Papers Semi-Supervised Instance Segmentation
“Semi-Supervised Instance Segmentation” 태그가 달린 논문 20편 · 필터 해제
StomataSeg: Semi-Supervised Instance Segmentation for Sorghum Stomatal Components
Sorghum is a globally important cereal grown widely in water-limited and stress-prone regions. Its strong drought tolerance makes it a priority crop for climate-resilient agriculture. Improving water-use efficiency in so…
Semi-Supervised Instance SegmentationCAST: Contrastive Adaptation and Distillation for Semi-Supervised Instance Segmentation
Instance segmentation demands costly per-pixel annotations and large models. We introduce CAST, a semi-supervised knowledge distillation (SSKD) framework that compresses pretrained vision foundation models (VFM) into com…
Domain AdaptationInstance SegmentationKnowledge DistillationPseudo Label+2Pseudo-Label Quality Decoupling and Correction for Semi-Supervised Instance Segmentation
Semi-Supervised Instance Segmentation (SSIS) involves classifying and grouping image pixels into distinct object instances using limited labeled data. This learning paradigm usually faces a significant challenge of unsta…
Instance SegmentationPseudo LabelSemantic SegmentationSemi-Supervised Instance SegmentationS^4M: Boosting Semi-Supervised Instance Segmentation with SAM
Semi-supervised instance segmentation poses challenges due to limited labeled data, causing difficulties in accurately localizing distinct object instances. Current teacher-student frameworks still suffer from performanc…
Data AugmentationInstance SegmentationPseudo LabelSegmentation+2Depth-Guided Semi-Supervised Instance Segmentation
Semi-Supervised Instance Segmentation (SSIS) aims to leverage an amount of unlabeled data during training. Previous frameworks primarily utilized the RGB information of unlabeled images to generate pseudo-labels. However…
Depth EstimationInstance SegmentationSemantic SegmentationSemi-Supervised Instance SegmentationBetter (pseudo-)labels for semi-supervised instance segmentation
Despite the availability of large datasets for tasks like image classification and image-text alignment, labeled data for more complex recognition tasks, such as detection and segmentation, is less abundant. In particula…
Few-Shot Learningimage-classificationImage ClassificationInstance Segmentation+2Boundary-aware Contrastive Learning for Semi-supervised Nuclei Instance Segmentation
Semi-supervised segmentation methods have demonstrated promising results in natural scenarios, providing a solution to reduce dependency on manual annotation. However, these methods face significant challenges when direc…
Contrastive LearningDenoisingInstance SegmentationSegmentation+2Semi-supervised Instance Segmentation with a Learned Shape Prior
To date, most instance segmentation approaches are based on supervised learning that requires a considerable amount of annotated object contours as training ground truth. Here, we propose a framework that searches for th…
Cell SegmentationInstance SegmentationObjectSegmentation+2Pseudo-label Alignment for Semi-supervised Instance Segmentation
Pseudo-labeling is significant for semi-supervised instance segmentation, which generates instance masks and classes from unannotated images for subsequent training. However, in existing pipelines, pseudo-labels that con…
Instance SegmentationPseudo LabelSegmentationSemantic Segmentation+1Guided Distillation for Semi-Supervised Instance Segmentation
Although instance segmentation methods have improved considerably, the dominant paradigm is to rely on fully-annotated training images, which are tedious to obtain. To alleviate this reliance, and boost results, semi-sup…
Instance SegmentationSemantic SegmentationSemi-Supervised Instance SegmentationCyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation
Nuclei instance segmentation on histopathology images is of great clinical value for disease analysis. Generally, fully-supervised algorithms for this task require pixel-wise manual annotations, which is especially time-…
Instance SegmentationMulti-Task LearningSegmentationSemantic Segmentation+2The Devil is in the Points: Weakly Semi-Supervised Instance Segmentation via Point-Guided Mask Representation
In this paper, we introduce a novel learning scheme named weakly semi-supervised instance segmentation (WSSIS) with point labels for budget-efficient and high-performance instance segmentation. Namely, we consider a data…
Instance SegmentationSemantic SegmentationSemi-Supervised Instance SegmentationPolite Teacher: Semi-Supervised Instance Segmentation with Mutual Learning and Pseudo-Label Thresholding
We present Polite Teacher, a simple yet effective method for the task of semi-supervised instance segmentation. The proposed architecture relies on the Teacher-Student mutual learning framework. To filter out noisy pseud…
Instance SegmentationPseudo LabelSegmentationSemantic Segmentation+1Noisy Boundaries: Lemon or Lemonade for Semi-supervised Instance Segmentation?
Current instance segmentation methods rely heavily on pixel-level annotated images. The huge cost to obtain such fully-annotated images restricts the dataset scale and limits the performance. In this paper, we formally a…
Instance SegmentationSegmentationSemantic SegmentationSemi-Supervised Instance SegmentationTest-time Adaptation with Slot-Centric Models
Current visual detectors, though impressive within their training distribution, often fail to parse out-of-distribution scenes into their constituent entities. Recent test-time adaptation methods use auxiliary self-super…
image-classificationImage ClassificationImage SegmentationInductive Bias+7ContrastMask: Contrastive Learning to Segment Every Thing
Partially-supervised instance segmentation is a task which requests segmenting objects from novel unseen categories via learning on limited seen categories with annotated masks thus eliminating demands of heavy annotatio…
Instance SegmentationSegmentationSemi-Supervised Instance SegmentationMask-guided sample selection for Semi-Supervised Instance Segmentation
Image segmentation methods are usually trained with pixel-level annotations, which require significant human effort to collect. The most common solution to address this constraint is to implement weakly-supervised pipeli…
Active LearningImage SegmentationInstance SegmentationSegmentation+2Learning Saliency Propagation for Semi-Supervised Instance Segmentation
Instance segmentation is a challenging task for both modeling and annotation. Due to the high annotation cost, modeling becomes more difficult because of the limited amount of supervision. We aim to improve the accuracy …
Instance Segmentationobject-detectionObject DetectionSegmentation+2CenterMask : Real-Time Anchor-Free Instance Segmentation
We propose a simple yet efficient anchor-free instance segmentation, called CenterMask, that adds a novel spatial attention-guided mask (SAG-Mask) branch to anchor-free one stage object detector (FCOS) in the same vein w…
Instance SegmentationObject DetectionPanoptic SegmentationReal-time Instance Segmentation+4BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning
Datasets drive vision progress, yet existing driving datasets are impoverished in terms of visual content and supported tasks to study multitask learning for autonomous driving. Researchers are usually constrained to stu…
Autonomous DrivingDiversityDomain AdaptationDrivable Area Detection+8