{"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/35","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":35,"pages_in_order":148,"rows_per_page":100,"rows":[3401,3500],"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/34","next":"/task/semantic-segmentation/papers/36","papers":[{"url":"/paper/revisiting-token-pruning-for-object-detection","slug":"revisiting-token-pruning-for-object-detection","title":"Revisiting Token Pruning for Object Detection and Instance Segmentation","date":"2023-06-12","arxiv_id":"2306.07050","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/revisiting-token-pruning-for-object-detection#ran","syntology_url":"https://syntology.ai/paper/2306.07050","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07050"}},"official":{"repos":["uzh-rpg/svit"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/volume-droid-a-real-time-implementation-of","slug":"volume-droid-a-real-time-implementation-of","title":"Volume-DROID: A Real-Time Implementation of Volumetric Mapping with DROID-SLAM","date":"2023-06-12","arxiv_id":"2306.06850","repositories_listed":1,"syntology":null},{"url":"/paper/illumination-controllable-dehazing-network","slug":"illumination-controllable-dehazing-network","title":"Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding","date":"2023-06-09","arxiv_id":"2306.05675","repositories_listed":1,"syntology":null},{"url":"/paper/segvitv2-exploring-efficient-and-continual","slug":"segvitv2-exploring-efficient-and-continual","title":"SegViTv2: Exploring Efficient and Continual Semantic Segmentation with Plain Vision Transformers","date":"2023-06-09","arxiv_id":"2306.06289","repositories_listed":1,"syntology":null},{"url":"/paper/topology-aware-uncertainty-for-image-1","slug":"topology-aware-uncertainty-for-image-1","title":"Topology-Aware Uncertainty for Image Segmentation","date":"2023-06-09","arxiv_id":"2306.05671","repositories_listed":1,"syntology":{"n":27,"n_ran":21,"n_constructed":4,"n_ran_checked":17,"n_instrument":4,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":17,"n_pointer_only":7,"phrase":"21 ran (of which 4 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 0 violated, 17 with no contract checked; 4 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/topology-aware-uncertainty-for-image-1#ran","syntology_url":"https://syntology.ai/paper/2306.05671","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05671"}},"official":{"repos":["Saumya-Gupta-26/struct-uncertainty"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/a-novel-confidence-induced-class-activation","slug":"a-novel-confidence-induced-class-activation","title":"A Novel Confidence Induced Class Activation Mapping for MRI Brain Tumor Segmentation","date":"2023-06-08","arxiv_id":"2306.05476","repositories_listed":1,"syntology":null},{"url":"/paper/channel-prior-convolutional-attention-for","slug":"channel-prior-convolutional-attention-for","title":"Channel prior convolutional attention for medical image segmentation","date":"2023-06-08","arxiv_id":"2306.05196","repositories_listed":1,"syntology":null},{"url":"/paper/devil-is-in-channels-contrastive-single","slug":"devil-is-in-channels-contrastive-single","title":"Devil is in Channels: Contrastive Single Domain Generalization for Medical Image Segmentation","date":"2023-06-08","arxiv_id":"2306.05254","repositories_listed":1,"syntology":null},{"url":"/paper/does-image-anonymization-impact-computer","slug":"does-image-anonymization-impact-computer","title":"Does Image Anonymization Impact Computer Vision Training?","date":"2023-06-08","arxiv_id":"2306.05135","repositories_listed":1,"syntology":null},{"url":"/paper/improving-visual-prompt-tuning-for-self","slug":"improving-visual-prompt-tuning-for-self","title":"Improving Visual Prompt Tuning for Self-supervised Vision Transformers","date":"2023-06-08","arxiv_id":"2306.05067","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":3,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 3 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) · 0 