Active Object Detection
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Benchmarks
PASCAL VOC 07+12
Most implemented
Distribution Discrepancy and Feature Heterogeneity for Active 3D Object Detection
Active Object Detection with Knowledge Aggregation and Distillation from Large Models
MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection
Sequential Voting with Relational Box Fields for Active Object Detection
Multiple instance active learning for object detection
Papers
Toward Active Object Detection for UAVs in the Wild: A Large-Scale Dataset, Benchmark and Method
Object detection is a fundamental component in numerous Unmanned Aerial Vehicle (UAV) applications, yet it has long been plagued by hindrances like occlusion or target pixel scarcity. Active Object Detection (AOD) provid…
Active Object DetectionRepresentation LearningReinforcement LearningEgoInteract: Synthetic Egocentric Videos Generation for Interaction Understanding and Anticipation
Collecting large-scale egocentric video datasets with dense spatial and temporal annotations is costly, slow, and often constrained by environmental biases, privacy constraints, and limited coverage of interaction patter…
Active Object DetectionAction SegmentationVideo GenerationPerformance-guided Reinforced Active Learning for Object Detection
Active learning (AL) strategies aim to train high-performance models with minimal labeling efforts, only selecting the most informative instances for annotation. Current approaches to evaluating data informativeness pred…
Active Object DetectionReinforcement LearningActive LearningBox-Level Class-Balanced Sampling for Active Object Detection
Training deep object detectors demands expensive bounding box annotation. Active learning (AL) is a promising technique to alleviate the annotation burden. Performing AL at box-level for object detection, i.e., selecting…
Active Object DetectionActive LearningUEVAVD: A Dataset for Developing UAV's Eye View Active Object Detection
Occlusion is a longstanding difficulty that challenges the UAV-based object detection. Many works address this problem by adapting the detection model. However, few of them exploit that the UAV could fundamentally improv…
Active Object DetectionDeep Reinforcement LearningInductive Biasobject-detection+1Distribution Discrepancy and Feature Heterogeneity for Active 3D Object Detection
LiDAR-based 3D object detection is a critical technology for the development of autonomous driving and robotics. However, the high cost of data annotation limits its advancement. We propose a novel and effective active l…
3D Object DetectionActive LearningActive Object DetectionAutonomous Driving+4