{"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/instance-segmentation/papers/12","list_of":"/task/instance-segmentation","task":"Instance 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":12,"pages_in_order":23,"rows_per_page":100,"rows":[1101,1200],"of":2262,"counts":{"archive_papers_tagged":2262,"with_a_code_link":1158,"where_syntology_ran_a_sample":324,"not_listed_spam_title":0,"listed":2262,"listed_where_code_ran":324,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":289,"every_run_a_failure_of_syntologys_instrument":35,"listed_with_a_run_with_no_instrument_failure":289,"listed_every_run_a_failure_of_syntologys_instrument":35,"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/instance-segmentation","prev":"/task/instance-segmentation/papers/11","next":"/task/instance-segmentation/papers/13","papers":[{"url":"/paper/a-spectral-approach-to-unsupervised-object","slug":"a-spectral-approach-to-unsupervised-object","title":"A 3D Convolutional Approach to Spectral Object Segmentation in Space and Time","date":"2019-07-05","arxiv_id":"1907.02731","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/a-spectral-approach-to-unsupervised-object#ran","syntology_url":"https://syntology.ai/paper/1907.02731","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1907.02731"}},"official":{"repos":["bit-ml/sfseg"],"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/where-are-the-masks-instance-segmentation","slug":"where-are-the-masks-instance-segmentation","title":"Where are the Masks: Instance Segmentation with Image-level Supervision","date":"2019-07-02","arxiv_id":"1907.01430","repositories_listed":1,"syntology":null},{"url":"/paper/automated-crater-shape-retrieval-using-weakly","slug":"automated-crater-shape-retrieval-using-weakly","title":"Automated crater shape retrieval using weakly-supervised deep learning","date":"2019-06-20","arxiv_id":"1906.08826","repositories_listed":1,"syntology":null},{"url":"/paper/learning-instance-occlusion-for-panoptic","slug":"learning-instance-occlusion-for-panoptic","title":"Learning Instance Occlusion for Panoptic Segmentation","date":"2019-06-13","arxiv_id":"1906.05896","repositories_listed":1,"syntology":null},{"url":"/paper/learning-object-bounding-boxes-for-3d","slug":"learning-object-bounding-boxes-for-3d","title":"Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds","date":"2019-06-04","arxiv_id":"1906.01140","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-object-bounding-boxes-for-3d#ran","syntology_url":"https://syntology.ai/paper/1906.01140","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.01140"}},"official":{"repos":["Yang7879/3D-BoNet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/190600358","slug":"190600358","title":"Data Augmentation for Object Detection via Progressive and Selective Instance-Switching","date":"2019-06-02","arxiv_id":"1906.00358","repositories_listed":1,"syntology":null},{"url":"/paper/amodal-instance-segmentation-with-kins","slug":"amodal-instance-segmentation-with-kins","title":"Amodal Instance Segmentation With KINS Dataset","date":"2019-06-01","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/learning-semantics-aware-distance-map-with","slug":"learning-semantics-aware-distance-map-with","title":"Learning Semantics-aware Distance Map with Semantics Layering Network for Amodal Instance Segmentation","date":"2019-05-30","arxiv_id":"1905.12898","repositories_listed":1,"syntology":null},{"url":"/paper/straight-to-shapes-real-time-instance","slug":"straight-to-shapes-real-time-instance","title":"Straight to Shapes++: Real-time Instance Segmentation Made More Accurate","date":"2019-05-27","arxiv_id":"1905.11358","repositories_listed":1,"syntology":null},{"url":"/paper/leveraging-domain-knowledge-to-improve-em","slug":"leveraging-domain-knowledge-to-improve-em","title":"Leveraging Domain Knowledge to Improve Microscopy Image Segmentation with Lifted Multicuts","date":"2019-05-25","arxiv_id":"1905.10535","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-learned-random-walker-for-seeded-1","slug":"end-to-end-learned-random-walker-for-seeded-1","title":"End-to-End Learned Random Walker for Seeded Image Segmentation","date":"2019-05-22","arxiv_id":"1905.09045","repositories_listed":1,"syntology":null},{"url":"/paper/vision-based-robotic-grasping-from-object","slug":"vision-based-robotic-grasping-from-object","title":"Vision-based