unverified","sample_list":"/paper/improving-visual-prompt-tuning-for-self#ran","syntology_url":"https://syntology.ai/paper/2306.05067","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05067"}},"official":{"repos":["ryongithub/gatedprompttuning"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":3,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/neighborhood-attention-makes-the-encoder-of","slug":"neighborhood-attention-makes-the-encoder-of","title":"Neighborhood Attention Makes the Encoder of ResUNet Stronger for Accurate Road Extraction","date":"2023-06-08","arxiv_id":"2306.04947","repositories_listed":1,"syntology":null},{"url":"/paper/point-lgmask-local-and-global-contexts","slug":"point-lgmask-local-and-global-contexts","title":"Point-LGMask: Local and Global Contexts Embedding for Point Cloud Pre-training with Multi-Ratio Masking","date":"2023-06-08","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/vig-unet-vision-graph-neural-networks-for","slug":"vig-unet-vision-graph-neural-networks-for","title":"ViG-UNet: Vision Graph Neural Networks for Medical Image Segmentation","date":"2023-06-08","arxiv_id":"2306.04905","repositories_listed":1,"syntology":null},{"url":"/paper/a-dataset-for-deep-learning-based-bone","slug":"a-dataset-for-deep-learning-based-bone","title":"A Dataset for Deep Learning-based Bone Structure Analyses in Total Hip Arthroplasty","date":"2023-06-07","arxiv_id":"2306.04579","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-lift-3d-object-instance","slug":"contrastive-lift-3d-object-instance","title":"Contrastive Lift: 3D Object Instance Segmentation by Slow-Fast Contrastive Fusion","date":"2023-06-07","arxiv_id":"2306.04633","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/contrastive-lift-3d-object-instance#ran","syntology_url":"https://syntology.ai/paper/2306.04633","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.04633"}},"official":{"repos":["yashbhalgat/contrastive-lift"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/corrmatch-label-propagation-via-correlation","slug":"corrmatch-label-propagation-via-correlation","title":"CorrMatch: Label Propagation via Correlation Matching for Semi-Supervised Semantic Segmentation","date":"2023-06-07","arxiv_id":"2306.04300","repositories_listed":1,"syntology":null},{"url":"/paper/phenobench-a-large-dataset-and-benchmarks-for","slug":"phenobench-a-large-dataset-and-benchmarks-for","title":"PhenoBench -- A Large Dataset and Benchmarks for Semantic Image Interpretation in the Agricultural Domain","date":"2023-06-07","arxiv_id":"2306.04557","repositories_listed":1,"syntology":null},{"url":"/paper/smrvis-point-cloud-extraction-from-3-d","slug":"smrvis-point-cloud-extraction-from-3-d","title":"SMRVIS: Point cloud extraction from 3-D ultrasound for non-destructive testing","date":"2023-06-07","arxiv_id":"2306.04668","repositories_listed":1,"syntology":null},{"url":"/paper/tec-net-vision-transformer-embrace","slug":"tec-net-vision-transformer-embrace","title":"TEC-Net: Vision Transformer Embrace Convolutional Neural Networks for Medical Image Segmentation","date":"2023-06-07","arxiv_id":"2306.04086","repositories_listed":1,"syntology":null},{"url":"/paper/accurate-fine-grained-segmentation-of-human","slug":"accurate-fine-grained-segmentation-of-human","title":"Accurate Fine-Grained Segmentation of Human Anatomy in Radiographs via Volumetric Pseudo-Labeling","date":"2023-06-06","arxiv_id":"2306.03934","repositories_listed":1,"syntology":null},{"url":"/paper/cit-net-convolutional-neural-networks-hand-in","slug":"cit-net-convolutional-neural-networks-hand-in","title":"CiT-Net: Convolutional Neural Networks Hand in Hand with Vision Transformers for Medical Image Segmentation","date":"2023-06-06","arxiv_id":"2306.03373","repositories_listed":1,"syntology":{"n":23,"n_ran":15,"n_constructed":15,"n_ran_checked":15,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":23,"phrase":"15 ran (of which 15 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified; every one of the 15 samples that ran constructed an object rather than computing a