{"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/object/papers/79","list_of":"/task/object","task":"Object","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":79,"pages_in_order":107,"rows_per_page":100,"rows":[7801,7900],"of":10696,"counts":{"archive_papers_tagged":10696,"with_a_code_link":3979,"where_syntology_ran_a_sample":1043,"not_listed_spam_title":0,"listed":10696,"listed_where_code_ran":1043,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":919,"every_run_a_failure_of_syntologys_instrument":124,"listed_with_a_run_with_no_instrument_failure":919,"listed_every_run_a_failure_of_syntologys_instrument":124,"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/object","prev":"/task/object/papers/78","next":"/task/object/papers/80","papers":[{"url":null,"slug":"gated3d-monocular-3d-object-detection-from","title":"Gated3D: Monocular 3D Object Detection From Temporal Illumination Cues","date":"2021-02-06","arxiv_id":"2102.03602","repositories_listed":0,"syntology":null},{"url":null,"slug":"custom-object-detection-via-multi-camera-self","title":"Custom Object Detection via Multi-Camera Self-Supervised Learning","date":"2021-02-05","arxiv_id":"2102.03442","repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-texture-aware-features-for-camouflaged","title":"Deep Texture-Aware Features for Camouflaged Object Detection","date":"2021-02-05","arxiv_id":"2102.02996","repositories_listed":0,"syntology":null},{"url":null,"slug":"multispectral-object-detection-with-deep","title":"Multispectral Object Detection with Deep Learning","date":"2021-02-05","arxiv_id":"2102.03115","repositories_listed":0,"syntology":null},{"url":null,"slug":"new-generation-deep-learning-for-video-object","title":"New Generation Deep Learning For Video Object Dection:A Survery","date":"2021-02-03","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"object-and-relation-centric-representations","title":"Object and Relation Centric Representations for Push Effect Prediction","date":"2021-02-03","arxiv_id":"2102.02100","repositories_listed":0,"syntology":null},{"url":null,"slug":"consistent-recurrent-neural-networks-for-3d","title":"Consistent Recurrent Neural Networks for 3D Neuron Segmentation","date":"2021-02-01","arxiv_id":"2102.01021","repositories_listed":0,"syntology":null},{"url":null,"slug":"forecasting-action-through-contact","title":"Forecasting Action through Contact Representations from First Person Video","date":"2021-02-01","arxiv_id":"2102.00649","repositories_listed":0,"syntology":null},{"url":null,"slug":"advancing-the-ambientgan-for-learning","title":"Advancing the AmbientGAN for learning stochastic object models","date":"2021-01-30","arxiv_id":"2102.00281","repositories_listed":0,"syntology":null},{"url":null,"slug":"latent-space-inpainting-for-packet-loss","title":"Latent-Space Inpainting for Packet Loss Concealment in Collaborative Object Detection","date":"2021-01-30","arxiv_id":"2102.00142","repositories_listed":0,"syntology":null},{"url":null,"slug":"objectaug-object-level-data-augmentation-for","title":"ObjectAug: Object-level Data Augmentation for Semantic Image Segmentation","date":"2021-01-30","arxiv_id":"2102.00221","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-landscape-of-multi-layer-linear-neural","title":"The Landscape of Multi-Layer Linear Neural Network From the Perspective of Algebraic Geometry","date":"2021-01-30","arxiv_id":"2102.04338","repositories_listed":0,"syntology":null},{"url":null,"slug":"few-shot-learning-for-road-object-detection","title":"Few-Shot Learning for Road Object Detection","date":"2021-01-29","arxiv_id":"2101.12543","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-domain-labels-for-object-detection","title":"Diminishing Domain Bias by Leveraging Domain Labels in Object Detection on UAVs","date":"2021-01-29","arxiv_id":"2101.12677","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-orientation-estimation-of-elongated","title":"The Orientation Estimation of Elongated Underground Objects via Multi-Polarization Aggregation and Selection Neural Network","date":"2021-01-29","arxiv_id":"2101.12398","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-detection-made-simpler-by-eliminating","title":"Object Detection Made Simpler by Eliminating Heuristic NMS","date":"2021-01-28","arxiv_id":"2101.11782","repositories_listed":0,"syntology":null},{"url":null,"slug":"logical-combinatorial-approaches-in-dynamic","title":"Logical-Combinatorial Approaches in Dynamic Recognition Problems","date":"2021-01-26","arxiv_id":"2101.11066","repositories_listed":0,"syntology":null},{"url":null,"slug":"named-entity-recognition-in-the-style-of","title":"Named Entity Recognition in the Style of Object Detection","date":"2021-01-26","arxiv_id":"2101.11122","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-missing-data-imputation-method-for-3d","title":"Anytime 3D Object Reconstruction using Multi-modal Variational Autoencoder","date":"2021-01-25","arxiv_id":"2101.10391","repositories_listed":0,"syntology":null},{"url":null,"slug":"anchor-distance-for-3d-multi-object-distance","title":"Anchor Distance for 3D Multi-Object Distance Estimation from 2D Single Shot","date":"2021-01-25","arxiv_id":"2101.10399","repositories_listed":0,"syntology":null},{"url":null,"slug":"ecol-r-encouraging-copying-in-novel-object","title":"ECOL-R: Encouraging