{"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/61","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":61,"pages_in_order":107,"rows_per_page":100,"rows":[6001,6100],"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/60","next":"/task/object/papers/62","papers":[{"url":null,"slug":"colmix-a-simple-data-augmentation-framework","title":"ColMix -- A Simple Data Augmentation Framework to Improve Object Detector Performance and Robustness in Aerial Images","date":"2023-05-22","arxiv_id":"2305.13509","repositories_listed":0,"syntology":null},{"url":null,"slug":"tinyissimoyolo-a-quantized-low-memory","title":"TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers","date":"2023-05-22","arxiv_id":"2306.00001","repositories_listed":0,"syntology":null},{"url":null,"slug":"you-only-look-at-one-category-level-object","title":"You Only Look at One: Category-Level Object Representations for Pose Estimation From a Single Example","date":"2023-05-22","arxiv_id":"2305.12626","repositories_listed":0,"syntology":null},{"url":null,"slug":"dexpbt-scaling-up-dexterous-manipulation-for","title":"DexPBT: Scaling up Dexterous Manipulation for Hand-Arm Systems with Population Based Training","date":"2023-05-20","arxiv_id":"2305.12127","repositories_listed":0,"syntology":null},{"url":null,"slug":"evaluation-metrics-for-cnns-compression","title":"Evaluation Metrics for DNNs Compression","date":"2023-05-18","arxiv_id":"2305.10616","repositories_listed":0,"syntology":null},{"url":null,"slug":"monotdp-twin-depth-perception-for-monocular","title":"MonoTDP: Twin Depth Perception for Monocular 3D Object Detection in Adverse Scenes","date":"2023-05-18","arxiv_id":"2305.10974","repositories_listed":0,"syntology":null},{"url":null,"slug":"slotdiffusion-object-centric-generative-1","title":"SlotDiffusion: Object-Centric Generative Modeling with Diffusion Models","date":"2023-05-18","arxiv_id":"2305.11281","repositories_listed":0,"syntology":null},{"url":null,"slug":"vision-language-pre-training-with-object","title":"Vision-Language Pre-training with Object Contrastive Learning for 3D Scene Understanding","date":"2023-05-18","arxiv_id":"2305.10714","repositories_listed":0,"syntology":null},{"url":null,"slug":"s-3-track-self-supervised-tracking-with-soft","title":"S$^3$Track: Self-supervised Tracking with Soft Assignment Flow","date":"2023-05-17","arxiv_id":"2305.09981","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-object-re-identification-from-point","title":"Object Re-Identification from Point Clouds","date":"2023-05-17","arxiv_id":"2305.10210","repositories_listed":0,"syntology":null},{"url":null,"slug":"correlation-pyramid-network-for-3d-single","title":"Correlation Pyramid Network for 3D Single Object Tracking","date":"2023-05-16","arxiv_id":"2305.09195","repositories_listed":0,"syntology":null},{"url":null,"slug":"inductive-graph-neural-networks-for-moving","title":"Inductive Graph Neural Networks for Moving Object Segmentation","date":"2023-05-16","arxiv_id":"2305.09585","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-higher-order-object-interactions-for","title":"Learning Higher-order Object Interactions for Keypoint-based Video Understanding","date":"2023-05-16","arxiv_id":"2305.09539","repositories_listed":0,"syntology":null},{"url":null,"slug":"revisiting-proprioceptive-sensing-for","title":"Revisiting Proprioceptive Sensing for Articulated Object Manipulation","date":"2023-05-16","arxiv_id":"2305.09584","repositories_listed":0,"syntology":null},{"url":null,"slug":"sctracker-multi-object-tracking-with-shape","title":"SCTracker: Multi-object tracking with shape and confidence constraints","date":"2023-05-16","arxiv_id":"2305.09523","repositories_listed":0,"syntology":null},{"url":null,"slug":"autorecon-automated-3d-object-discovery-and","title":"AutoRecon: Automated 3D Object Discovery and