{"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/autonomous-driving/papers/15","list_of":"/task/autonomous-driving","task":"Autonomous Driving","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":15,"pages_in_order":61,"rows_per_page":100,"rows":[1401,1500],"of":6092,"counts":{"archive_papers_tagged":6092,"with_a_code_link":2091,"where_syntology_ran_a_sample":470,"not_listed_spam_title":0,"listed":6092,"listed_where_code_ran":470,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":415,"every_run_a_failure_of_syntologys_instrument":55,"listed_with_a_run_with_no_instrument_failure":415,"listed_every_run_a_failure_of_syntologys_instrument":55,"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/autonomous-driving","prev":"/task/autonomous-driving/papers/14","next":"/task/autonomous-driving/papers/16","papers":[{"url":"/paper/birds-of-a-feather-trust-together-knowing","slug":"birds-of-a-feather-trust-together-knowing","title":"Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation","date":"2022-11-29","arxiv_id":"2211.16466","repositories_listed":1,"syntology":null},{"url":"/paper/self-supervised-surround-view-depth","slug":"self-supervised-surround-view-depth","title":"Self-Supervised Surround-View Depth Estimation with Volumetric Feature Fusion","date":"2022-11-29","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/superfusion-multilevel-lidar-camera-fusion","slug":"superfusion-multilevel-lidar-camera-fusion","title":"SuperFusion: Multilevel LiDAR-Camera Fusion for Long-Range HD Map Generation","date":"2022-11-28","arxiv_id":"2211.15656","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 1 unverified","sample_list":"/paper/superfusion-multilevel-lidar-camera-fusion#ran","syntology_url":"https://syntology.ai/paper/2211.15656","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.15656"}},"official":{"repos":["haomo-ai/superfusion"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/octet-object-aware-counterfactual","slug":"octet-object-aware-counterfactual","title":"OCTET: Object-aware Counterfactual Explanations","date":"2022-11-22","arxiv_id":"2211.12380","repositories_listed":1,"syntology":null},{"url":"/paper/doubly-contrastive-end-to-end-semantic","slug":"doubly-contrastive-end-to-end-semantic","title":"Doubly Contrastive End-to-End Semantic Segmentation for Autonomous Driving under Adverse Weather","date":"2022-11-21","arxiv_id":"2211.11131","repositories_listed":1,"syntology":null},{"url":"/paper/robustloc-robust-camera-pose-regression-in","slug":"robustloc-robust-camera-pose-regression-in","title":"RobustLoc: Robust Camera Pose Regression in Challenging Driving Environments","date":"2022-11-21","arxiv_id":"2211.11238","repositories_listed":1,"syntology":null},{"url":"/paper/dynamic-conditional-imitation-learning-for-1","slug":"dynamic-conditional-imitation-learning-for-1","title":"Dynamic Conditional Imitation Learning for Autonomous Driving","date":"2022-11-17","arxiv_id":"2211.11579","repositories_listed":1,"syntology":null},{"url":"/paper/interpretable-self-aware-neural-networks-for","slug":"interpretable-self-aware-neural-networks-for","title":"Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction","date":"2022-11-16","arxiv_id":"2211.08701","repositories_listed":1,"syntology":null},{"url":"/paper/robust-deep-learning-for-autonomous-driving","slug":"robust-deep-learning-for-autonomous-driving","title":"Robust Deep Learning for Autonomous Driving","date":"2022-11-14","arxiv_id":"2211.07772","repositories_listed":1,"syntology":null},{"url":"/paper/sotif-entropy-online-sotif-risk","slug":"sotif-entropy-online-sotif-risk","title":"SOTIF Entropy: Online SOTIF Risk Quantification and Mitigation for Autonomous Driving","date":"2022-11-08","arxiv_id":"2211.04009","repositories_listed":1,"syntology":null},{"url":"/paper/pesotif-a-challenging-visual-dataset-for","slug":"pesotif-a-challenging-visual-dataset-for","title":"PeSOTIF: a Challenging Visual Dataset for Perception SOTIF Problems in Long-tail Traffic Scenarios","date":"2022-11-07","arxiv_id":"2211.03402","repositories_listed":1,"syntology":null},{"url":"/paper/an-empirical-bayes-analysis-of-vehicle","slug":"an-empirical-bayes-analysis-of-vehicle","title":"An Empirical Bayes Analysis of Object Trajectory Representation