result","sample_list":"/paper/cit-net-convolutional-neural-networks-hand-in#ran","syntology_url":"https://syntology.ai/paper/2306.03373","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.03373"}},"official":{"repos":["sr0920/cit-net"],"state":"official (archive's flag): 15 ran","n_ran":15,"n_constructed":15,"n_ran_no_instrument_failure":15,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/conditional-diffusion-models-for-weakly","slug":"conditional-diffusion-models-for-weakly","title":"Conditional Diffusion Models for Weakly Supervised Medical Image Segmentation","date":"2023-06-06","arxiv_id":"2306.03878","repositories_listed":1,"syntology":null},{"url":"/paper/curriculum-based-augmented-fourier-domain","slug":"curriculum-based-augmented-fourier-domain","title":"Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentation","date":"2023-06-06","arxiv_id":"2306.03511","repositories_listed":1,"syntology":null},{"url":"/paper/dformer-diffusion-guided-transformer-for","slug":"dformer-diffusion-guided-transformer-for","title":"DFormer: Diffusion-guided Transformer for Universal Image Segmentation","date":"2023-06-06","arxiv_id":"2306.03437","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":4,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 1 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/dformer-diffusion-guided-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2306.03437","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.03437"}},"official":{"repos":["cp3wan/dformer"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/dvis-decoupled-video-instance-segmentation","slug":"dvis-decoupled-video-instance-segmentation","title":"DVIS: Decoupled Video Instance Segmentation Framework","date":"2023-06-06","arxiv_id":"2306.03413","repositories_listed":1,"syntology":null},{"url":"/paper/instructive-feature-enhancement-for","slug":"instructive-feature-enhancement-for","title":"Instructive Feature Enhancement for Dichotomous Medical Image Segmentation","date":"2023-06-06","arxiv_id":"2306.03497","repositories_listed":1,"syntology":null},{"url":"/paper/sgat4pass-spherical-geometry-aware","slug":"sgat4pass-spherical-geometry-aware","title":"SGAT4PASS: Spherical Geometry-Aware Transformer for PAnoramic Semantic Segmentation","date":"2023-06-06","arxiv_id":"2306.03403","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":5,"n_instrument":4,"n_unverified":2,"n_honours":1,"n_violates":1,"n_no_contract":3,"n_pointer_only":11,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 1 violated, 3 with no contract checked; 4 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/sgat4pass-spherical-geometry-aware#ran","syntology_url":"https://syntology.ai/paper/2306.03403","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.03403"}},"official":{"repos":["tencentarc/sgat4pass"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-label-free-scene-understanding-by","slug":"towards-label-free-scene-understanding-by","title":"Towards Label-free Scene Understanding by Vision Foundation Models","date":"2023-06-06","arxiv_id":"2306.03899","repositories_listed":1,"syntology":null},{"url":"/paper/asymmetric-patch-sampling-for-contrastive","slug":"asymmetric-patch-sampling-for-contrastive","title":"Asymmetric Patch Sampling for Contrastive Learning","date":"2023-06-05","arxiv_id":"2306.02854","repositories_listed":1,"syntology":null},{"url":"/paper/cyclic-learning-bridging-image-level-labels","slug":"cyclic-learning-bridging-image-level-labels","title":"Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation","date":"2023-06-05","arxiv_id":"2306.02691","repositories_listed":1,"syntology":null},{"url":"/paper/dual-self-distillation-of-u-shaped-networks","slug":"dual-self-distillation-of-u-shaped-networks","title":"Volumetric medical image segmentation through dual self-distillation in U-shaped networks","date":"2023-06-05","arxiv_id":"2306.03271","repositories_listed":1,"syntology":null},{"url":"/paper/3rd-place-solution-for-pvuw2023-vss-track-a","slug":"3rd-place-solution-for-pvuw2023-vss-track-a","title":"3rd Place Solution for PVUW2023 VSS Track: A Large Model for Semantic Segmentation on