Reconstruction","date":"2023-05-15","arxiv_id":"2305.08810","repositories_listed":0,"syntology":null},{"url":null,"slug":"combining-geolocation-and-height-estimation","title":"Combining geolocation and height estimation of objects from street level imagery","date":"2023-05-14","arxiv_id":"2305.08232","repositories_listed":0,"syntology":null},{"url":null,"slug":"instance-aware-repeat-factor-sampling-for","title":"Instance-Aware Repeat Factor Sampling for Long-Tailed Object Detection","date":"2023-05-14","arxiv_id":"2305.08069","repositories_listed":0,"syntology":null},{"url":null,"slug":"aura-automatic-mask-generator-using","title":"AURA : Automatic Mask Generator using Randomized Input Sampling for Object Removal","date":"2023-05-13","arxiv_id":"2305.07857","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-object-slam-framework-for-association","title":"An Object SLAM Framework for Association, Mapping, and High-Level Tasks","date":"2023-05-12","arxiv_id":"2305.07299","repositories_listed":0,"syntology":null},{"url":null,"slug":"pillaracc-sparse-pointpillars-accelerator-for","title":"SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving","date":"2023-05-12","arxiv_id":"2305.07522","repositories_listed":0,"syntology":null},{"url":null,"slug":"local-region-to-region-mapping-based-approach","title":"Local Region-to-Region Mapping-based Approach to Classify Articulated Objects","date":"2023-05-10","arxiv_id":"2305.06394","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequence-agnostic-multi-object-navigation","title":"Sequence-Agnostic Multi-Object Navigation","date":"2023-05-10","arxiv_id":"2305.06178","repositories_listed":0,"syntology":null},{"url":null,"slug":"effects-of-real-life-traffic-sign-alteration","title":"Effects of Real-Life Traffic Sign Alteration on YOLOv7- an Object Recognition Model","date":"2023-05-09","arxiv_id":"2305.05499","repositories_listed":0,"syntology":null},{"url":null,"slug":"egocentric-hierarchical-visual-semantics","title":"Egocentric Hierarchical Visual Semantics","date":"2023-05-09","arxiv_id":"2305.05422","repositories_listed":0,"syntology":null},{"url":null,"slug":"group-activity-recognition-via-dynamic","title":"Group Activity Recognition via Dynamic Composition and Interaction","date":"2023-05-09","arxiv_id":"2305.05583","repositories_listed":0,"syntology":null},{"url":null,"slug":"high-throughput-cotton-phenotyping-big-data","title":"High-throughput Cotton Phenotyping Big Data Pipeline Lambda Architecture Computer Vision Deep Neural Networks","date":"2023-05-09","arxiv_id":"2305.05423","repositories_listed":0,"syntology":null},{"url":null,"slug":"deformernet-learning-bimanual-manipulation-of","title":"DeformerNet: Learning Bimanual Manipulation of 3D Deformable Objects","date":"2023-05-08","arxiv_id":"2305.04449","repositories_listed":0,"syntology":null},{"url":null,"slug":"seggpt-meets-co-saliency-scene","title":"SegGPT Meets Co-Saliency Scene","date":"2023-05-08","arxiv_id":"2305.04396","repositories_listed":0,"syntology":null},{"url":null,"slug":"cognition-guided-human-object-relationship","title":"Cognition Guided Human-Object Relationship Detection","date":"2023-05-06","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-hybrid-actor-critic-maps-for-6d-non","title":"HACMan: Learning Hybrid Actor-Critic Maps for 6D Non-Prehensile Manipulation","date":"2023-05-06","arxiv_id":"2305.03942","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-object-video-generation-from-single","title":"Multi-object Video Generation from Single Frame Layouts","date":"2023-05-06","arxiv_id":"2305.03983","repositories_listed":0,"syntology":null},{"url":null,"slug":"human-attention-guided-explainable-artificial","title":"Human Attention-Guided Explainable Artificial Intelligence for Computer Vision Models","date":"2023-05-05","arxiv_id":"2305.03601","repositories_listed":0,"syntology":null},{"url":null,"slug":"haista-net-human-assisted-instance","title":"HAISTA-NET: Human