Models","date":"2022-11-03","arxiv_id":"2211.01696","repositories_listed":1,"syntology":null},{"url":"/paper/domain-adaptive-object-detection-for","slug":"domain-adaptive-object-detection-for","title":"Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather","date":"2022-10-27","arxiv_id":"2210.15176","repositories_listed":1,"syntology":null},{"url":"/paper/joint-multi-person-body-detection-and","slug":"joint-multi-person-body-detection-and","title":"Joint Multi-Person Body Detection and Orientation Estimation via One Unified Embedding","date":"2022-10-27","arxiv_id":"2210.15586","repositories_listed":1,"syntology":null},{"url":"/paper/sim-to-real-via-sim-to-seg-end-to-end-off","slug":"sim-to-real-via-sim-to-seg-end-to-end-off","title":"Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real Data","date":"2022-10-25","arxiv_id":"2210.14721","repositories_listed":1,"syntology":null},{"url":"/paper/iterative-patch-selection-for-high-resolution","slug":"iterative-patch-selection-for-high-resolution","title":"Iterative Patch Selection for High-Resolution Image Recognition","date":"2022-10-24","arxiv_id":"2210.13007","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":5,"n_instrument":2,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/iterative-patch-selection-for-high-resolution#ran","syntology_url":"https://syntology.ai/paper/2210.13007","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.13007"}},"official":{"repos":["benbergner/ips"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/dorothie-spoken-dialogue-for-handling","slug":"dorothie-spoken-dialogue-for-handling","title":"DOROTHIE: Spoken Dialogue for Handling Unexpected Situations in Interactive Autonomous Driving Agents","date":"2022-10-22","arxiv_id":"2210.12511","repositories_listed":1,"syntology":null},{"url":"/paper/graphcspn-geometry-aware-depth-completion-via","slug":"graphcspn-geometry-aware-depth-completion-via","title":"GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs","date":"2022-10-19","arxiv_id":"2210.10758","repositories_listed":1,"syntology":null},{"url":"/paper/learning-preferences-for-interactive-autonomy","slug":"learning-preferences-for-interactive-autonomy","title":"Learning Preferences for Interactive Autonomy","date":"2022-10-19","arxiv_id":"2210.10899","repositories_listed":1,"syntology":null},{"url":"/paper/online-lidar-camera-extrinsic-parameters-self","slug":"online-lidar-camera-extrinsic-parameters-self","title":"Online LiDAR-Camera Extrinsic Parameters Self-checking","date":"2022-10-19","arxiv_id":"2210.10537","repositories_listed":1,"syntology":null},{"url":"/paper/intra-source-style-augmentation-for-improved","slug":"intra-source-style-augmentation-for-improved","title":"Intra-Source Style Augmentation for Improved Domain Generalization","date":"2022-10-18","arxiv_id":"2210.10175","repositories_listed":1,"syntology":null},{"url":"/paper/intelligent-resource-allocation-in-joint","slug":"intelligent-resource-allocation-in-joint","title":"Intelligent Resource Allocation in Joint Radar-Communication With Graph Neural Networks","date":"2022-10-17","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/row-wise-lidar-lane-detection-network-with","slug":"row-wise-lidar-lane-detection-network-with","title":"Row-wise LiDAR Lane Detection Network with Lane Correlation Refinement","date":"2022-10-17","arxiv_id":"2210.08745","repositories_listed":1,"syntology":null},{"url":"/paper/model-based-imitation-learning-for-urban","slug":"model-based-imitation-learning-for-urban","title":"Model-Based Imitation Learning for Urban Driving","date":"2022-10-14","arxiv_id":"2210.07729","repositories_listed":1,"syntology":{"n":23,"n_ran":21,"n_constructed":13,"n_ran_checked":13,"n_instrument":8,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":0,"phrase":"21 ran (of which 13 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 8 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/model-based-imitation-learning-for-urban#ran","syntology_url":"https://syntology.ai/paper/2210.07729","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.07729"}},"official":{"repos":["wayveai/mile"],"state":"official (archive's flag): 21 ran","n_ran":21,"n_constructed":13,"n_ran_no_instrument_failure":13,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pishgu-universal-path-prediction-architecture","slug":"pishgu-universal-path-prediction-architecture","title":"Pishgu: Universal Path Prediction Network Architecture for Real-time Cyber-physical Edge Systems","date":"2022-10-14","arxiv_id":"2210.08057","repositories_listed":1,"syntology":null},{"url":"/paper/dimensionality-of-datasets-in-object","slug":"dimensionality-of-datasets-in-object","title":"Dimensionality