Papers Semi-Supervised Object Detection
“Semi-Supervised Object Detection” 태그가 달린 논문 117편 · 필터 해제
Boosting Semi-Supervised Object Detection in Remote Sensing Images With Active Teaching
The lack of object-level annotations poses a significant challenge for object detection in remote sensing images (RSIs). To address this issue, active learning (AL) and semi-supervised learning (SSL) techniques have been…
Active LearningObjectobject-detectionObject Detection+1Detection of Thermal Events by Semi-Supervised Learning for Tokamak First Wall Safety
This paper explores a semi-supervised object detection approach to detect hot spots on the internal wall of Tokamaks. A huge amount of data is produced during an experimental campaign by the infrared (IR) viewing systems…
object-detectionObject DetectionSemi-Supervised Object DetectionCredible Teacher for Semi-Supervised Object Detection in Open Scene
Semi-Supervised Object Detection (SSOD) has achieved resounding success by leveraging unlabeled data to improve detection performance. However, in Open Scene Semi-Supervised Object Detection (O-SSOD), unlabeled data may …
object-detectionObject DetectionSemi-Supervised Object DetectionA-Teacher: Asymmetric Network for 3D Semi-Supervised Object Detection
This work proposes the first online asymmetric semi-supervised framework namely A-Teacher for LiDAR-based 3D object detection. Our motivation stems from the observation that 1) existing symmetric teacher-student meth…
3D Object Detectionobject-detectionObject DetectionSemi-Supervised Object DetectionMixed Pseudo Labels for Semi-Supervised Object Detection
While the pseudo-label method has demonstrated considerable success in semi-supervised object detection tasks, this paper uncovers notable limitations within this approach. Specifically, the pseudo-label method tends to …
Objectobject-detectionObject DetectionPseudo Label+1Multiview Aerial Visual Recognition (MAVREC): Can Multi-view Improve Aerial Visual Perception?
Despite the commercial abundance of UAVs, aerial data acquisition remains challenging, and the existing Asia and North America-centric open-source UAV datasets are small-scale or low-resolution and lack diversity in scen…
BenchmarkingDiversityobject-detectionObject Detection+1Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection
Semi-supervised object detection is crucial for 3D scene understanding, efficiently addressing the limitation of acquiring large-scale 3D bounding box annotations. Existing methods typically employ a teacher-student fram…
3D Object DetectionDenoisingObjectobject-detection+4Weakly-semi-supervised object detection in remotely sensed imagery
Deep learning for detecting objects in remotely sensed imagery can enable new technologies for important applications including mitigating climate change. However, these models often require large datasets labeled with b…
Objectobject-detectionObject DetectionSemi-Supervised Object DetectionLEOD: Label-Efficient Object Detection for Event Cameras
Object detection with event cameras benefits from the sensor's low latency and high dynamic range. However, it is costly to fully label event streams for supervised training due to their high temporal resolution. To redu…
Event-based visionObject DetectionSemi-Supervised Object DetectionWeakly Supervised Object DetectionDensity-Guided Dense Pseudo Label Selection For Semi-supervised Oriented Object Detection
Recently, dense pseudo-label, which directly selects pseudo labels from the original output of the teacher model without any complicated post-processing steps, has received considerable attention in semi-supervised objec…
Objectobject-detectionObject DetectionOriented Object Detection+2Towards Few-Annotation Learning for Object Detection: Are Transformer-based Models More Efficient ?
For specialized and dense downstream tasks such as object detection, labeling data requires expertise and can be very expensive, making few-shot and semi-supervised models much more attractive alternatives. While in the …
Objectobject-detectionObject DetectionSemi-Supervised Object DetectionNavigating Data Heterogeneity in Federated Learning A Semi-Supervised Federated Object Detection
Federated Learning (FL) has emerged as a potent framework for training models across distributed data sources while maintaining data privacy. Nevertheless, it faces challenges with limited high-quality labels and non-IID…
Autonomous DrivingFederated LearningNavigateobject-detection+3Rethinking Scale Imbalance in Semi-supervised Object Detection for Aerial Images
This paper focuses on the scale imbalance problem of semi-supervised object detection(SSOD) in aerial images. Compared to natural images, objects in aerial images show smaller sizes and larger quantities per image, incre…
object-detectionObject DetectionPseudo LabelSemi-Supervised Object DetectionSemi-Supervised Object Detection with Uncurated Unlabeled Data for Remote Sensing Images
Annotating remote sensing images (RSIs) presents a notable challenge due to its labor-intensive nature. Semi-supervised object detection (SSOD) methods tackle this issue by generating pseudo-labels for the unlabeled data…
Objectobject-detectionObject DetectionSemi-Supervised Object DetectionALWOD: Active Learning for Weakly-Supervised Object Detection
Object detection (OD), a crucial vision task, remains challenged by the lack of large training datasets with precise object localization labels. In this work, we propose ALWOD, a new framework that addresses this problem…
Active LearningObjectobject-detectionObject Detection+3Density Crop-guided Semi-supervised Object Detection in Aerial Images
One of the important bottlenecks in training modern object detectors is the need for labeled images where bounding box annotations have to be produced for each object present in the image. This bottleneck is further exac…
Objectobject-detectionObject DetectionObject Detection In Aerial Images+1Generalized Open-World Semi-Supervised Object Detection
Traditional semi-supervised object detection methods assume a fixed set of object classes (in-distribution or ID classes) during training and deployment, which limits performance in real-world scenarios where unseen clas…
Objectobject-detectionObject DetectionSemi-Supervised Object DetectionAligned Unsupervised Pretraining of Object Detectors with Self-training
The unsupervised pretraining of object detectors has recently become a key component of object detector training, as it leads to improved performance and faster convergence during the supervised fine-tuning stage. Existi…
Few-Shot Object DetectionObjectobject-detectionObject Detection+2Training-based Model Refinement and Representation Disagreement for Semi-Supervised Object Detection
Semi-supervised object detection (SSOD) aims to improve the performance and generalization of existing object detectors by utilizing limited labeled data and extensive unlabeled data. Despite many advances, recent SSOD m…
object-detectionObject DetectionSemi-Supervised Object DetectionSemi-DETR: Semi-Supervised Object Detection with Detection Transformers
We analyze the DETR-based framework on semi-supervised object detection (SSOD) and observe that (1) the one-to-one assignment strategy generates incorrect matching when the pseudo ground-truth bounding box is inaccurate,…
Objectobject-detectionObject DetectionPseudo Label+1