VSPW","date":"2023-06-04","arxiv_id":"2306.02291","repositories_listed":1,"syntology":null},{"url":"/paper/sam3d-zero-shot-3d-object-detection-via","slug":"sam3d-zero-shot-3d-object-detection-via","title":"SAM3D: Zero-Shot 3D Object Detection via Segment Anything Model","date":"2023-06-04","arxiv_id":"2306.02245","repositories_listed":1,"syntology":null},{"url":"/paper/using-unreliable-pseudo-labels-for-label","slug":"using-unreliable-pseudo-labels-for-label","title":"Using Unreliable Pseudo-Labels for Label-Efficient Semantic Segmentation","date":"2023-06-04","arxiv_id":"2306.02314","repositories_listed":1,"syntology":null},{"url":"/paper/balancing-logit-variation-for-long-tailed-1","slug":"balancing-logit-variation-for-long-tailed-1","title":"Balancing Logit Variation for Long-tailed Semantic Segmentation","date":"2023-06-03","arxiv_id":"2306.02061","repositories_listed":1,"syntology":null},{"url":"/paper/content-aware-token-sharing-for-efficient-1","slug":"content-aware-token-sharing-for-efficient-1","title":"Content-aware Token Sharing for Efficient Semantic Segmentation with Vision Transformers","date":"2023-06-03","arxiv_id":"2306.02095","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-multi-grained-knowledge-reuse-for","slug":"efficient-multi-grained-knowledge-reuse-for","title":"Evolving Knowledge Mining for Class Incremental Segmentation","date":"2023-06-03","arxiv_id":"2306.02027","repositories_listed":1,"syntology":null},{"url":"/paper/robust-and-generalisable-segmentation-of","slug":"robust-and-generalisable-segmentation-of","title":"Robust and Generalisable Segmentation of Subtle Epilepsy-causing Lesions: a Graph Convolutional Approach","date":"2023-06-02","arxiv_id":"2306.01375","repositories_listed":1,"syntology":null},{"url":"/paper/towards-in-context-scene-understanding","slug":"towards-in-context-scene-understanding","title":"Towards In-context Scene Understanding","date":"2023-06-02","arxiv_id":"2306.01667","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":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/towards-in-context-scene-understanding#ran","syntology_url":"https://syntology.ai/paper/2306.01667","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.01667"}},"official":null}},{"url":"/paper/transformer-based-annotation-bias-aware","slug":"transformer-based-annotation-bias-aware","title":"Transformer-based Annotation Bias-aware Medical Image Segmentation","date":"2023-06-02","arxiv_id":"2306.01340","repositories_listed":1,"syntology":null},{"url":"/paper/a-robust-feature-downsampling-module-for","slug":"a-robust-feature-downsampling-module-for","title":"A Robust Feature Downsampling Module for Remote Sensing Visual Tasks","date":"2023-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/desam-decoupling-segment-anything-model-for","slug":"desam-decoupling-segment-anything-model-for","title":"DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation","date":"2023-06-01","arxiv_id":"2306.00499","repositories_listed":1,"syntology":null},{"url":"/paper/evaluation-of-multi-indicator-and-multi-organ","slug":"evaluation-of-multi-indicator-and-multi-organ","title":"Evaluation of Multi-indicator And Multi-organ Medical Image Segmentation Models","date":"2023-06-01","arxiv_id":"2306.00446","repositories_listed":1,"syntology":null},{"url":"/paper/geo-tiles-for-semantic-segmentation-of-earth","slug":"geo-tiles-for-semantic-segmentation-of-earth","title":"Geo-Tiles for Semantic Segmentation of Earth Observation Imagery","date":"2023-06-01","arxiv_id":"2306.00823","repositories_listed":1,"syntology":null},{"url":"/paper/pre-training-auto-generated-volumetric-shapes","slug":"pre-training-auto-generated-volumetric-shapes","title":"Pre-Training Auto-Generated Volumetric Shapes for 3D Medical Image Segmentation","date":"2023-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/robust-t-loss-for-medical-image-segmentation","slug":"robust-t-loss-for-medical-image-segmentation","title":"Robust T-Loss for Medical Image