of datasets in object detection networks","date":"2022-10-13","arxiv_id":"2210.07049","repositories_listed":1,"syntology":null},{"url":"/paper/lacv-net-semantic-segmentation-of-large-scale","slug":"lacv-net-semantic-segmentation-of-large-scale","title":"LACV-Net: Semantic Segmentation of Large-Scale Point Cloud Scene via Local Adaptive and Comprehensive VLAD","date":"2022-10-12","arxiv_id":"2210.05870","repositories_listed":1,"syntology":null},{"url":"/paper/trianglenet-edge-prior-augmented-network-for","slug":"trianglenet-edge-prior-augmented-network-for","title":"TriangleNet: Edge Prior Augmented Network for Semantic Segmentation through Cross-Task Consistency","date":"2022-10-11","arxiv_id":"2210.05152","repositories_listed":1,"syntology":null},{"url":"/paper/uncertainty-aware-lidar-panoptic-segmentation","slug":"uncertainty-aware-lidar-panoptic-segmentation","title":"Uncertainty-aware LiDAR Panoptic Segmentation","date":"2022-10-10","arxiv_id":"2210.04472","repositories_listed":1,"syntology":null},{"url":"/paper/viewfool-evaluating-the-robustness-of-visual","slug":"viewfool-evaluating-the-robustness-of-visual","title":"ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints","date":"2022-10-08","arxiv_id":"2210.03895","repositories_listed":1,"syntology":null},{"url":"/paper/clad-a-realistic-continual-learning-benchmark","slug":"clad-a-realistic-continual-learning-benchmark","title":"CLAD: A realistic Continual Learning benchmark for Autonomous Driving","date":"2022-10-07","arxiv_id":"2210.03482","repositories_listed":1,"syntology":null},{"url":"/paper/gma3d-local-global-attention-learning-to","slug":"gma3d-local-global-attention-learning-to","title":"GMA3D: Local-Global Attention Learning to Estimate Occluded Motions of Scene Flow","date":"2022-10-07","arxiv_id":"2210.03296","repositories_listed":1,"syntology":null},{"url":"/paper/mind-your-data-hiding-backdoors-in-offline","slug":"mind-your-data-hiding-backdoors-in-offline","title":"BAFFLE: Hiding Backdoors in Offline Reinforcement Learning Datasets","date":"2022-10-07","arxiv_id":"2210.04688","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-confidence-for-lidar-depth-maps","slug":"unsupervised-confidence-for-lidar-depth-maps","title":"Unsupervised confidence for LiDAR depth maps and applications","date":"2022-10-06","arxiv_id":"2210.03118","repositories_listed":1,"syntology":null},{"url":"/paper/bayesian-quadrature-for-probability-threshold","slug":"bayesian-quadrature-for-probability-threshold","title":"An Active Learning Reliability Method for Systems with Partially Defined Performance Functions","date":"2022-10-05","arxiv_id":"2210.02168","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/bayesian-quadrature-for-probability-threshold#ran","syntology_url":"https://syntology.ai/paper/2210.02168","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02168"}},"official":{"repos":["fiveai/hgp_experiments"],"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/cw-erm-improving-autonomous-driving-planning","slug":"cw-erm-improving-autonomous-driving-planning","title":"CW-ERM: Improving Autonomous Driving Planning with Closed-loop Weighted Empirical Risk Minimization","date":"2022-10-05","arxiv_id":"2210.02174","repositories_listed":1,"syntology":null},{"url":"/paper/image-masking-for-robust-self-supervised","slug":"image-masking-for-robust-self-supervised","title":"Image Masking for Robust Self-Supervised Monocular Depth Estimation","date":"2022-10-05","arxiv_id":"2210.02357","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":1,"n_no_contract":0,"n_pointer_only":1,"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) · 0 unverified","sample_list":"/paper/image-masking-for-robust-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2210.02357","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02357"}},"official":{"repos":["neurai-lab/mimdepth"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["unlocated"]}}},{"url":"/paper/learning-across-domains-and-devices-style","slug":"learning-across-domains-and-devices-style","title":"Learning Across Domains and Devices: Style-Driven Source-Free Domain Adaptation in Clustered Federated