Robotic Grasping From Object Localization, Object Pose Estimation to Grasp Estimation for Parallel Grippers: A Review","date":"2019-05-16","arxiv_id":"1905.06658","repositories_listed":1,"syntology":null},{"url":"/paper/budgeted-training-rethinking-deep-neural","slug":"budgeted-training-rethinking-deep-neural","title":"Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints","date":"2019-05-12","arxiv_id":"1905.04753","repositories_listed":1,"syntology":null},{"url":"/paper/actor-critic-instance-segmentation","slug":"actor-critic-instance-segmentation","title":"Actor-Critic Instance Segmentation","date":"2019-04-10","arxiv_id":"1904.05126","repositories_listed":1,"syntology":null},{"url":"/paper/instance-segmentation-of-biological-images","slug":"instance-segmentation-of-biological-images","title":"Instance Segmentation of Biological Images Using Harmonic Embeddings","date":"2019-04-10","arxiv_id":"1904.05257","repositories_listed":1,"syntology":null},{"url":"/paper/shapemask-learning-to-segment-novel-objects","slug":"shapemask-learning-to-segment-novel-objects","title":"ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors","date":"2019-04-05","arxiv_id":"1904.03239","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-learning-via-conditional","slug":"self-supervised-learning-via-conditional","title":"Self-Supervised Learning via Conditional Motion Propagation","date":"2019-03-27","arxiv_id":"1903.11412","repositories_listed":1,"syntology":{"n":14,"n_ran":14,"n_constructed":0,"n_ran_checked":13,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":1,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/self-supervised-learning-via-conditional#ran","syntology_url":"https://syntology.ai/paper/1903.11412","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.11412"}},"official":{"repos":["XiaohangZhan/conditional-motion-propagation"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/blvd-building-a-large-scale-5d-semantics","slug":"blvd-building-a-large-scale-5d-semantics","title":"BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving","date":"2019-03-15","arxiv_id":"1903.06405","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/blvd-building-a-large-scale-5d-semantics#ran","syntology_url":"https://syntology.ai/paper/1903.06405","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.06405"}},"official":{"repos":["VCCIV/BLVD"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/neural-scene-decomposition-for-multi-person","slug":"neural-scene-decomposition-for-multi-person","title":"Neural Scene Decomposition for Multi-Person Motion Capture","date":"2019-03-13","arxiv_id":"1903.05684","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-complementary-parts-models","slug":"weakly-supervised-complementary-parts-models","title":"Weakly Supervised Complementary Parts Models for Fine-Grained Image Classification from the Bottom Up","date":"2019-03-07","arxiv_id":"1903.02827","repositories_listed":1,"syntology":{"n":9,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"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) · 2 unverified","sample_list":"/paper/weakly-supervised-complementary-parts-models#ran","syntology_url":"https://syntology.ai/paper/1903.02827","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.02827"}},"official":null}},{"url":"/paper/single-image-piece-wise-planar-3d","slug":"single-image-piece-wise-planar-3d","title":"Single-Image Piece-wise Planar 3D Reconstruction via Associative Embedding","date":"2019-02-26","arxiv_id":"1902.09777","repositories_listed":1,"syntology":{"n":13,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":11,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"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) · 11 unverified","sample_list":"/paper/single-image-piece-wise-planar-3d#ran","syntology_url":"https://syntology.ai/paper/1902.09777","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.09777"}},"official":{"repos":["svip-lab/PlanarReconstruction"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/u-netplus-a-modified-encoder-decoder-u-net","slug":"u-netplus-a-modified-encoder-decoder-u-net","title":"U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instrument","date":"2019-02-24","arxiv_id":"1902.08994","repositories_listed":1,"syntology":null},{"url":"/paper/masc-multi-scale-affinity-with-sparse","slug":"masc-multi-scale-affinity-with-sparse","title":"MASC: