Papers Semi-Supervised Object Detection
“Semi-Supervised Object Detection” 태그가 달린 논문 117편 · 필터 해제
DExTeR: Weakly Semi-Supervised Object Detection with Class and Instance Experts for Medical Imaging
Detecting anatomical landmarks in medical imaging is essential for diagnosis and intervention guidance. However, object detection models rely on costly bounding box annotations, limiting scalability. Weakly Semi-Supervis…
Semi-Supervised Object DetectionPractical Insights into Semi-Supervised Object Detection Approaches
Learning in data-scarce settings has recently gained significant attention in the research community. Semi-supervised object detection(SSOD) aims to improve detection performance by leveraging a large number of unlabeled…
Semi-Supervised Object DetectionFew-Shot LearningBuilding Blocks for Robust and Effective Semi-Supervised Real-World Object Detection
Semi-supervised object detection (SSOD) based on pseudo-labeling significantly reduces dependence on large labeled datasets by effectively leveraging both labeled and unlabeled data. However, real-world applications of S…
Autonomous DrivingData Augmentationobject-detectionObject Detection+2ClipGrader: Leveraging Vision-Language Models for Robust Label Quality Assessment in Object Detection
High-quality annotations are essential for object detection models, but ensuring label accuracy - especially for bounding boxes - remains both challenging and costly. This paper introduces ClipGrader, a novel approach th…
Objectobject-detectionObject DetectionPseudo Label+1Semi-Supervised Weed Detection in Vegetable Fields: In-domain and Cross-domain Experiments
Robust weed detection remains a challenging task in precision weeding, requiring not only potent weed detection models but also large-scale, labeled data. However, the labeled data adequate for model training is practica…
Semi-Supervised Object DetectionSimLTD: Simple Supervised and Semi-Supervised Long-Tailed Object Detection
Recent years have witnessed tremendous advances on modern visual recognition systems. Despite such progress, many vision models still struggle with the open problem of learning from few exemplars. This paper focuses on t…
Few-Shot Object DetectionLong-tailed Object DetectionObject DetectionSemi-Supervised Object Detection+1Co-Learning: Towards Semi-Supervised Object Detection with Road-side Cameras
Recently, deep learning has experienced rapid expansion, contributing significantly to the progress of supervised learning methodologies. However, acquiring labeled data in real-world settings can be costly, labor-intens…
Navigateobject-detectionObject DetectionSemi-Supervised Object DetectionCollaborative Feature-Logits Contrastive Learning for Open-Set Semi-Supervised Object Detection
Current Semi-Supervised Object Detection (SSOD) methods enhance detector performance by leveraging large amounts of unlabeled data, assuming that both labeled and unlabeled data share the same label space. However, in op…
Contrastive Learningobject-detectionObject DetectionSemi-Supervised Object DetectionApplying the Lower-Biased Teacher Model in Semi-Supervised Object Detection
I present the Lower Biased Teacher model, an enhancement of the Unbiased Teacher model, specifically tailored for semi-supervised object detection tasks. The primary innovation of this model is the integration of a local…
Objectobject-detectionObject DetectionPseudo Label+1Semi-Supervised 3D Object Detection with Channel Augmentation using Transformation Equivariance
Accurate 3D object detection is crucial for autonomous vehicles and robots to navigate and interact with the environment safely and effectively. Meanwhile, the performance of 3D detector relies on the data size and annot…
3D Object DetectionAutonomous VehiclesNavigateobject-detection+2Class-balanced Open-set Semi-supervised Object Detection for Medical Images
Medical image datasets in the real world are often unlabeled and imbalanced, and Semi-Supervised Object Detection (SSOD) can utilize unlabeled data to improve an object detector. However, existing approaches predominantl…
Objectobject-detectionObject DetectionOut-of-Distribution Detection+1Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer
The impressive advancements in semi-supervised learning have driven researchers to explore its potential in object detection tasks within the field of computer vision. Semi-Supervised Object Detection (SSOD) leverages a …
Data AugmentationObjectobject-detectionObject Detection+1Multi-clue Consistency Learning to Bridge Gaps Between General and Oriented Object in Semi-supervised Detection
While existing semi-supervised object detection (SSOD) methods perform well in general scenes, they encounter challenges in handling oriented objects in aerial images. We experimentally find three gaps between general an…
object-detectionObject DetectionOriented Object DetectionSemi-Supervised Object DetectionSOOD++: Leveraging Unlabeled Data to Boost Oriented Object Detection
Semi-supervised object detection (SSOD), leveraging unlabeled data to boost object detectors, has become a hot topic recently. However, existing SSOD approaches mainly focus on horizontal objects, leaving multi-oriented …
Objectobject-detectionObject DetectionOriented Object Detection+2Power of Cooperative Supervision: Multiple Teachers Framework for Enhanced 3D Semi-Supervised Object Detection
To ensure safe urban driving for autonomous platforms, it is crucial not only to develop high-performance object detection techniques but also to establish a diverse and representative dataset that captures various urban…
Objectobject-detectionObject DetectionSemi-Supervised Object DetectionCollaboration of Teachers for Semi-supervised Object Detection
Recent semi-supervised object detection (SSOD) has achieved remarkable progress by leveraging unlabeled data for training. Mainstream SSOD methods rely on Consistency Regularization methods and Exponential Moving Average…
Objectobject-detectionObject DetectionSemi-Supervised Object DetectionIMWA: Iterative Model Weight Averaging Benefits Class-Imbalanced Learning Tasks
Model Weight Averaging (MWA) is a technique that seeks to enhance model's performance by averaging the weights of multiple trained models. This paper first empirically finds that 1) the vanilla MWA can benefit the class-…
image-classificationImage Classificationobject-detectionObject Detection+1Sparse Semi-DETR: Sparse Learnable Queries for Semi-Supervised Object Detection
In this paper, we address the limitations of the DETR-based semi-supervised object detection (SSOD) framework, particularly focusing on the challenges posed by the quality of object queries. In DETR-based SSOD, the one-t…
Objectobject-detectionObject DetectionPseudo Label+2Gradient-based Sampling for Class Imbalanced Semi-supervised Object Detection
Current semi-supervised object detection (SSOD) algorithms typically assume class balanced datasets (PASCAL VOC etc.) or slightly class imbalanced datasets (MS-COCO, etc). This assumption can be easily violated since rea…
object-detectionObject DetectionSemi-Supervised Object DetectionSeMoLi: What Moves Together Belongs Together
We tackle semi-supervised object detection based on motion cues. Recent results suggest that heuristic-based clustering methods in conjunction with object trackers can be used to pseudo-label instances of moving objects …
ClusteringObjectobject-detectionObject Detection+3