Learning","date":"2022-10-05","arxiv_id":"2210.02326","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":1,"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/learning-across-domains-and-devices-style#ran","syntology_url":"https://syntology.ai/paper/2210.02326","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02326"}},"official":{"repos":["erosinho13/ladd"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dfferentiable-raycasting-for-self-supervised","slug":"dfferentiable-raycasting-for-self-supervised","title":"Differentiable Raycasting for Self-supervised Occupancy Forecasting","date":"2022-10-04","arxiv_id":"2210.01917","repositories_listed":1,"syntology":{"n":20,"n_ran":19,"n_constructed":0,"n_ran_checked":19,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":19,"n_pointer_only":2,"phrase":"19 ran (of which 0 constructed an object rather than computing a result; 19 with no instrument failure: 0 honoured, 0 violated, 19 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/dfferentiable-raycasting-for-self-supervised#ran","syntology_url":"https://syntology.ai/paper/2210.01917","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.01917"}},"official":{"repos":["tarashakhurana/emergent-occ-forecasting"],"state":"official (archive's flag): 19 ran","n_ran":19,"n_constructed":0,"n_ran_no_instrument_failure":19,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/planedepth-plane-based-self-supervised","slug":"planedepth-plane-based-self-supervised","title":"PlaneDepth: Self-supervised Depth Estimation via Orthogonal Planes","date":"2022-10-04","arxiv_id":"2210.01612","repositories_listed":1,"syntology":null},{"url":"/paper/road-r-the-autonomous-driving-dataset-with","slug":"road-r-the-autonomous-driving-dataset-with","title":"ROAD-R: The Autonomous Driving Dataset with Logical Requirements","date":"2022-10-04","arxiv_id":"2210.01597","repositories_listed":1,"syntology":null},{"url":"/paper/efficient-bayes-inference-in-neural-networks","slug":"efficient-bayes-inference-in-neural-networks","title":"Efficient Bayes Inference in Neural Networks through Adaptive Importance Sampling","date":"2022-10-03","arxiv_id":"2210.00993","repositories_listed":1,"syntology":null},{"url":"/paper/lopr-latent-occupancy-prediction-using","slug":"lopr-latent-occupancy-prediction-using","title":"LOPR: Latent Occupancy PRediction using Generative Models","date":"2022-10-03","arxiv_id":"2210.01249","repositories_listed":1,"syntology":null},{"url":"/paper/pcb-randnet-rethinking-random-sampling-for","slug":"pcb-randnet-rethinking-random-sampling-for","title":"PCB-RandNet: Rethinking Random Sampling for LIDAR Semantic Segmentation in Autonomous Driving Scene","date":"2022-09-28","arxiv_id":"2209.13797","repositories_listed":1,"syntology":null},{"url":"/paper/crossdtr-cross-view-and-depth-guided","slug":"crossdtr-cross-view-and-depth-guided","title":"CrossDTR: Cross-view and Depth-guided Transformers for 3D Object Detection","date":"2022-09-27","arxiv_id":"2209.13507","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-attention-gan-for-vehicle-motion","slug":"exploring-attention-gan-for-vehicle-motion","title":"Exploring Attention GAN for Vehicle Motion Prediction","date":"2022-09-26","arxiv_id":"2209.12674","repositories_listed":1,"syntology":null},{"url":"/paper/ground-then-navigate-language-guided","slug":"ground-then-navigate-language-guided","title":"Ground then Navigate: Language-guided Navigation in Dynamic Scenes","date":"2022-09-24","arxiv_id":"2209.11972","repositories_listed":1,"syntology":null},{"url":"/paper/leader-learning-attention-over-driving","slug":"leader-learning-attention-over-driving","title":"LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty","date":"2022-09-23","arxiv_id":"2209.11422","repositories_listed":1,"syntology":null},{"url":"/paper/query-based-hard-image-retrieval-for-object","slug":"query-based-hard-image-retrieval-for-object","title":"Query-based Hard-Image Retrieval for Object Detection at Test Time","date":"2022-09-23","arxiv_id":"2209.11559","repositories_listed":1,"syntology":null},{"url":"/paper/ganet-goal-area-network-for-motion","slug":"ganet-goal-area-network-for-motion","title":"GANet: Goal Area Network for Motion Forecasting","date":"2022-09-20","arxiv_id":"2209.09723","repositories_listed":1,"syntology":null},{"url":"/paper/a-dual-cycled-cross-view-transformer-network","slug":"a-dual-cycled-cross-view-transformer-network","title":"A Dual-Cycled Cross-View Transformer Network for Unified Road Layout Estimation and 3D Object Detection in the