Copying in Novel Object Captioning with Reinforcement Learning","date":"2021-01-25","arxiv_id":"2101.09865","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-optimisation-with-an-innovation-cnn","title":"Iterative Optimisation with an Innovation CNN for Pose Refinement","date":"2021-01-22","arxiv_id":"2101.08895","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-spike-learning-system-for-event-driven","title":"A Spike Learning System for Event-driven Object Recognition","date":"2021-01-21","arxiv_id":"2101.08850","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-two-stage-data-association-approach-for-3d","title":"A two-stage data association approach for 3D Multi-object Tracking","date":"2021-01-21","arxiv_id":"2101.08684","repositories_listed":0,"syntology":null},{"url":null,"slug":"collide-pred-prediction-of-on-road-collision","title":"COLLIDE-PRED: Prediction of On-Road Collision From Surveillance Videos","date":"2021-01-21","arxiv_id":"2101.08463","repositories_listed":0,"syntology":null},{"url":null,"slug":"occlusion-handling-in-generic-object","title":"Occlusion Handling in Generic Object Detection: A Review","date":"2021-01-21","arxiv_id":"2101.08845","repositories_listed":0,"syntology":null},{"url":null,"slug":"video-relation-detection-with-trajectory","title":"Video Relation Detection with Trajectory-aware Multi-modal Features","date":"2021-01-20","arxiv_id":"2101.08165","repositories_listed":0,"syntology":null},{"url":null,"slug":"visible-light-communication-based-monitoring","title":"Visible light communication-based monitoring for indoor environments using unsupervised learning","date":"2021-01-20","arxiv_id":"2101.10838","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-improvement-of-object-detection","title":"An Improvement of Object Detection Performance using Multi-step Machine Learnings","date":"2021-01-19","arxiv_id":"2101.07571","repositories_listed":0,"syntology":null},{"url":null,"slug":"collaboration-among-image-and-object-level","title":"Collaboration among Image and Object Level Features for Image Colourisation","date":"2021-01-19","arxiv_id":"2101.07576","repositories_listed":0,"syntology":null},{"url":null,"slug":"go-finder-a-registration-free-wearable-system","title":"GO-Finder: A Registration-Free Wearable System for Assisting Users in Finding Lost Objects via Hand-Held Object Discovery","date":"2021-01-18","arxiv_id":"2101.07314","repositories_listed":0,"syntology":null},{"url":null,"slug":"auto4d-learning-to-label-4d-objects-from","title":"Auto4D: Learning to Label 4D Objects from Sequential Point Clouds","date":"2021-01-17","arxiv_id":"2101.06586","repositories_listed":0,"syntology":null},{"url":null,"slug":"improving-apparel-detection-with-category","title":"Improving Apparel Detection with Category Grouping and Multi-grained Branches","date":"2021-01-17","arxiv_id":"2101.06770","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-remote-sensing-image-attribute","title":"Adaptive Remote Sensing Image Attribute Learning for Active Object Detection","date":"2021-01-16","arxiv_id":"2101.06438","repositories_listed":0,"syntology":null},{"url":null,"slug":"videoclick-video-object-segmentation-with-a","title":"VideoClick: Video Object Segmentation with a Single Click","date":"2021-01-16","arxiv_id":"2101.06545","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluating-disentanglement-of-structured","title":"Evaluating Disentanglement of Structured Representations","date":"2021-01-11","arxiv_id":"2101.04041","repositories_listed":0,"syntology":null},{"url":null,"slug":"trackmpnn-a-message-passing-graph-neural","title":"TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking","date":"2021-01-11","arxiv_id":"2101.04206","repositories_listed":0,"syntology":null},{"url":null,"slug":"channel-boosting-feature-ensemble-for-radar","title":"Channel Boosting Feature Ensemble for Radar-based Object Detection","date":"2021-01-10","arxiv_id":"2101.03531","repositories_listed":0,"syntology":null},{"url":null,"slug":"from-black-box-to-white-box-examining","title":"From Black-box to White-box: Examining Confidence Calibration under different Conditions","date":"2021-01-08","arxiv_id":"2101.02971","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-object-recommendation-with-hints-when","title":"Spatial Object Recommendation with Hints: When Spatial Granularity Matters","date":"2021-01-08","arxiv_id":"2101.02969","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-robust-illumination-invariant-camera-system","title":"A Robust Illumination-Invariant Camera System for Agricultural Applications","date":"2021-01-06","arxiv_id":"2101.02190","repositories_listed":0,"syntology":null},{"url":null,"slug":"af-ems-detector-improve-the-multi-scale","title":"AF-EMS Detector: Improve the Multi-Scale Detection Per- formance of the Anchor-Free Detector","date":"2021-01-06","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-object-tracking-with-a-hierarchical","title":"Multi-object Tracking with a Hierarchical Single-branch Network","date":"2021-01-06","arxiv_id":"2101.01984","repositories_listed":0,"syntology":null},{"url":null,"slug":"rethnet-object-by-object-learning-for","title":"RethNet: Object-by-Object Learning for Detecting Facial Skin Problems","date":"2021-01-06","arxiv_id":"2101.02127","repositories_listed":0,"syntology":null},{"url":null,"slug":"spatial-attention-improves-iterative-6d","title":"Spatial