Assisted Instance Segmentation Through Attention","date":"2023-05-04","arxiv_id":"2305.03105","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-hand-held-object-reconstruction-from","title":"3D Reconstruction of Objects in Hands without Real World 3D Supervision","date":"2023-05-04","arxiv_id":"2305.03036","repositories_listed":0,"syntology":null},{"url":null,"slug":"modelling-spatio-temporal-interactions-for","title":"Modelling Spatio-Temporal Interactions for Compositional Action Recognition","date":"2023-05-04","arxiv_id":"2305.02673","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-systematic-study-on-object-recognition","title":"A Systematic Study on Object Recognition Using Millimeter-wave Radar","date":"2023-05-03","arxiv_id":"2305.02085","repositories_listed":0,"syntology":null},{"url":null,"slug":"attention-based-feature-fusion-for-multi","title":"Attention Based Feature Fusion For Multi-Agent Collaborative Perception","date":"2023-05-03","arxiv_id":"2305.02061","repositories_listed":0,"syntology":null},{"url":null,"slug":"illicit-item-detection-in-x-ray-images-for","title":"Illicit item detection in X-ray images for security applications","date":"2023-05-03","arxiv_id":"2305.01936","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-based-relational-object-matching","title":"Learning-based Relational Object Matching Across Views","date":"2023-05-03","arxiv_id":"2305.02398","repositories_listed":0,"syntology":null},{"url":"/paper/contactart-learning-3d-interaction-priors-for","slug":"contactart-learning-3d-interaction-priors-for","title":"ContactArt: Learning 3D Interaction Priors for Category-level Articulated Object and Hand Poses Estimation","date":"2023-05-02","arxiv_id":"2305.01618","repositories_listed":0,"syntology":null},{"url":null,"slug":"drpt-disentangled-and-recurrent-prompt-tuning","title":"DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning","date":"2023-05-02","arxiv_id":"2305.01239","repositories_listed":0,"syntology":null},{"url":null,"slug":"transcar-transformer-based-camera-and-radar","title":"TransCAR: Transformer-based Camera-And-Radar Fusion for 3D Object Detection","date":"2023-04-30","arxiv_id":"2305.00397","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-extensible-multimodal-multi-task-object","title":"An Extensible Multimodal Multi-task Object Dataset with Materials","date":"2023-04-29","arxiv_id":"2305.14352","repositories_listed":0,"syntology":null},{"url":null,"slug":"infradet3d-multi-modal-3d-object-detection","title":"InfraDet3D: Multi-Modal 3D Object Detection based on Roadside Infrastructure Camera and LiDAR Sensors","date":"2023-04-29","arxiv_id":"2305.00314","repositories_listed":0,"syntology":null},{"url":null,"slug":"regularizing-self-training-for-unsupervised","title":"Regularizing Self-training for Unsupervised Domain Adaptation via Structural Constraints","date":"2023-04-29","arxiv_id":"2305.00131","repositories_listed":0,"syntology":null},{"url":null,"slug":"compositional-3d-human-object-neural","title":"Compositional 3D Human-Object Neural Animation","date":"2023-04-27","arxiv_id":"2304.14070","repositories_listed":0,"syntology":null},{"url":null,"slug":"dual-feature-fusion-attention-network-for","title":"Dual-feature Fusion Attention Network for Small Object Segmentation","date":"2023-04-27","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-articulated-shape-with-keypoint","title":"Learning Articulated Shape with Keypoint Pseudo-labels from Web Images","date":"2023-04-27","arxiv_id":"2304.14396","repositories_listed":0,"syntology":null},{"url":null,"slug":"oricon3d-effective-3d-object-detection-using","title":"OriCon3D: Effective 3D Object Detection using Orientation and Confidence","date":"2023-04-27","arxiv_id":"2304.14484","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-precise-weakly-supervised-object","title":"Towards Precise Weakly Supervised Object Detection