Bird's-Eye-View","date":"2022-09-19","arxiv_id":"2209.08844","repositories_listed":1,"syntology":null},{"url":"/paper/a-real-time-dynamic-obstacle-tracking-and","slug":"a-real-time-dynamic-obstacle-tracking-and","title":"A real-time dynamic obstacle tracking and mapping system for UAV navigation and collision avoidance with an RGB-D camera","date":"2022-09-17","arxiv_id":"2209.08258","repositories_listed":1,"syntology":null},{"url":"/paper/gatraj-a-graph-and-attention-based-multi","slug":"gatraj-a-graph-and-attention-based-multi","title":"GATraj: A Graph- and Attention-based Multi-Agent Trajectory Prediction Model","date":"2022-09-16","arxiv_id":"2209.07857","repositories_listed":1,"syntology":null},{"url":"/paper/lossless-simd-compression-of-lidar-range-and","slug":"lossless-simd-compression-of-lidar-range-and","title":"Lossless SIMD Compression of LiDAR Range and Attribute Scan Sequences","date":"2022-09-16","arxiv_id":"2209.08196","repositories_listed":1,"syntology":null},{"url":"/paper/4denoisenet-adverse-weather-denoising-from","slug":"4denoisenet-adverse-weather-denoising-from","title":"4DenoiseNet: Adverse Weather Denoising from Adjacent Point Clouds","date":"2022-09-15","arxiv_id":"2209.07121","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":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) · 0 unverified","sample_list":"/paper/4denoisenet-adverse-weather-denoising-from#ran","syntology_url":"https://syntology.ai/paper/2209.07121","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.07121"}},"official":{"repos":["alvariseppanen/4denoisenet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/viewer-centred-surface-completion-for","slug":"viewer-centred-surface-completion-for","title":"Viewer-Centred Surface Completion for Unsupervised Domain Adaptation in 3D Object Detection","date":"2022-09-14","arxiv_id":"2209.06407","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/viewer-centred-surface-completion-for#ran","syntology_url":"https://syntology.ai/paper/2209.06407","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.06407"}},"official":{"repos":["darrenjkt/SEE-VCN"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/svnet-where-so-3-equivariance-meets","slug":"svnet-where-so-3-equivariance-meets","title":"SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud Representation","date":"2022-09-13","arxiv_id":"2209.05924","repositories_listed":1,"syntology":null},{"url":"/paper/msmdfusion-fusing-lidar-and-camera-at","slug":"msmdfusion-fusing-lidar-and-camera-at","title":"MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection","date":"2022-09-07","arxiv_id":"2209.03102","repositories_listed":1,"syntology":null},{"url":"/paper/3dlanenas-neural-architecture-search-for","slug":"3dlanenas-neural-architecture-search-for","title":"3DLaneNAS: Neural Architecture Search for Accurate and Light-Weight 3D Lane Detection","date":"2022-09-06","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/real-time-3d-single-object-tracking-with","slug":"real-time-3d-single-object-tracking-with","title":"Real-time 3D Single Object Tracking with Transformer","date":"2022-09-02","arxiv_id":"2209.00860","repositories_listed":1,"syntology":null},{"url":"/paper/boosting-night-time-scene-parsing-with","slug":"boosting-night-time-scene-parsing-with","title":"Boosting Night-time Scene Parsing with Learnable Frequency","date":"2022-08-30","arxiv_id":"2208.14241","repositories_listed":1,"syntology":null},{"url":"/paper/maptr-structured-modeling-and-learning-for","slug":"maptr-structured-modeling-and-learning-for","title":"MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction","date":"2022-08-30","arxiv_id":"2208.14437","repositories_listed":1,"syntology":null},{"url":"/paper/verifiable-obstacle-detection","slug":"verifiable-obstacle-detection","title":"Verifiable Obstacle Detection","date":"2022-08-30","arxiv_id":"2208.14403","repositories_listed":1,"syntology":null},{"url":"/paper/progressive-self-distillation-for-ground-to","slug":"progressive-self-distillation-for-ground-to","title":"Progressive Self-Distillation for Ground-to-Aerial Perception Knowledge Transfer","date":"2022-08-29","arxiv_id":"2208.13404","repositories_listed":1,"syntology":null},{"url":"/paper/unsupervised-spike-depth-estimation-via-cross","slug":"unsupervised-spike-depth-estimation-via-cross","title":"Unsupervised Spike Depth Estimation via Cross-modality Cross-domain Knowledge