Attention Improves Iterative 6D Object Pose Estimation","date":"2021-01-05","arxiv_id":"2101.01659","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-framework-for-fast-scalable-bnn-inference","title":"A Framework for Fast Scalable BNN Inference using Googlenet and Transfer Learning","date":"2021-01-04","arxiv_id":"2101.00793","repositories_listed":0,"syntology":null},{"url":null,"slug":"spotpatch-parameter-efficient-transfer","title":"SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection","date":"2021-01-04","arxiv_id":"2101.01260","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-saliency-detection-via","title":"Weakly-Supervised Saliency Detection via Salient Object Subitizing","date":"2021-01-04","arxiv_id":"2101.00932","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-evolution-of-cnn-object-classifiers-on-low","title":"An Evolution of CNN Object Classifiers on Low-Resolution Images","date":"2021-01-03","arxiv_id":"2101.00686","repositories_listed":0,"syntology":null},{"url":null,"slug":"six-channel-image-representation-for-cross","title":"Six-channel Image Representation for Cross-domain Object Detection","date":"2021-01-03","arxiv_id":"2101.00561","repositories_listed":0,"syntology":null},{"url":null,"slug":"weakly-supervised-multi-object-tracking-and","title":"Weakly Supervised Multi-Object Tracking and Segmentation","date":"2021-01-03","arxiv_id":"2101.00667","repositories_listed":0,"syntology":null},{"url":null,"slug":"3dvg-transformer-relation-modeling-for-visual","title":"3DVG-Transformer: Relation Modeling for Visual Grounding on Point Clouds","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"autonomous-learning-of-object-centric","title":"Autonomous Learning of Object-Centric Abstractions for High-Level Planning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"bayesian-meta-learning-for-few-shot-3d-shape","title":"Bayesian Meta-Learning for Few-Shot 3D Shape Completion","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"deep-active-learning-for-object-detection","title":"Deep Active Learning for Object Detection with Mixture Density Networks","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/deep-repulsive-clustering-of-ordered-data","slug":"deep-repulsive-clustering-of-ordered-data","title":"Deep Repulsive Clustering of Ordered Data Based on Order-Identity Decomposition","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/deep-transport-network-for-unsupervised-video","slug":"deep-transport-network-for-unsupervised-video","title":"Deep Transport Network for Unsupervised Video Object Segmentation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"dense-global-context-aware-rcnn-for-object","title":"Dense Global Context Aware RCNN for Object Detection","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-human-object-relationships-in","title":"Detecting Human-Object Relationships in Videos","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"discovering-human-interactions-with-large","title":"Discovering Human Interactions With Large-Vocabulary Objects via Query and Multi-Scale Detection","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"discrete-predictive-representation-for-long","title":"Discrete Predictive Representation for Long-horizon Planning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"domain-invariant-disentangled-network-for","title":"Domain-Invariant Disentangled Network for Generalizable Object Detection","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"ecs-net-improving-weakly-supervised-semantic","title":"ECS-Net: Improving Weakly Supervised Semantic Segmentation by Using Connections Between Class Activation Maps","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"empirical-upper-bound-in-object-detection","title":"EMPIRICAL UPPER BOUND IN OBJECT DETECTION","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-geometry-aware-contrast-and","title":"Exploring Geometry-Aware Contrast and Clustering Harmonization for Self-Supervised 3D Object Detection","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"factorizing-declarative-and-procedural","title":"Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"foc-osod-focus-on-classification-one-shot","title":"FOC OSOD: Focus on Classification One-Shot Object Detection","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"foreground-activation-maps-for-weakly","title":"Foreground Activation Maps for Weakly Supervised Object Localization","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"generating-furry-cars-disentangling-object","title":"Generating Furry Cars: Disentangling Object Shape and Appearance across Multiple Domains","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-bas3net-boundary-aware-semi-supervised","title":"Graph-BAS3Net: Boundary-Aware Semi-Supervised Segmentation Network With Bilateral Graph Convolution","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"grounding-physical-object-and-event-concepts","title":"Grounding Physical Object and Event Concepts Through Dynamic Visual Reasoning","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"high-performance-discriminative-tracking-with","title":"High-Performance Discriminative Tracking With Transformers","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"holistic-pose-graph-modeling-geometric","title":"Holistic