Papers Active Object Detection
“Active Object Detection” 태그가 달린 논문 18편 · 필터 해제
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+4Active Object Detection with Knowledge Aggregation and Distillation from Large Models
Accurately detecting active objects undergoing state changes is essential for comprehending human interactions and facilitating decision-making. The existing methods for active object detection (AOD) primarily rely on vi…
Active Object DetectionDecision MakingDecoderKnowledge Distillation+3Error Detection in Egocentric Procedural Task Videos
We present a new egocentric procedural error dataset containing videos with various types of errors as well as normal videos and propose a new framework for procedural error detection using error-free training videos…
Action SegmentationActive Object DetectionAnomaly DetectionError Understanding+3Aligning Data Selection with Performance: Performance-driven Reinforcement Learning for Active Learning in Object Detection
Active learning strategies aim to train high-performance models with minimal labeled data by selecting the most informative instances for labeling. However, existing methods for assessing data informativeness often fail …
Active LearningActive Object DetectionInformativenessNavigate+5Learning to View: Decision Transformers for Active Object Detection
Active perception describes a broad class of techniques that couple planning and perception systems to move the robot in a way to give the robot more information about the environment. In most robotic systems, perception…
Active Object DetectionMotion PlanningObjectobject-detection+3MUS-CDB: Mixed Uncertainty Sampling with Class Distribution Balancing for Active Annotation in Aerial Object Detection
Recent aerial object detection models rely on a large amount of labeled training data, which requires unaffordable manual labeling costs in large aerial scenes with dense objects. Active learning effectively reduces the …
Active LearningActive Object DetectionInformativenessObject+2Sequential Voting with Relational Box Fields for Active Object Detection
A key component of understanding hand-object interactions is the ability to identify the active object -- the object that is being manipulated by the human hand. In order to accurately localize the active object, any met…
Active Object DetectionDecision MakingImitation LearningObject+3SCOD: Active Object Detection for Embodied Agents using Sensory Commutativity of Action Sequences
We introduce SCOD (Sensory Commutativity Object Detection), an active method for movable and immovable object detection. SCOD exploits the commutative properties of action sequences, in the scenario of an embodied agent …
Active Object DetectionObjectobject-detectionObject Detection+1Multiple instance active learning for object detection
Despite the substantial progress of active learning for image recognition, there still lacks an instance-level active learning method specified for object detection. In this paper, we propose Multiple Instance Active Obj…
Active LearningActive Object DetectionMultiple Instance LearningObject+2Adaptive Remote Sensing Image Attribute Learning for Active Object Detection
In recent years, deep learning methods bring incredible progress to the field of object detection. However, in the field of remote sensing image processing, existing methods neglect the relationship between imaging confi…
Active Object DetectionAttributeDeep Reinforcement LearningObject+2The MECCANO Dataset: Understanding Human-Object Interactions from Egocentric Videos in an Industrial-like Domain
Wearable cameras allow to collect images and videos of humans interacting with the world. While human-object interactions have been thoroughly investigated in third person vision, the problem has been understudied in ego…
Action RecognitionActive Object DetectionHuman-Object Interaction DetectionObject+3Active Detection and Localization of Textureless Objects in Cluttered Environments
This paper introduces an active object detection and localization framework that combines a robust untextured object detection and 3D pose estimation algorithm with a novel next-best-view selection strategy. We address t…
3D Pose EstimationActive Object DetectionObjectobject-detection+3Nonmyopic View Planning for Active Object Detection
One of the central problems in computer vision is the detection of semantically important objects and the estimation of their pose. Most of the work in object detection has been based on single image processing and its p…
Active Object DetectionObjectobject-detectionObject Detection+1