Transfer","date":"2022-08-26","arxiv_id":"2208.12527","repositories_listed":1,"syntology":null},{"url":"/paper/augmenting-reinforcement-learning-with-1","slug":"augmenting-reinforcement-learning-with-1","title":"Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous Driving","date":"2022-08-24","arxiv_id":"2208.12263","repositories_listed":1,"syntology":null},{"url":"/paper/lane-change-classification-and-prediction","slug":"lane-change-classification-and-prediction","title":"Lane Change Classification and Prediction with Action Recognition Networks","date":"2022-08-24","arxiv_id":"2208.11650","repositories_listed":1,"syntology":null},{"url":"/paper/a-simple-baseline-for-multi-camera-3d-object","slug":"a-simple-baseline-for-multi-camera-3d-object","title":"A Simple Baseline for Multi-Camera 3D Object Detection","date":"2022-08-22","arxiv_id":"2208.10035","repositories_listed":1,"syntology":null},{"url":"/paper/monopcns-monocular-3d-object-detection-via","slug":"monopcns-monocular-3d-object-detection-via","title":"MonoSIM: Simulating Learning Behaviors of Heterogeneous Point Cloud Object Detectors for Monocular 3D Object Detection","date":"2022-08-19","arxiv_id":"2208.09446","repositories_listed":1,"syntology":null},{"url":"/paper/context-aware-streaming-perception-in-dynamic","slug":"context-aware-streaming-perception-in-dynamic","title":"Context-Aware Streaming Perception in Dynamic Environments","date":"2022-08-16","arxiv_id":"2208.07479","repositories_listed":1,"syntology":null},{"url":"/paper/exploring-point-bev-fusion-for-3d-point-cloud","slug":"exploring-point-bev-fusion-for-3d-point-cloud","title":"Exploring Point-BEV Fusion for 3D Point Cloud Object Tracking with Transformer","date":"2022-08-10","arxiv_id":"2208.05216","repositories_listed":1,"syntology":null},{"url":"/paper/robust-continual-test-time-adaptation","slug":"robust-continual-test-time-adaptation","title":"NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation","date":"2022-08-10","arxiv_id":"2208.05117","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"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) · 1 unverified","sample_list":"/paper/robust-continual-test-time-adaptation#ran","syntology_url":"https://syntology.ai/paper/2208.05117","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.05117"}},"official":{"repos":["taesikgong/note"],"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/aerial-monocular-3d-object-detection","slug":"aerial-monocular-3d-object-detection","title":"Aerial Monocular 3D Object Detection","date":"2022-08-08","arxiv_id":"2208.03974","repositories_listed":1,"syntology":null},{"url":"/paper/coordinated-pso-pid-based-longitudinal","slug":"coordinated-pso-pid-based-longitudinal","title":"Coordinated PSO-PID based longitudinal control with LPV-MPC based lateral control for autonomous vehicles","date":"2022-08-05","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/ipdae-improved-patch-based-deep-autoencoder","slug":"ipdae-improved-patch-based-deep-autoencoder","title":"IPDAE: Improved Patch-Based Deep Autoencoder for Lossy Point Cloud Geometry Compression","date":"2022-08-04","arxiv_id":"2208.02519","repositories_listed":1,"syntology":null},{"url":"/paper/vip3d-end-to-end-visual-trajectory-prediction","slug":"vip3d-end-to-end-visual-trajectory-prediction","title":"ViP3D: End-to-end Visual Trajectory Prediction via 3D Agent Queries","date":"2022-08-02","arxiv_id":"2208.01582","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/vip3d-end-to-end-visual-trajectory-prediction#ran","syntology_url":"https://syntology.ai/paper/2208.01582","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.01582"}},"official":{"repos":["Tsinghua-MARS-Lab/ViP3D"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/strajnet-occupancy-flow-prediction-via-multi","slug":"strajnet-occupancy-flow-prediction-via-multi","title":"STrajNet: Multi-modal Hierarchical Transformer for Occupancy Flow Field Prediction in Autonomous Driving","date":"2022-07-31","arxiv_id":"2208.00394","repositories_listed":1,"syntology":null},{"url":"/paper/safety-enhanced-autonomous-driving-using-1","slug":"safety-enhanced-autonomous-driving-using-1","title":"Safety-Enhanced Autonomous Driving Using Interpretable Sensor Fusion Transformer","date":"2022-07-28","arxiv_id":"2207.14024","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/safety-enhanced-autonomous-driving-using-1#ran","syntology_url":"https://syntology.ai/paper/2207.14024","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.14024"}},"official":{"repos":["opendilab/InterFuser"],"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/gps-glass-learning-nighttime-semantic","slug":"gps-glass-learning-nighttime-semantic","title":"GPS-GLASS: Learning Nighttime Semantic Segmentation Using Daytime Video and GPS data","date":"2022-07-27","arxiv_id":"2207.13297","repositories_listed":1,"syntology":null},{"url":"/paper/pointfix-learning-to-fix-domain-bias-for","slug":"pointfix-learning-to-fix-domain-bias-for","title":"PointFix: Learning to Fix Domain Bias for Robust Online Stereo Adaptation","date":"2022-07-27","arxiv_id":"2207.13340","repositories_listed":1,"syntology":null},{"url":"/paper/semi-supervised-3d-object-detection-with","slug":"semi-supervised-3d-object-detection-with","title":"Semi-supervised 3D Object Detection with Proficient Teachers","date":"2022-07-26","arxiv_id":"2207.12655","repositories_listed":1,"syntology":null},{"url":"/paper/codit-conformal-out-of-distribution-detection","slug":"codit-conformal-out-of-distribution-detection","title":"CODiT: Conformal Out-of-Distribution Detection in Time-Series Data","date":"2022-07-24","arxiv_id":"2207.11769","repositories_listed":1,"syntology":null},{"url":"/paper/driver-dojo-a-benchmark-for-generalizable","slug":"driver-dojo-a-benchmark-for-generalizable","title":"Driver Dojo: A Benchmark for Generalizable Reinforcement Learning for Autonomous Driving","date":"2022-07-23","arxiv_id":"2207.11432","repositories_listed":1,"syntology":null},{"url":"/paper/training-certifiably-robust-neural-networks-1","slug":"training-certifiably-robust-neural-networks-1","title":"Provable Defense Against Geometric Transformations","date":"2022-07-22","arxiv_id":"2207.11177","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/training-certifiably-robust-neural-networks-1#ran","syntology_url":"https://syntology.ai/paper/2207.11177","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.11177"}},"official":{"repos":["uiuc-arc/cgt"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/autoalignv2-deformable-feature-aggregation","slug":"autoalignv2-deformable-feature-aggregation","title":"AutoAlignV2: Deformable Feature Aggregation for Dynamic Multi-Modal 3D Object Detection","date":"2022-07-21","arxiv_id":"2207.10316","repositories_listed":1,"syntology":null},{"url":"/paper/latent-discriminant-deterministic-uncertainty","slug":"latent-discriminant-deterministic-uncertainty","title":"Latent Discriminant deterministic Uncertainty","date":"2022-07-20","arxiv_id":"2207.10130","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":1,"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/latent-discriminant-deterministic-uncertainty#ran","syntology_url":"https://syntology.ai/paper/2207.10130","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10130"}},"official":{"repos":["ensta-u2is/ldu"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/visual-knowledge-tracing","slug":"visual-knowledge-tracing","title":"Visual Knowledge Tracing","date":"2022-07-20","arxiv_id":"2207.10157","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/visual-knowledge-tracing#ran","syntology_url":"https://syntology.ai/paper/2207.10157","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.10157"}},"official":{"repos":["nkondapa/visualknowledgetracing"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/anti-carla-an-adversarial-testing-framework","slug":"anti-carla-an-adversarial-testing-framework","title":"ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA","date":"2022-07-19","arxiv_id":"2208.06309","repositories_listed":1,"syntology":null},{"url":"/paper/det6d-a-ground-aware-full-pose-3d-object","slug":"det6d-a-ground-aware-full-pose-3d-object","title":"Det6D: A Ground-Aware Full-Pose 3D Object Detector for Improving Terrain Robustness","date":"2022-07-19","arxiv_id":"2207.09412","repositories_listed":1,"syntology":null},{"url":"/paper/latency-aware-collaborative-perception","slug":"latency-aware-collaborative-perception","title":"Latency-Aware Collaborative Perception","date":"2022-07-18","arxiv_id":"2207.08560","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"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) · 0 unverified","sample_list":"/paper/latency-aware-collaborative-perception#ran","syntology_url":"https://syntology.ai/paper/2207.08560","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.08560"}},"official":{"repos":["mediabrain-sjtu/syncnet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/semantic-novelty-detection-via-relational","slug":"semantic-novelty-detection-via-relational","title":"Semantic