Multi-scale Affinity with Sparse Convolution for 3D Instance Segmentation","date":"2019-02-12","arxiv_id":"1902.04478","repositories_listed":1,"syntology":null},{"url":"/paper/towards-segmenting-everything-that-moves","slug":"towards-segmenting-everything-that-moves","title":"Towards Segmenting Anything That Moves","date":"2019-02-11","arxiv_id":"1902.03715","repositories_listed":1,"syntology":null},{"url":"/paper/single-network-panoptic-segmentation-for","slug":"single-network-panoptic-segmentation-for","title":"Single Network Panoptic Segmentation for Street Scene Understanding","date":"2019-02-07","arxiv_id":"1902.02678","repositories_listed":1,"syntology":null},{"url":"/paper/instance-segmentation-as-image-segmentation","slug":"instance-segmentation-as-image-segmentation","title":"Instance Segmentation as Image Segmentation Annotation","date":"2019-02-01","arxiv_id":"1902.05498","repositories_listed":1,"syntology":null},{"url":"/paper/4d-generic-video-object-proposals","slug":"4d-generic-video-object-proposals","title":"4D Generic Video Object Proposals","date":"2019-01-26","arxiv_id":"1901.09260","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/4d-generic-video-object-proposals#ran","syntology_url":"https://syntology.ai/paper/1901.09260","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.09260"}},"official":{"repos":["aljosaosep/4DGVT"],"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/upsnet-a-unified-panoptic-segmentation","slug":"upsnet-a-unified-panoptic-segmentation","title":"UPSNet: A Unified Panoptic Segmentation Network","date":"2019-01-12","arxiv_id":"1901.03784","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/upsnet-a-unified-panoptic-segmentation#ran","syntology_url":"https://syntology.ai/paper/1901.03784","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.03784"}},"official":{"repos":["uber-research/UPSNet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mid-fusion-octree-based-object-level-multi","slug":"mid-fusion-octree-based-object-level-multi","title":"MID-Fusion: Octree-based Object-Level Multi-Instance Dynamic SLAM","date":"2018-12-19","arxiv_id":"1812.07976","repositories_listed":1,"syntology":null},{"url":"/paper/3d-sis-3d-semantic-instance-segmentation-of","slug":"3d-sis-3d-semantic-instance-segmentation-of","title":"3D-SIS: 3D Semantic Instance Segmentation of RGB-D Scans","date":"2018-12-17","arxiv_id":"1812.07003","repositories_listed":1,"syntology":{"n":2,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"0 ran · 2 unverified","sample_list":"/paper/3d-sis-3d-semantic-instance-segmentation-of#ran","syntology_url":"https://syntology.ai/paper/1812.07003","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.07003"}},"official":{"repos":["Sekunde/3D-SIS"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":[]}}},{"url":"/paper/gspn-generative-shape-proposal-network-for-3d","slug":"gspn-generative-shape-proposal-network-for-3d","title":"GSPN: Generative Shape Proposal Network for 3D Instance Segmentation in Point Cloud","date":"2018-12-08","arxiv_id":"1812.03320","repositories_listed":1,"syntology":{"n":8,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/gspn-generative-shape-proposal-network-for-3d#ran","syntology_url":"https://syntology.ai/paper/1812.03320","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1812.03320"}},"official":{"repos":["ericyi/GSPN"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/affinity-derivation-and-graph-merge-for","slug":"affinity-derivation-and-graph-merge-for","title":"Affinity Derivation and Graph Merge for Instance Segmentation","date":"2018-11-27","arxiv_id":"1811.10870","repositories_listed":1,"syntology":{"n":11,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"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) · 4 unverified","sample_list":"/paper/affinity-derivation-and-graph-merge-for#ran","syntology_url":"https://syntology.ai/paper/1811.10870","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.10870"}},"official":{"repos":["xck36/GMIS"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/rethinking-imagenet-pre-training","slug":"rethinking-imagenet-pre-training","title":"Rethinking ImageNet Pre-training","date":"2018-11-21","arxiv_id":"1811.08883","repositories_listed":1,"syntology":null},{"url":"/paper/domain-randomization-for-scene-specific-car","slug":"domain-randomization-for-scene-specific-car","title":"Domain