date":"2023-09-19","arxiv_id":"2309.10527","repositories_listed":1,"syntology":null},{"url":"/paper/upl-sfda-uncertainty-aware-pseudo-label","slug":"upl-sfda-uncertainty-aware-pseudo-label","title":"UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation","date":"2023-09-19","arxiv_id":"2309.10244","repositories_listed":1,"syntology":null},{"url":"/paper/dformer-rethinking-rgbd-representation","slug":"dformer-rethinking-rgbd-representation","title":"DFormer: Rethinking RGBD Representation Learning for Semantic Segmentation","date":"2023-09-18","arxiv_id":"2309.09668","repositories_listed":1,"syntology":null},{"url":"/paper/domain-generalization-with-fourier-transform","slug":"domain-generalization-with-fourier-transform","title":"Domain Generalization with Fourier Transform and Soft Thresholding","date":"2023-09-18","arxiv_id":"2309.09866","repositories_listed":1,"syntology":null},{"url":"/paper/human-activity-segmentation-challenge-ecml","slug":"human-activity-segmentation-challenge-ecml","title":"Human Activity Segmentation Challenge @ ECML/PKDD’23","date":"2023-09-18","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/dynamon-motion-aware-fast-and-robust-camera","slug":"dynamon-motion-aware-fast-and-robust-camera","title":"DynaMoN: Motion-Aware Fast and Robust Camera Localization for Dynamic Neural Radiance Fields","date":"2023-09-16","arxiv_id":"2309.08927","repositories_listed":1,"syntology":null},{"url":"/paper/ma-sam-modality-agnostic-sam-adaptation-for","slug":"ma-sam-modality-agnostic-sam-adaptation-for","title":"MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation","date":"2023-09-16","arxiv_id":"2309.08842","repositories_listed":1,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"10 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; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/ma-sam-modality-agnostic-sam-adaptation-for#ran","syntology_url":"https://syntology.ai/paper/2309.08842","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.08842"}},"official":{"repos":["cchen-cc/ma-sam"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/omnilrs-a-photorealistic-simulator-for-lunar","slug":"omnilrs-a-photorealistic-simulator-for-lunar","title":"OmniLRS: A Photorealistic Simulator for Lunar Robotics","date":"2023-09-16","arxiv_id":"2309.08997","repositories_listed":1,"syntology":null},{"url":"/paper/nisf-neural-implicit-segmentation-functions","slug":"nisf-neural-implicit-segmentation-functions","title":"NISF: Neural Implicit Segmentation Functions","date":"2023-09-15","arxiv_id":"2309.08643","repositories_listed":1,"syntology":null},{"url":"/paper/t-uda-temporal-unsupervised-domain-adaptation","slug":"t-uda-temporal-unsupervised-domain-adaptation","title":"T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds","date":"2023-09-15","arxiv_id":"2309.08302","repositories_listed":1,"syntology":null},{"url":"/paper/treelearn-a-comprehensive-deep-learning","slug":"treelearn-a-comprehensive-deep-learning","title":"TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds","date":"2023-09-15","arxiv_id":"2309.08471","repositories_listed":1,"syntology":null},{"url":"/paper/ura-uncertainty-aware-path-planning-using","slug":"ura-uncertainty-aware-path-planning-using","title":"URA*: Uncertainty-aware Path Planning using Image-based Aerial-to-Ground Traversability Estimation for Off-road Environments","date":"2023-09-15","arxiv_id":"2309.08814","repositories_listed":1,"syntology":null},{"url":"/paper/x-pdnet-accurate-joint-plane-instance","slug":"x-pdnet-accurate-joint-plane-instance","title":"X-PDNet: Accurate Joint Plane Instance Segmentation and Monocular Depth Estimation with Cross-Task Distillation and Boundary Correction","date":"2023-09-15","arxiv_id":"2309.08424","repositories_listed":1,"syntology":null},{"url":"/paper/nucleus-aware-self-supervised-pretraining","slug":"nucleus-aware-self-supervised-pretraining","title":"Nucleus-aware Self-supervised Pretraining Using Unpaired Image-to-image Translation for Histopathology Images","date":"2023-09-14","arxiv_id":"2309.07394","repositories_listed":1,"syntology":null},{"url":"/paper/temporal-aware-hierarchical-mask","slug":"temporal-aware-hierarchical-mask","title":"Temporal-aware Hierarchical Mask Classification for Video Semantic Segmentation","date":"2023-09-14","arxiv_id":"2309.08020","repositories_listed":1,"syntology":null},{"url":"/paper/keep-it-simpool-who-said-supervised","slug":"keep-it-simpool-who-said-supervised","title":"Keep It SimPool: Who Said Supervised Transformers Suffer from Attention Deficit?","date":"2023-09-13","arxiv_id":"2309.06891","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"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 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/keep-it-simpool-who-said-supervised#ran","syntology_url":"https://syntology.ai/paper/2309.06891","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.06891"}},"official":{"repos":["billpsomas/simpool"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/samus-adapting-segment-anything-model-for","slug":"samus-adapting-segment-anything-model-for","title":"Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting","date":"2023-09-13","arxiv_id":"2309.06824","repositories_listed":1,"syntology":{"n":12,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"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) · 7 unverified","sample_list":"/paper/samus-adapting-segment-anything-model-for#ran","syntology_url":"https://syntology.ai/paper/2309.06824","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.06824"}},"official":{"repos":["xianlin7/samus"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/deep-evidential-fusion-with-uncertainty","slug":"deep-evidential-fusion-with-uncertainty","title":"Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation","date":"2023-09-12","arxiv_id":"2309.05919","repositories_listed":1,"syntology":null},{"url":"/paper/melage-a-purely-python-based-neuroimaging","slug":"melage-a-purely-python-based-neuroimaging","title":"MELAGE: A purely python based Neuroimaging software (Neonatal)","date":"2023-09-12","arxiv_id":"2309.07175","repositories_listed":1,"syntology":null}],"record_sha256":"4f3be66cfe1dbbbe60fef13f0ec7bc251c456bacff0d4567ba33900b0ae84bfc","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}