Pose Graph: Modeling Geometric Structure Among Objects in a Scene Using Graph Inference for 3D Object Prediction","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"identifying-the-sources-of-uncertainty-in","title":"Identifying the Sources of Uncertainty in Object Classification","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"inas-integral-nas-for-device-aware-salient","title":"iNAS: Integral NAS for Device-Aware Salient Object Detection","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"interactive-prototype-learning-for-egocentric","title":"Interactive Prototype Learning for Egocentric Action Recognition","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"layouttransformer-relation-aware-scene-layout","title":"LayoutTransformer: Relation-Aware Scene Layout Generation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-a-unified-label-space","title":"Learning a unified label space","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"msfm-multi-scale-fusion-module-for-object","title":"MSFM: Multi-Scale Fusion Module for Object Detection","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-scale-network-architecture-search-for","title":"Multi-scale Network Architecture Search for Object Detection","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"non-maximum-suppression-also-closes-the","title":"Non-maximum Suppression Also Closes the Variational Approximation Gap of Multi-object Variational Autoencoders","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"objects-as-cameras-estimating-high-frequency","title":"Objects As Cameras: Estimating High-Frequency Illumination From Shadows","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"one-size-doesn-t-fit-all-adaptive-label","title":"One Size Doesn't Fit All: Adaptive Label Smoothing","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"predictive-uncertainty-in-deep-object","title":"Predictive Uncertainty in Deep Object Detectors: Estimation and Evaluation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"r-monet-region-based-unsupervised-scene","title":"R-MONet: Region-Based Unsupervised Scene Decomposition and Representation via Consistency of Object Representations","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-small-object-detection-on-the-water","title":"Robust Small Object Detection on the Water Surface Through Fusion of Camera and Millimeter Wave Radar","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"sgpa-structure-guided-prior-adaptation-for","title":"SGPA: Structure-Guided Prior Adaptation for Category-Level 6D Object Pose Estimation","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"stvgbert-a-visual-linguistic-transformer","title":"STVGBert: A Visual-Linguistic Transformer Based Framework for Spatio-Temporal Video Grounding","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"temporal-and-object-quantification-nets","title":"Temporal and Object Quantification Nets","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"understanding-mental-representations-of","title":"Understanding Mental Representations Of Objects Through Verbs Applied To Them","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"unsupervised-discovery-of-3d-physical-objects-1","title":"Unsupervised Discovery of 3D Physical Objects","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"venet-voting-enhancement-network-for-3d","title":"VENet: Voting Enhancement Network for 3D Object Detection","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"video-object-segmentation-with-dynamic-memory","title":"Video Object Segmentation With Dynamic Memory Networks and Adaptive Object Alignment","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"you-don-t-only-look-once-constructing-spatial","title":"You Don't Only Look Once: Constructing Spatial-Temporal Memory for Integrated 3D Object Detection and Tracking","date":"2021-01-01","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"language-mediated-object-centric-1","title":"Language-Mediated, Object-Centric Representation Learning","date":"2020-12-31","arxiv_id":"2012.15814","repositories_listed":0,"syntology":null},{"url":null,"slug":"mm-fsod-meta-and-metric-integrated-few-shot","title":"MM-FSOD: Meta and metric integrated few-shot object detection","date":"2020-12-30","arxiv_id":"2012.15159","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-sorting-using-faster-r-cnn","title":"Object sorting using faster R-CNN","date":"2020-12-29","arxiv_id":"2012.14840","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-partial-visual-tactile-fused","title":"Generative Partial Visual-Tactile Fused Object Clustering","date":"2020-12-28","arxiv_id":"2012.14070","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-category-extended-object-detector","title":"Towards a category-extended object detector with limited data","date":"2020-12-28","arxiv_id":"2012.14115","repositories_listed":0,"syntology":null},{"url":null,"slug":"ellipse-regression-with-predicted","title":"Ellipse Regression with Predicted Uncertainties for Accurate Multi-View 3D Object Estimation","date":"2020-12-27","arxiv_id":"2101.05212","repositories_listed":0,"syntology":null}],"record_sha256":"2697e55b245b528d020fc400aed4918badef4d3ef3a09582df5e9d9f0f6a9b15","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}