Randomization for Scene-Specific Car Detection and Pose Estimation","date":"2018-11-14","arxiv_id":"1811.05939","repositories_listed":1,"syntology":null},{"url":"/paper/road-damage-detection-and-classification-in","slug":"road-damage-detection-and-classification-in","title":"Road Damage Detection And Classification In Smartphone Captured Images Using Mask R-CNN","date":"2018-11-12","arxiv_id":"1811.04535","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-and-semi-supervised-panoptic","slug":"weakly-and-semi-supervised-panoptic","title":"Weakly- and Semi-Supervised Panoptic Segmentation","date":"2018-08-10","arxiv_id":"1808.03575","repositories_listed":1,"syntology":null},{"url":"/paper/instance-segmentation-by-deep-coloring","slug":"instance-segmentation-by-deep-coloring","title":"Instance Segmentation by Deep Coloring","date":"2018-07-26","arxiv_id":"1807.10007","repositories_listed":1,"syntology":null},{"url":"/paper/learning-instance-segmentation-by-interaction","slug":"learning-instance-segmentation-by-interaction","title":"Learning Instance Segmentation by Interaction","date":"2018-06-21","arxiv_id":"1806.08354","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"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, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/learning-instance-segmentation-by-interaction#ran","syntology_url":"https://syntology.ai/paper/1806.08354","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.08354"}},"official":{"repos":["pathak22/seg-by-interaction"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/iterative-fully-convolutional-neural-networks","slug":"iterative-fully-convolutional-neural-networks","title":"Iterative fully convolutional neural networks for automatic vertebra segmentation and identification","date":"2018-04-12","arxiv_id":"1804.04383","repositories_listed":1,"syntology":null},{"url":"/paper/weakly-supervised-instance-segmentation-using-1","slug":"weakly-supervised-instance-segmentation-using-1","title":"Weakly Supervised Instance Segmentation using Class Peak Response","date":"2018-04-03","arxiv_id":"1804.00880","repositories_listed":1,"syntology":null},{"url":"/paper/predicting-future-instance-segmentation-by","slug":"predicting-future-instance-segmentation-by","title":"Predicting Future Instance Segmentation by Forecasting Convolutional Features","date":"2018-03-30","arxiv_id":"1803.11496","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-cluster-for-proposal-free","slug":"learning-to-cluster-for-proposal-free","title":"Learning to Cluster for Proposal-Free Instance Segmentation","date":"2018-03-17","arxiv_id":"1803.06459","repositories_listed":1,"syntology":null},{"url":"/paper/learning-to-segment-via-cut-and-paste","slug":"learning-to-segment-via-cut-and-paste","title":"Learning to Segment via Cut-and-Paste","date":"2018-03-16","arxiv_id":"1803.06414","repositories_listed":1,"syntology":null},{"url":"/paper/im2height-height-estimation-from-single","slug":"im2height-height-estimation-from-single","title":"IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network","date":"2018-02-28","arxiv_id":"1802.10249","repositories_listed":1,"syntology":null},{"url":"/paper/srda-generating-instance-segmentation","slug":"srda-generating-instance-segmentation","title":"SRDA: Generating Instance Segmentation Annotation Via Scanning, Reasoning And Domain Adaptation","date":"2018-01-26","arxiv_id":"1801.08839","repositories_listed":1,"syntology":null},{"url":"/paper/brain-tumor-segmentation-based-on-refined","slug":"brain-tumor-segmentation-based-on-refined","title":"Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss","date":"2017-12-25","arxiv_id":"1712.09093","repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-neural-networks-for-semantic","slug":"recurrent-neural-networks-for-semantic","title":"Recurrent Neural Networks for Semantic Instance Segmentation","date":"2017-12-02","arxiv_id":"1712.00617","repositories_listed":1,"syntology":null},{"url":"/paper/distance-to-center-of-mass-encoding-for","slug":"distance-to-center-of-mass-encoding-for","title":"Distance to Center of Mass Encoding for Instance