Segmentation","date":"2023-06-01","arxiv_id":"2306.00753","repositories_listed":1,"syntology":null},{"url":"/paper/s-2-me-spatial-spectral-mutual-teaching-and","slug":"s-2-me-spatial-spectral-mutual-teaching-and","title":"S$^2$ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation","date":"2023-06-01","arxiv_id":"2306.00451","repositories_listed":1,"syntology":null},{"url":"/paper/a-unified-framework-for-u-net-design-and-1","slug":"a-unified-framework-for-u-net-design-and-1","title":"A Unified Framework for U-Net Design and Analysis","date":"2023-05-31","arxiv_id":"2305.19638","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":3,"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) · 1 unverified","sample_list":"/paper/a-unified-framework-for-u-net-design-and-1#ran","syntology_url":"https://syntology.ai/paper/2305.19638","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19638"}},"official":{"repos":["fabianfalck/unet-design"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/deepmerge-deep-learning-based-region-merging","slug":"deepmerge-deep-learning-based-region-merging","title":"DeepMerge: Deep-Learning-Based Region-Merging for Image Segmentation","date":"2023-05-31","arxiv_id":"2305.19787","repositories_listed":1,"syntology":null},{"url":"/paper/democratizing-pathological-image-segmentation","slug":"democratizing-pathological-image-segmentation","title":"Democratizing Pathological Image Segmentation with Lay Annotators via Molecular-empowered Learning","date":"2023-05-31","arxiv_id":"2306.00047","repositories_listed":1,"syntology":null},{"url":"/paper/fast-snn-fast-spiking-neural-network-by","slug":"fast-snn-fast-spiking-neural-network-by","title":"Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANN","date":"2023-05-31","arxiv_id":"2305.19868","repositories_listed":1,"syntology":null},{"url":"/paper/treasure-in-distribution-a-domain","slug":"treasure-in-distribution-a-domain","title":"Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation","date":"2023-05-31","arxiv_id":"2305.19949","repositories_listed":1,"syntology":null},{"url":"/paper/independent-component-alignment-for-multi-1","slug":"independent-component-alignment-for-multi-1","title":"Independent Component Alignment for Multi-Task Learning","date":"2023-05-30","arxiv_id":"2305.19000","repositories_listed":1,"syntology":null},{"url":"/paper/joint-optimization-of-class-specific-training","slug":"joint-optimization-of-class-specific-training","title":"Joint Optimization of Class-Specific Training- and Test-Time Data Augmentation in Segmentation","date":"2023-05-30","arxiv_id":"2305.19084","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-pathological-image","slug":"semi-supervised-pathological-image","title":"Semi-supervised Pathological Image Segmentation via Cross Distillation of Multiple Attentions","date":"2023-05-30","arxiv_id":"2305.18830","repositories_listed":1,"syntology":null},{"url":"/paper/camodiffusion-camouflaged-object-detection","slug":"camodiffusion-camouflaged-object-detection","title":"CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion Models","date":"2023-05-29","arxiv_id":"2305.17932","repositories_listed":1,"syntology":null},{"url":"/paper/contextual-object-detection-with-multimodal","slug":"contextual-object-detection-with-multimodal","title":"Contextual Object Detection with Multimodal Large Language Models","date":"2023-05-29","arxiv_id":"2305.18279","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/contextual-object-detection-with-multimodal#ran","syntology_url":"https://syntology.ai/paper/2305.18279","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18279"}},"official":{"repos":["yuhangzang/contextdet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/few-shot-rotation-invariant-aerial-image","slug":"few-shot-rotation-invariant-aerial-image","title":"Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation","date":"2023-05-29","arxiv_id":"2306.11734","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/few-shot-rotation-invariant-aerial-image#ran","syntology_url":"https://syntology.ai/paper/2306.11734","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.11734"}},"official":{"repos":["caoql98/frinet"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/hierarchical-neural-memory-network-for-low-1","slug":"hierarchical-neural-memory-network-for-low-1","title":"Hierarchical