via Interactive Contrastive Learning of Context Information","date":"2023-04-27","arxiv_id":"2304.14114","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparison-of-artificial-neural-network","title":"Comparison of artificial neural network adaptive control techniques for a nonlinear system with delay","date":"2023-04-26","arxiv_id":"2304.13468","repositories_listed":0,"syntology":null},{"url":null,"slug":"group-equivariant-bev-for-3d-object-detection","title":"Group Equivariant BEV for 3D Object Detection","date":"2023-04-26","arxiv_id":"2304.13390","repositories_listed":0,"syntology":null},{"url":"/paper/neural-pbir-reconstruction-of-shape-material","slug":"neural-pbir-reconstruction-of-shape-material","title":"Neural-PBIR Reconstruction of Shape, Material, and Illumination","date":"2023-04-26","arxiv_id":"2304.13445","repositories_listed":0,"syntology":null},{"url":null,"slug":"object-semantics-give-us-the-depth-we-need","title":"Object Semantics Give Us the Depth We Need: Multi-task Approach to Aerial Depth Completion","date":"2023-04-25","arxiv_id":"2304.12542","repositories_listed":0,"syntology":null},{"url":null,"slug":"hosnerf-dynamic-human-object-scene-neural","title":"HOSNeRF: Dynamic Human-Object-Scene Neural Radiance Fields from a Single Video","date":"2023-04-24","arxiv_id":"2304.12281","repositories_listed":0,"syntology":null},{"url":null,"slug":"meta-tuning-loss-functions-and-data","title":"Meta-tuning Loss Functions and Data Augmentation for Few-shot Object Detection","date":"2023-04-24","arxiv_id":"2304.12161","repositories_listed":0,"syntology":null},{"url":null,"slug":"robust-and-efficient-memory-network-for-video","title":"Robust and Efficient Memory Network for Video Object Segmentation","date":"2023-04-24","arxiv_id":"2304.11840","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformer-based-stereo-aware-3d-object","title":"Transformer-based stereo-aware 3D object detection from binocular images","date":"2023-04-24","arxiv_id":"2304.11906","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-framework-for-benchmarking-real-time","title":"A Framework for Benchmarking Real-Time Embedded Object Detection","date":"2023-04-23","arxiv_id":"2304.11580","repositories_listed":0,"syntology":null},{"url":null,"slug":"omnilabel-a-challenging-benchmark-for","title":"OmniLabel: A Challenging Benchmark for Language-Based Object Detection","date":"2023-04-22","arxiv_id":"2304.11463","repositories_listed":0,"syntology":null},{"url":null,"slug":"factored-neural-representation-for-scene","title":"Factored Neural Representation for Scene Understanding","date":"2023-04-21","arxiv_id":"2304.10950","repositories_listed":0,"syntology":null},{"url":null,"slug":"omni-line-of-sight-imaging-for-holistic-shape","title":"Omni-Line-of-Sight Imaging for Holistic Shape Reconstruction","date":"2023-04-21","arxiv_id":"2304.10780","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-object-detection-robustness-a","title":"Enhancing object detection robustness: A synthetic and natural perturbation approach","date":"2023-04-20","arxiv_id":"2304.10622","repositories_listed":0,"syntology":null},{"url":null,"slug":"farm3d-learning-articulated-3d-animals-by","title":"Farm3D: Learning Articulated 3D Animals by Distilling 2D Diffusion","date":"2023-04-20","arxiv_id":"2304.10535","repositories_listed":0,"syntology":null},{"url":null,"slug":"automatic-interaction-and-activity","title":"Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection","date":"2023-04-19","arxiv_id":"2304.09789","repositories_listed":0,"syntology":null},{"url":null,"slug":"mmdr-a-result-feature-fusion-object-detection","title":"MMDR: A Result Feature Fusion Object Detection Approach for Autonomous System","date":"2023-04-19","arxiv_id":"2304.09609","repositories_listed":0,"syntology":null},{"url":null,"slug":"post-training-quantization-for-object","title":"Improving Post-Training Quantization