Segmentation","date":"2017-11-24","arxiv_id":"1711.09060","repositories_listed":1,"syntology":null},{"url":"/paper/sgpn-similarity-group-proposal-network-for-3d","slug":"sgpn-similarity-group-proposal-network-for-3d","title":"SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation","date":"2017-11-23","arxiv_id":"1711.08588","repositories_listed":1,"syntology":null},{"url":"/paper/s4net-single-stage-salient-instance","slug":"s4net-single-stage-salient-instance","title":"S4Net: Single Stage Salient-Instance Segmentation","date":"2017-11-21","arxiv_id":"1711.07618","repositories_listed":1,"syntology":null},{"url":"/paper/fast-scene-understanding-for-autonomous","slug":"fast-scene-understanding-for-autonomous","title":"Fast Scene Understanding for Autonomous Driving","date":"2017-08-08","arxiv_id":"1708.02550","repositories_listed":1,"syntology":null},{"url":"/paper/co-fusion-real-time-segmentation-tracking-and","slug":"co-fusion-real-time-segmentation-tracking-and","title":"Co-Fusion: Real-time Segmentation, Tracking and Fusion of Multiple Objects","date":"2017-06-20","arxiv_id":"1706.06629","repositories_listed":1,"syntology":null},{"url":"/paper/pixelwise-instance-segmentation-with-a","slug":"pixelwise-instance-segmentation-with-a","title":"Pixelwise Instance Segmentation with a Dynamically Instantiated Network","date":"2017-04-07","arxiv_id":"1704.02386","repositories_listed":1,"syntology":null},{"url":"/paper/semantic-instance-segmentation-via-deep","slug":"semantic-instance-segmentation-via-deep","title":"Semantic Instance Segmentation via Deep Metric Learning","date":"2017-03-30","arxiv_id":"1703.10277","repositories_listed":1,"syntology":null},{"url":"/paper/scenenet-rgb-d-5m-photorealistic-images-of","slug":"scenenet-rgb-d-5m-photorealistic-images-of","title":"SceneNet RGB-D: 5M Photorealistic Images of Synthetic Indoor Trajectories with Ground Truth","date":"2016-12-15","arxiv_id":"1612.05079","repositories_listed":1,"syntology":null},{"url":"/paper/end-to-end-instance-segmentation-with","slug":"end-to-end-instance-segmentation-with","title":"End-to-End Instance Segmentation with Recurrent Attention","date":"2016-05-30","arxiv_id":"1605.09410","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/end-to-end-instance-segmentation-with#ran","syntology_url":"https://syntology.ai/paper/1605.09410","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1605.09410"}},"official":null}},{"url":"/paper/a-multipath-network-for-object-detection","slug":"a-multipath-network-for-object-detection","title":"A MultiPath Network for Object Detection","date":"2016-04-07","arxiv_id":"1604.02135","repositories_listed":1,"syntology":null},{"url":"/paper/learning-rich-features-from-rgb-d-images-for","slug":"learning-rich-features-from-rgb-d-images-for","title":"Learning Rich Features from RGB-D Images for Object Detection and Segmentation","date":"2014-07-22","arxiv_id":"1407.5736","repositories_listed":1,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":4,"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) · 4 unverified","sample_list":"/paper/learning-rich-features-from-rgb-d-images-for#ran","syntology_url":"https://syntology.ai/paper/1407.5736","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1407.5736"}},"official":null}},{"url":"/paper/score-scene-context-matters-in-open","slug":"score-scene-context-matters-in-open","title":"SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation","date":"2025-07-17","arxiv_id":"2507.12857","repositories_listed":0,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/score-scene-context-matters-in-open#ran","syntology_url":"https://syntology.ai/paper/2507.12857","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2507.12857"}},"official":null}},{"url":null,"slug":"tomato-multi-angle-multi-pose-dataset-for","title":"Tomato Multi-Angle Multi-Pose Dataset for Fine-Grained Phenotyping","date":"2025-07-15","arxiv_id":"2507.11279","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-appearance-geometric-cues-for-robust","title":"Beyond Appearance: Geometric Cues for Robust Video Instance Segmentation","date":"2025-07-08","arxiv_id":"2507.05948","repositories_listed":0,"syntology":null},{"url":null,"slug":"dreamgrasp-zero-shot-3d-multi-object","title":"DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation","date":"2025-07-08","arxiv_id":"2507.05627","repositories_listed":0,"syntology":null},{"url":null,"slug":"spade-spatial-aware-denoising-network-for","title":"SPADE: Spatial-Aware Denoising Network for Open-vocabulary Panoptic Scene Graph Generation with Long- and Local-range Context