Neural Memory Network for Low Latency Event Processing","date":"2023-05-29","arxiv_id":"2305.17852","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/hierarchical-neural-memory-network-for-low-1#ran","syntology_url":"https://syntology.ai/paper/2305.17852","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.17852"}},"official":{"repos":["hamarh/HMNet_pth"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/aims-all-inclusive-multi-level-segmentation","slug":"aims-all-inclusive-multi-level-segmentation","title":"AIMS: All-Inclusive Multi-Level Segmentation","date":"2023-05-28","arxiv_id":"2305.17768","repositories_listed":1,"syntology":null},{"url":"/paper/simpson-simplifying-photo-cleanup-with-single-1","slug":"simpson-simplifying-photo-cleanup-with-single-1","title":"SimpSON: Simplifying Photo Cleanup with Single-Click Distracting Object Segmentation Network","date":"2023-05-28","arxiv_id":"2305.17624","repositories_listed":1,"syntology":null},{"url":"/paper/ccdwt-gan-generative-adversarial-networks","slug":"ccdwt-gan-generative-adversarial-networks","title":"CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization","date":"2023-05-27","arxiv_id":"2305.17420","repositories_listed":1,"syntology":null},{"url":"/paper/condition-invariant-semantic-segmentation","slug":"condition-invariant-semantic-segmentation","title":"Condition-Invariant Semantic Segmentation","date":"2023-05-27","arxiv_id":"2305.17349","repositories_listed":1,"syntology":null},{"url":"/paper/detect-any-shadow-segment-anything-for-video","slug":"detect-any-shadow-segment-anything-for-video","title":"Detect Any Shadow: Segment Anything for Video Shadow Detection","date":"2023-05-26","arxiv_id":"2305.16698","repositories_listed":1,"syntology":null},{"url":"/paper/gratt-vis-gated-residual-attention-for-auto","slug":"gratt-vis-gated-residual-attention-for-auto","title":"GRAtt-VIS: Gated Residual Attention for Auto Rectifying Video Instance Segmentation","date":"2023-05-26","arxiv_id":"2305.17096","repositories_listed":1,"syntology":null},{"url":"/paper/openvis-open-vocabulary-video-instance","slug":"openvis-open-vocabulary-video-instance","title":"OpenVIS: Open-vocabulary Video Instance Segmentation","date":"2023-05-26","arxiv_id":"2305.16835","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-segmentation-of-sparse-irregular-1","slug":"semantic-segmentation-of-sparse-irregular-1","title":"Semantic segmentation of sparse irregular point clouds for leaf/wood discrimination","date":"2023-05-26","arxiv_id":"2305.16963","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/semantic-segmentation-of-sparse-irregular-1#ran","syntology_url":"https://syntology.ai/paper/2305.16963","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.16963"}},"official":{"repos":["na1an/phd_mission"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/soc-semantic-assisted-object-cluster-for","slug":"soc-semantic-assisted-object-cluster-for","title":"SOC: Semantic-Assisted Object Cluster for Referring Video Object Segmentation","date":"2023-05-26","arxiv_id":"2305.17011","repositories_listed":1,"syntology":null},{"url":"/paper/sssegmenation-an-open-source-supervised","slug":"sssegmenation-an-open-source-supervised","title":"SSSegmenation: An Open Source Supervised Semantic Segmentation Toolbox Based on PyTorch","date":"2023-05-26","arxiv_id":"2305.17091","repositories_listed":1,"syntology":null},{"url":"/paper/energy-based-detection-of-adverse-weather","slug":"energy-based-detection-of-adverse-weather","title":"Energy-based Detection of Adverse Weather Effects in LiDAR Data","date":"2023-05-25","arxiv_id":"2305.16129","repositories_listed":1,"syntology":null},{"url":"/paper/growsp-unsupervised-semantic-segmentation-of-1","slug":"growsp-unsupervised-semantic-segmentation-of-1","title":"GrowSP: Unsupervised Semantic Segmentation of 3D Point Clouds","date":"2023-05-25","arxiv_id":"2305.16404","repositories_listed":1,"syntology":null},{"url":"/paper/knowledge-diffusion-for-distillation-1","slug":"knowledge-diffusion-for-distillation-1","title":"Knowledge Diffusion