Novelty Detection via Relational Reasoning","date":"2022-07-18","arxiv_id":"2207.08699","repositories_listed":1,"syntology":null},{"url":"/paper/jperceiver-joint-perception-network-for-depth","slug":"jperceiver-joint-perception-network-for-depth","title":"JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving Scenes","date":"2022-07-16","arxiv_id":"2207.07895","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/jperceiver-joint-perception-network-for-depth#ran","syntology_url":"https://syntology.ai/paper/2207.07895","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.07895"}},"official":{"repos":["sunnyhelen/jperceiver"],"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/nefsac-neurally-filtered-minimal-samples","slug":"nefsac-neurally-filtered-minimal-samples","title":"NeFSAC: Neurally Filtered Minimal Samples","date":"2022-07-16","arxiv_id":"2207.07872","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 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) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/nefsac-neurally-filtered-minimal-samples#ran","syntology_url":"https://syntology.ai/paper/2207.07872","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.07872"}},"official":{"repos":["cavalli1234/nefsac"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dolphins-dataset-for-collaborative-perception","slug":"dolphins-dataset-for-collaborative-perception","title":"DOLPHINS: Dataset for Collaborative Perception enabled Harmonious and Interconnected Self-driving","date":"2022-07-15","arxiv_id":"2207.07609","repositories_listed":1,"syntology":null},{"url":"/paper/st-p3-end-to-end-vision-based-autonomous","slug":"st-p3-end-to-end-vision-based-autonomous","title":"ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning","date":"2022-07-15","arxiv_id":"2207.07601","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":4,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/st-p3-end-to-end-vision-based-autonomous#ran","syntology_url":"https://syntology.ai/paper/2207.07601","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.07601"}},"official":null}},{"url":"/paper/bayescap-bayesian-identity-cap-for-calibrated","slug":"bayescap-bayesian-identity-cap-for-calibrated","title":"BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks","date":"2022-07-14","arxiv_id":"2207.06873","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 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) · 0 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/bayescap-bayesian-identity-cap-for-calibrated#ran","syntology_url":"https://syntology.ai/paper/2207.06873","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.06873"}},"official":{"repos":["explainableml/bayescap"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/teachers-in-concordance-for-pseudo-labeling","slug":"teachers-in-concordance-for-pseudo-labeling","title":"Teachers in concordance for pseudo-labeling of 3D sequential data","date":"2022-07-13","arxiv_id":"2207.06079","repositories_listed":1,"syntology":null},{"url":"/paper/2dpass-2d-priors-assisted-semantic","slug":"2dpass-2d-priors-assisted-semantic","title":"2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds","date":"2022-07-10","arxiv_id":"2207.04397","repositories_listed":1,"syntology":null},{"url":"/paper/mix-teaching-a-simple-unified-and-effective","slug":"mix-teaching-a-simple-unified-and-effective","title":"Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection","date":"2022-07-10","arxiv_id":"2207.04448","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":3,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/mix-teaching-a-simple-unified-and-effective#ran","syntology_url":"https://syntology.ai/paper/2207.04448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.04448"}},"official":{"repos":["yanglei18/mix-teaching"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/mirror-complementary-transformer-network-for","slug":"mirror-complementary-transformer-network-for","title":"Mirror Complementary Transformer Network for RGB-thermal Salient Object Detection","date":"2022-07-07","arxiv_id":"2207.03558","repositories_listed":1,"syntology":null}],"record_sha256":"de41f25ec99e302e8d6b3c0d1f07d9a74fbb3827dc7eef32947464814177e0a5","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}