Reasoning","date":"2025-07-08","arxiv_id":"2507.05798","repositories_listed":0,"syntology":null},{"url":null,"slug":"votesplat-hough-voting-gaussian-splatting-for","title":"VoteSplat: Hough Voting Gaussian Splatting for 3D Scene Understanding","date":"2025-06-28","arxiv_id":"2506.22799","repositories_listed":0,"syntology":null},{"url":null,"slug":"leader360v-the-large-scale-real-world-360","title":"Leader360V: The Large-scale, Real-world 360 Video Dataset for Multi-task Learning in Diverse Environment","date":"2025-06-17","arxiv_id":"2506.14271","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-survey-on-video-scene-parsing","title":"A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects","date":"2025-06-16","arxiv_id":"2506.13552","repositories_listed":0,"syntology":null},{"url":null,"slug":"albert-advanced-localization-and","title":"ALBERT: Advanced Localization and Bidirectional Encoder Representations from Transformers for Automotive Damage Evaluation","date":"2025-06-12","arxiv_id":"2506.10524","repositories_listed":0,"syntology":null},{"url":null,"slug":"accurate-and-efficient-zero-shot-6d-pose","title":"Accurate and efficient zero-shot 6D pose estimation with frozen foundation models","date":"2025-06-11","arxiv_id":"2506.09784","repositories_listed":0,"syntology":null},{"url":null,"slug":"opensplat3d-open-vocabulary-3d-instance","title":"OpenSplat3D: Open-Vocabulary 3D Instance Segmentation using Gaussian Splatting","date":"2025-06-09","arxiv_id":"2506.07697","repositories_listed":0,"syntology":null},{"url":null,"slug":"sam2auto-auto-annotation-using-flash","title":"SAM2Auto: Auto Annotation Using FLASH","date":"2025-06-09","arxiv_id":"2506.07850","repositories_listed":0,"syntology":null},{"url":null,"slug":"you-only-estimate-once-unified-one-stage-real","title":"You Only Estimate Once: Unified, One-stage, Real-Time Category-level Articulated Object 6D Pose Estimation for Robotic Grasping","date":"2025-06-06","arxiv_id":"2506.05719","repositories_listed":0,"syntology":null},{"url":null,"slug":"bringing-sam-to-new-heights-leveraging","title":"Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery","date":"2025-06-05","arxiv_id":"2506.04970","repositories_listed":0,"syntology":null},{"url":null,"slug":"czechlynx-a-dataset-for-individual","title":"CzechLynx: A Dataset for Individual Identification and Pose Estimation of the Eurasian Lynx","date":"2025-06-05","arxiv_id":"2506.04931","repositories_listed":0,"syntology":null},{"url":null,"slug":"gen-n-val-agentic-image-data-generation-and","title":"Gen-n-Val: Agentic Image Data Generation and Validation","date":"2025-06-05","arxiv_id":"2506.04676","repositories_listed":0,"syntology":null},{"url":null,"slug":"sppsformer-high-quality-superpoint-based","title":"SPPSFormer: High-quality Superpoint-based Transformer for Roof Plane Instance Segmentation from Point Clouds","date":"2025-05-30","arxiv_id":"2505.24475","repositories_listed":0,"syntology":null},{"url":"/paper/cast-contrastive-adaptation-and-distillation","slug":"cast-contrastive-adaptation-and-distillation","title":"CAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation","date":"2025-05-28","arxiv_id":"2505.21904","repositories_listed":0,"syntology":null},{"url":null,"slug":"conflunet-multiple-sclerosis-lesion-instance","title":"ConfLUNet: Multiple sclerosis lesion instance segmentation in presence of confluent lesions","date":"2025-05-28","arxiv_id":"2505.22537","repositories_listed":0,"syntology":null},{"url":null,"slug":"detailed-evaluation-of-modern-machine","title":"Detailed Evaluation of Modern Machine Learning Approaches for Optic Plastics Sorting","date":"2025-05-22","arxiv_id":"2505.16513","repositories_listed":0,"syntology":null},{"url":null,"slug":"gen2seg-generative-models-enable","title":"gen2seg: Generative Models Enable Generalizable Instance Segmentation","date":"2025-05-21","arxiv_id":"2505.15263","repositories_listed":0,"syntology":null},{"url":null,"slug":"instance-segmentation-for-point-sets","title":"Instance Segmentation for Point Sets","date":"2025-05-20","arxiv_id":"2505.14583","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-transformers-through-conditioned","title":"Enhancing Transformers Through Conditioned Embedded Tokens","date":"2025-05-19","arxiv_id":"2505.12789","repositories_listed":0,"syntology":null},{"url":null,"slug":"flowcut-unsupervised-video-instance","title":"FlowCut: Unsupervised Video Instance Segmentation via Temporal Mask Matching","date":"2025-05-19","arxiv_id":"2505.13174","repositories_listed":0,"syntology":null},{"url":null,"slug":"industry-focused-synthetic-segmentation-pre","title":"Industrial Synthetic Segment Pre-training","date":"2025-05-19","arxiv_id":"2505.13099","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11075","title":"Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation","date":"2025-05-16","arxiv_id":"2505.11075","repositories_listed":0,"syntology":null},{"url":null,"slug":"2505-11439","title":"SurgPose: Generalisable Surgical Instrument Pose Estimation using Zero-Shot Learning and Stereo Vision","date":"2025-05-16","arxiv_id":"2505.11439","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-raspgrade-dataset-towards-automatic","title":"The RaspGrade Dataset: Towards Automatic Raspberry Ripeness Grading with Deep Learning","date":"2025-05-13","arxiv_id":"2505.08537","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-foundation-model-embedding-based","title":"Vision Foundation Model Embedding-Based Semantic Anomaly Detection","date":"2025-05-12","arxiv_id":"2505.07998","repositories_listed":0,"syntology":null},{"url":null,"slug":"mix-qsam-mixed-precision-quantization-of-the","title":"Mix-QSAM: Mixed-Precision Quantization of the Segment Anything Model","date":"2025-05-08","arxiv_id":"2505.04861","repositories_listed":0,"syntology":null},{"url":null,"slug":"repsnet-a-nucleus-instance-segmentation-model","title":"RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization","date":"2025-05-08","arxiv_id":"2505.05073","repositories_listed":0,"syntology":null},{"url":null,"slug":"hyb-kan-vit-hybrid-kolmogorov-arnold-networks","title":"Hyb-KAN ViT: Hybrid Kolmogorov-Arnold Networks Augmented Vision Transformer","date":"2025-05-07","arxiv_id":"2505.04740","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-for-robotic-leaf","title":"Self-Supervised Learning for Robotic Leaf Manipulation: A Hybrid Geometric-Neural Approach","date":"2025-05-06","arxiv_id":"2505.03702","repositories_listed":0,"syntology":null},{"url":null,"slug":"segment-any-rgb-thermal-model-with-language","title":"Segment Any RGB-Thermal Model with Language-aided Distillation","date":"2025-05-04","arxiv_id":"2505.01950","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-novel-waveinst-based-network-for-tree-trunk","title":"A Novel WaveInst-based Network for Tree Trunk Structure Extraction and Pattern Analysis in Forest Inventory","date":"2025-05-03","arxiv_id":"2505.01656","repositories_listed":0,"syntology":null},{"url":null,"slug":"global-collinearity-aware-polygonizer-for","title":"Global Collinearity-aware Polygonizer for Polygonal Building Mapping in Remote Sensing","date":"2025-05-02","arxiv_id":"2505.01385","repositories_listed":0,"syntology":null},{"url":null,"slug":"mosam-motion-guided-segment-anything-model","title":"MoSAM: Motion-Guided Segment Anything Model with Spatial-Temporal Memory Selection","date":"2025-04-30","arxiv_id":"2505.00739","repositories_listed":0,"syntology":null},{"url":null,"slug":"nvsmask3d-hard-visual-prompting-with-camera","title":"NVSMask3D: Hard Visual Prompting with Camera Pose Interpolation for 3D Open Vocabulary Instance Segmentation","date":"2025-04-20","arxiv_id":"2504.14638","repositories_listed":0,"syntology":null},{"url":null,"slug":"occlusion-ordered-semantic-instance","title":"Occlusion-Ordered Semantic Instance Segmentation","date":"2025-04-18","arxiv_id":"2504.14054","repositories_listed":0,"syntology":null},{"url":null,"slug":"cags-open-vocabulary-3d-scene-understanding","title":"CAGS: Open-Vocabulary 3D Scene Understanding with Context-Aware Gaussian Splatting","date":"2025-04-16","arxiv_id":"2504.11893","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-shot-star-convex-polygon-based","title":"Single-shot Star-convex Polygon-based Instance Segmentation for Spatially-correlated Biomedical Objects","date":"2025-04-16","arxiv_id":"2504.12078","repositories_listed":0,"syntology":null},{"url":null,"slug":"cap-net-a-unified-network-for-6d-pose-and","title":"CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image","date":"2025-04-15","arxiv_id":"2504.11230","repositories_listed":0,"syntology":null}],"record_sha256":"3bf5707841c79ffa2f8558f9221d42612110829f42e4849f1add3a8f480f6bdc","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}