for Distillation","date":"2023-05-25","arxiv_id":"2305.15712","repositories_listed":1,"syntology":null},{"url":"/paper/referred-by-multi-modality-a-unified-temporal","slug":"referred-by-multi-modality-a-unified-temporal","title":"Referred by Multi-Modality: A Unified Temporal Transformer for Video Object Segmentation","date":"2023-05-25","arxiv_id":"2305.16318","repositories_listed":1,"syntology":null},{"url":"/paper/self-aware-and-cross-sample-prototypical","slug":"self-aware-and-cross-sample-prototypical","title":"Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image Segmentation","date":"2023-05-25","arxiv_id":"2305.16214","repositories_listed":1,"syntology":null},{"url":"/paper/a-tale-of-two-features-stable-diffusion","slug":"a-tale-of-two-features-stable-diffusion","title":"A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence","date":"2023-05-24","arxiv_id":"2305.15347","repositories_listed":1,"syntology":null},{"url":"/paper/gamus-a-geometry-aware-multi-modal-semantic","slug":"gamus-a-geometry-aware-multi-modal-semantic","title":"GAMUS: A Geometry-aware Multi-modal Semantic Segmentation Benchmark for Remote Sensing Data","date":"2023-05-24","arxiv_id":"2305.14914","repositories_listed":1,"syntology":null},{"url":"/paper/an-accelerated-pipeline-for-multi-label-renal","slug":"an-accelerated-pipeline-for-multi-label-renal","title":"An Accelerated Pipeline for Multi-label Renal Pathology Image Segmentation at the Whole Slide Image Level","date":"2023-05-23","arxiv_id":"2305.14566","repositories_listed":1,"syntology":null},{"url":"/paper/mianet-aggregating-unbiased-instance-and-1","slug":"mianet-aggregating-unbiased-instance-and-1","title":"MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation","date":"2023-05-23","arxiv_id":"2305.13864","repositories_listed":1,"syntology":null},{"url":"/paper/pulling-target-to-source-a-new-perspective-on","slug":"pulling-target-to-source-a-new-perspective-on","title":"Pulling Target to Source: A New Perspective on Domain Adaptive Semantic Segmentation","date":"2023-05-23","arxiv_id":"2305.13752","repositories_listed":1,"syntology":null},{"url":"/paper/sad-segment-any-rgbd","slug":"sad-segment-any-rgbd","title":"SAD: Segment Any RGBD","date":"2023-05-23","arxiv_id":"2305.14207","repositories_listed":1,"syntology":null},{"url":"/paper/vdd-varied-drone-dataset-for-semantic","slug":"vdd-varied-drone-dataset-for-semantic","title":"VDD: Varied Drone Dataset for Semantic Segmentation","date":"2023-05-23","arxiv_id":"2305.13608","repositories_listed":1,"syntology":null},{"url":"/paper/dermsynth3d-synthesis-of-in-the-wild","slug":"dermsynth3d-synthesis-of-in-the-wild","title":"DermSynth3D: Synthesis of in-the-wild Annotated Dermatology Images","date":"2023-05-22","arxiv_id":"2305.12621","repositories_listed":1,"syntology":null},{"url":"/paper/hgformer-hierarchical-grouping-transformer-1","slug":"hgformer-hierarchical-grouping-transformer-1","title":"HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation","date":"2023-05-22","arxiv_id":"2305.13031","repositories_listed":1,"syntology":null},{"url":"/paper/matcher-segment-anything-with-one-shot-using","slug":"matcher-segment-anything-with-one-shot-using","title":"Matcher: Segment Anything with One Shot Using All-Purpose Feature Matching","date":"2023-05-22","arxiv_id":"2305.13310","repositories_listed":1,"syntology":null},{"url":"/paper/readmem-robust-embedding-association-for-a","slug":"readmem-robust-embedding-association-for-a","title":"READMem: Robust Embedding Association for a Diverse Memory in Unconstrained Video Object Segmentation","date":"2023-05-22","arxiv_id":"2305.12823","repositories_listed":1,"syntology":null},{"url":"/paper/restore-anything-pipeline-segment-anything","slug":"restore-anything-pipeline-segment-anything","title":"Restore Anything Pipeline: Segment Anything Meets Image Restoration","date":"2023-05-22","arxiv_id":"2305.13093","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-promoted-debiasing-and-background-1","slug":"semantic-promoted-debiasing-and-background-1","title":"Semantic-Promoted Debiasing and Background Disambiguation for