on Object Detection with Task Loss-Guided Lp Metric","date":"2023-04-19","arxiv_id":"2304.09785","repositories_listed":0,"syntology":null},{"url":null,"slug":"cabinet-scaling-neural-collision-detection","title":"CabiNet: Scaling Neural Collision Detection for Object Rearrangement with Procedural Scene Generation","date":"2023-04-18","arxiv_id":"2304.09302","repositories_listed":0,"syntology":null},{"url":null,"slug":"event-camera-and-lidar-based-human-tracking","title":"Event Camera and LiDAR based Human Tracking for Adverse Lighting Conditions in Subterranean Environments","date":"2023-04-18","arxiv_id":"2304.08908","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-sim-to-real-dense-object-descriptors","title":"Learning Sim-to-Real Dense Object Descriptors for Robotic Manipulation","date":"2023-04-18","arxiv_id":"2304.08703","repositories_listed":0,"syntology":null},{"url":null,"slug":"perceive-excavate-and-purify-a-novel-object","title":"Perceive, Excavate and Purify: A Novel Object Mining Framework for Instance Segmentation","date":"2023-04-18","arxiv_id":"2304.08826","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-multi-view-data-for-improved","title":"Leveraging Multi-view Data for Improved Detection Performance: An Industrial Use Case","date":"2023-04-17","arxiv_id":"2304.08111","repositories_listed":0,"syntology":null},{"url":null,"slug":"bent-broken-bicycles-leveraging-synthetic","title":"Bent & Broken Bicycles: Leveraging synthetic data for damaged object re-identification","date":"2023-04-16","arxiv_id":"2304.07883","repositories_listed":0,"syntology":null},{"url":null,"slug":"likelihood-based-generative-radiance-field","title":"Likelihood-Based Generative Radiance Field with Latent Space Energy-Based Model for 3D-Aware Disentangled Image Representation","date":"2023-04-16","arxiv_id":"2304.07918","repositories_listed":0,"syntology":null},{"url":null,"slug":"odsmoothgrad-generating-saliency-maps-for","title":"ODSmoothGrad: Generating Saliency Maps for Object Detectors","date":"2023-04-15","arxiv_id":"2304.07609","repositories_listed":0,"syntology":null},{"url":"/paper/yolo-drone-airborne-real-time-detection-of","slug":"yolo-drone-airborne-real-time-detection-of","title":"YOLO-Drone:Airborne real-time detection of dense small objects from high-altitude perspective","date":"2023-04-14","arxiv_id":"2304.06925","repositories_listed":0,"syntology":null},{"url":null,"slug":"gamifying-math-education-using-object","title":"Gamifying Math Education using Object Detection","date":"2023-04-13","arxiv_id":"2304.06270","repositories_listed":0,"syntology":null},{"url":null,"slug":"palf-pre-annotation-and-camera-lidar-late","title":"PALF: Pre-Annotation and Camera-LiDAR Late Fusion for the Easy Annotation of Point Clouds","date":"2023-04-13","arxiv_id":"2304.08591","repositories_listed":0,"syntology":null},{"url":null,"slug":"rosi-recovering-3d-shape-interiors-from-few","title":"RoSI: Recovering 3D Shape Interiors from Few Articulation Images","date":"2023-04-13","arxiv_id":"2304.06342","repositories_listed":0,"syntology":null},{"url":null,"slug":"tracking-by-3d-model-estimation-of-unknown","title":"Tracking by 3D Model Estimation of Unknown Objects in Videos","date":"2023-04-13","arxiv_id":"2304.06419","repositories_listed":0,"syntology":null},{"url":null,"slug":"impact-of-pseudo-depth-on-open-world-object","title":"Impact of Pseudo Depth on Open World Object Segmentation with Minimal User Guidance","date":"2023-04-12","arxiv_id":"2304.05716","repositories_listed":0,"syntology":null},{"url":null,"slug":"mesh2tex-generating-mesh-textures-from-image","title":"Mesh2Tex: Generating Mesh Textures from Image Queries","date":"2023-04-12","arxiv_id":"2304.05868","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-comprehensive-study-on-object-detection","title":"A Comprehensive Study on Object Detection Techniques in Unconstrained