Zero-Shot Instance Segmentation","date":"2023-05-22","arxiv_id":"2305.13173","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-based-detection-of-adversarial","slug":"uncertainty-based-detection-of-adversarial","title":"Uncertainty-based Detection of Adversarial Attacks in Semantic Segmentation","date":"2023-05-22","arxiv_id":"2305.12825","repositories_listed":1,"syntology":null},{"url":"/paper/uvosam-a-mask-free-paradigm-for-unsupervised","slug":"uvosam-a-mask-free-paradigm-for-unsupervised","title":"UVOSAM: A Mask-free Paradigm for Unsupervised Video Object Segmentation via Segment Anything Model","date":"2023-05-22","arxiv_id":"2305.12659","repositories_listed":1,"syntology":null},{"url":"/paper/jetseg-efficient-real-time-semantic","slug":"jetseg-efficient-real-time-semantic","title":"JetSeg: Efficient Real-Time Semantic Segmentation Model for Low-Power GPU-Embedded Systems","date":"2023-05-19","arxiv_id":"2305.11419","repositories_listed":1,"syntology":null},{"url":"/paper/when-sam-meets-shadow-detection","slug":"when-sam-meets-shadow-detection","title":"When SAM Meets Shadow Detection","date":"2023-05-19","arxiv_id":"2305.11513","repositories_listed":1,"syntology":null},{"url":"/paper/domain-adaptive-sim-to-real-segmentation-of","slug":"domain-adaptive-sim-to-real-segmentation-of","title":"Domain Adaptive Sim-to-Real Segmentation of Oropharyngeal Organs","date":"2023-05-18","arxiv_id":"2305.10883","repositories_listed":1,"syntology":null},{"url":"/paper/quantifying-the-robustness-of-deep","slug":"quantifying-the-robustness-of-deep","title":"Quantifying the robustness of deep multispectral segmentation models against natural perturbations and data poisoning","date":"2023-05-18","arxiv_id":"2305.11347","repositories_listed":1,"syntology":null},{"url":"/paper/ultra-high-resolution-segmentation-with-ultra-1","slug":"ultra-high-resolution-segmentation-with-ultra-1","title":"Ultra-High Resolution Segmentation with Ultra-Rich Context: A Novel Benchmark","date":"2023-05-18","arxiv_id":"2305.10899","repositories_listed":1,"syntology":null},{"url":"/paper/explain-any-concept-segment-anything-meets-1","slug":"explain-any-concept-segment-anything-meets-1","title":"Explain Any Concept: Segment Anything Meets Concept-Based Explanation","date":"2023-05-17","arxiv_id":"2305.10289","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/explain-any-concept-segment-anything-meets-1#ran","syntology_url":"https://syntology.ai/paper/2305.10289","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.10289"}},"official":{"repos":["Jerry00917/samshap"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/object-segmentation-by-mining-cross-modal","slug":"object-segmentation-by-mining-cross-modal","title":"Object Segmentation by Mining Cross-Modal Semantics","date":"2023-05-17","arxiv_id":"2305.10469","repositories_listed":1,"syntology":null},{"url":"/paper/concurrent-misclassification-and-out-of","slug":"concurrent-misclassification-and-out-of","title":"Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow","date":"2023-05-16","arxiv_id":"2305.09610","repositories_listed":1,"syntology":null},{"url":"/paper/leaf-only-sam-a-segment-anything-pipeline-for","slug":"leaf-only-sam-a-segment-anything-pipeline-for","title":"Leaf Only SAM: A Segment Anything Pipeline for Zero-Shot Automated Leaf Segmentation","date":"2023-05-16","arxiv_id":"2305.09418","repositories_listed":1,"syntology":null},{"url":"/paper/multi-level-global-context-cross-consistency","slug":"multi-level-global-context-cross-consistency","title":"Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model","date":"2023-05-16","arxiv_id":"2305.09447","repositories_listed":1,"syntology":null},{"url":"/paper/bridging-the-domain-gap-self-supervised-3d","slug":"bridging-the-domain-gap-self-supervised-3d","title":"Bridging the Domain Gap: Self-Supervised 3D Scene Understanding with Foundation Models","date":"2023-05-15","arxiv_id":"2305.08776","repositories_listed":1,"syntology":null}],"record_sha256":"93d4821435e52183c655453faa122b7d29743ac53558a454a239fc3b13fa5e99","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}