Environments","date":"2023-04-11","arxiv_id":"2304.05295","repositories_listed":0,"syntology":null},{"url":null,"slug":"bounding-box-annotation-with-visible-status","title":"Efficiently Collecting Training Dataset for 2D Object Detection by Online Visual Feedback","date":"2023-04-11","arxiv_id":"2304.04901","repositories_listed":0,"syntology":null},{"url":null,"slug":"crowdsim2-an-open-synthetic-benchmark-for","title":"CrowdSim2: an Open Synthetic Benchmark for Object Detectors","date":"2023-04-11","arxiv_id":"2304.05090","repositories_listed":0,"syntology":null},{"url":null,"slug":"density-map-distillation-for-incremental","title":"Density Map Distillation for Incremental Object Counting","date":"2023-04-11","arxiv_id":"2304.05255","repositories_listed":0,"syntology":null},{"url":null,"slug":"most-multiple-object-localization-with-self","title":"MOST: Multiple Object localization with Self-supervised Transformers for object discovery","date":"2023-04-11","arxiv_id":"2304.05387","repositories_listed":0,"syntology":null},{"url":null,"slug":"overload-latency-attacks-on-object-detection","title":"Overload: Latency Attacks on Object Detection for Edge Devices","date":"2023-04-11","arxiv_id":"2304.05370","repositories_listed":0,"syntology":null},{"url":null,"slug":"head-tail-loss-a-simple-function-for-oriented","title":"Head-tail Loss: A simple function for Oriented Object Detection and Anchor-free models","date":"2023-04-10","arxiv_id":"2304.04503","repositories_listed":0,"syntology":null},{"url":null,"slug":"shapeshift-superquadric-based-object-pose","title":"ShapeShift: Superquadric-based Object Pose Estimation for Robotic Grasping","date":"2023-04-10","arxiv_id":"2304.04861","repositories_listed":0,"syntology":null},{"url":null,"slug":"self-supervised-learning-of-object","title":"Self-Supervised Learning of Object Segmentation from Unlabeled RGB-D Videos","date":"2023-04-09","arxiv_id":"2304.04325","repositories_listed":0,"syntology":null},{"url":null,"slug":"sparse-dense-fusion-for-3d-object-detection","title":"Sparse Dense Fusion for 3D Object Detection","date":"2023-04-09","arxiv_id":"2304.04179","repositories_listed":0,"syntology":null},{"url":null,"slug":"devil-s-on-the-edges-selective-quad-attention","title":"Devil's on the Edges: Selective Quad Attention for Scene Graph Generation","date":"2023-04-07","arxiv_id":"2304.03495","repositories_listed":0,"syntology":null},{"url":null,"slug":"language-aware-multiple-datasets-detection","title":"Language-aware Multiple Datasets Detection Pretraining for DETRs","date":"2023-04-07","arxiv_id":"2304.03580","repositories_listed":0,"syntology":null},{"url":null,"slug":"reduce-reuse-recycle-modular-multi-object","title":"MOPA: Modular Object Navigation with PointGoal Agents","date":"2023-04-07","arxiv_id":"2304.03696","repositories_listed":0,"syntology":null},{"url":null,"slug":"continual-detection-transformer-for","title":"Continual Detection Transformer for Incremental Object Detection","date":"2023-04-06","arxiv_id":"2304.03110","repositories_listed":0,"syntology":null},{"url":null,"slug":"ditto-nerf-diffusion-based-iterative-text-to","title":"DITTO-NeRF: Diffusion-based Iterative Text To Omni-directional 3D Model","date":"2023-04-06","arxiv_id":"2304.02827","repositories_listed":0,"syntology":null},{"url":null,"slug":"exposing-and-mitigating-spurious-correlations","title":"Exposing and Mitigating Spurious Correlations for Cross-Modal Retrieval","date":"2023-04-06","arxiv_id":"2304.03391","repositories_listed":0,"syntology":null},{"url":null,"slug":"instant-nvr-instant-neural-volumetric","title":"Instant-NVR: Instant Neural Volumetric Rendering for Human-object Interactions from Monocular RGBD Stream","date":"2023-04-06","arxiv_id":"2304.03184","repositories_listed":0,"syntology":null}],"record_sha256":"105720ef90c105279d17a4dfba2dac01b79bd483fa65403bc5387dd202f04aa7","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}