Papers Weakly-supervised instance segmentation
“Weakly-supervised instance segmentation” 태그가 달린 논문 39편 · 필터 해제
WISH: Weakly Supervised Instance Segmentation using Heterogeneous Labels
Instance segmentation traditionally relies on dense pixel-level annotations, making it costly and labor-intensive. To alleviate this burden, weakly supervised instance segmentation utilizes cost-effective weak labels…
Instance SegmentationSegmentationSemantic SegmentationWeakly-supervised instance segmentationFine-grained Background Representation for Weakly Supervised Semantic Segmentation
Generating reliable pseudo masks from image-level labels is challenging in the weakly supervised semantic segmentation (WSSS) task due to the lack of spatial information. Prevalent class activation map (CAM)-based soluti…
Contrastive LearningInstance SegmentationSemantic SegmentationWeakly-supervised instance segmentation+2BAISeg: Boundary Assisted Weakly Supervised Instance Segmentation
How to extract instance-level masks without instance-level supervision is the main challenge of weakly supervised instance segmentation (WSIS). Popular WSIS methods estimate a displacement field (DF) via learning inter-p…
Boundary DetectionClusteringInstance SegmentationSegmentation+2Boosting Box-supervised Instance Segmentation with Pseudo Depth
The realm of Weakly Supervised Instance Segmentation (WSIS) under box supervision has garnered substantial attention, showcasing remarkable advancements in recent years. However, the limitations of box supervision become…
Box-supervised Instance SegmentationDepth EstimationDepth PredictionInstance Segmentation+3Complete Instances Mining for Weakly Supervised Instance Segmentation
Weakly supervised instance segmentation (WSIS) using only image-level labels is a challenging task due to the difficulty of aligning coarse annotations with the finer task. However, with the advancement of deep neural ne…
Instance SegmentationSegmentationSemantic SegmentationWeakly-supervised instance segmentationMWSIS: Multimodal Weakly Supervised Instance Segmentation with 2D Box Annotations for Autonomous Driving
Instance segmentation is a fundamental research in computer vision, especially in autonomous driving. However, manual mask annotation for instance segmentation is quite time-consuming and costly. To address this problem,…
3D Instance SegmentationAutonomous DrivingInstance SegmentationSegmentation+2Quantification of cardiac capillarization in single-immunostained myocardial slices using weakly supervised instance segmentation
Decreased myocardial capillary density has been reported as an important histopathological feature associated with various heart disorders. Quantitative assessment of cardiac capillarization typically involves double imm…
Instance SegmentationPrompt EngineeringSegmentationSemantic Segmentation+1PWISeg: Point-based Weakly-supervised Instance Segmentation for Surgical Instruments
In surgical procedures, correct instrument counting is essential. Instance segmentation is a location method that locates not only an object's bounding box but also each pixel's specific details. However, obtaining mask-…
Instance SegmentationSegmentationSemantic SegmentationSurgical tool detection+2Synthetic Instance Segmentation from Semantic Image Segmentation Masks
In recent years, instance segmentation has garnered significant attention across various applications. However, training a fully-supervised instance segmentation model requires costly both instance-level and pixel-level …
Image SegmentationInstance SegmentationSegmentationSemantic Segmentation+1ClickSeg: 3D Instance Segmentation with Click-Level Weak Annotations
3D instance segmentation methods often require fully-annotated dense labels for training, which are costly to obtain. In this paper, we present ClickSeg, a novel click-level weakly supervised 3D instance segmentation met…
3D Instance SegmentationClusteringInstance SegmentationSegmentation+2Weakly-Supervised Text Instance Segmentation
Text segmentation is a challenging vision task with many downstream applications. Current text segmentation methods require pixel-level annotations, which are expensive in the cost of human labor and limited in applicati…
Contrastive LearningInstance SegmentationSegmentationSemantic Segmentation+3SIM: Semantic-aware Instance Mask Generation for Box-Supervised Instance Segmentation
Weakly supervised instance segmentation using only bounding box annotations has recently attracted much research attention. Most of the current efforts leverage low-level image features as extra supervision without expli…
Box-supervised Instance SegmentationInstance SegmentationSegmentationSemantic Segmentation+1Class-incremental Continual Learning for Instance Segmentation with Image-level Weak Supervision
Instance segmentation requires labor-intensive manual labeling of the contours of complex objects in images for training. The labels can also be provided incrementally in practice to balance the human labor in differ…
Continual LearningIncremental LearningInstance SegmentationSegmentation+4EM-Paste: EM-guided Cut-Paste with DALL-E Augmentation for Image-level Weakly Supervised Instance Segmentation
We propose EM-PASTE: an Expectation Maximization(EM) guided Cut-Paste compositional dataset augmentation approach for weakly-supervised instance segmentation using only image-level supervision. The proposed method consis…
Instance SegmentationObjectRegion ProposalSegmentation+2LWSIS: LiDAR-guided Weakly Supervised Instance Segmentation for Autonomous Driving
Image instance segmentation is a fundamental research topic in autonomous driving, which is crucial for scene understanding and road safety. Advanced learning-based approaches often rely on the costly 2D mask annotations…
Autonomous DrivingInstance SegmentationScene UnderstandingSegmentation+3AsyInst: Asymmetric Affinity with DepthGrad and Color for Box-Supervised Instance Segmentation
The weakly supervised instance segmentation is a challenging task. The existing methods typically use bounding boxes as supervision and optimize the network with a regularization loss term such as pairwise color affinity…
Box-supervised Instance SegmentationInstance SegmentationSegmentationSemantic Segmentation+1BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation
Labeling objects with pixel-wise segmentation requires a huge amount of human labor compared to bounding boxes. Most existing methods for weakly supervised instance segmentation focus on designing heuristic losses with p…
Box-supervised Instance SegmentationInstance SegmentationSegmentationSemantic Segmentation+2Weakly Supervised Instance Segmentation using Motion Information via Optical Flow
Weakly supervised instance segmentation has gained popularity because it reduces high annotation cost of pixel-level masks required for model training. Recent approaches for weakly supervised instance segmentation detect…
Instance SegmentationOptical Flow EstimationSegmentationSemantic Segmentation+1Bounding Box Tightness Prior for Weakly Supervised Image Segmentation
This paper presents a weakly supervised image segmentation method that adopts tight bounding box annotations. It proposes generalized multiple instance learning (MIL) and smooth maximum approximation to integrate the bou…
Image SegmentationMultiple Instance LearningSemantic SegmentationWeakly-supervised instance segmentationBeyond Semantic to Instance Segmentation: Weakly-Supervised Instance Segmentation via Semantic Knowledge Transfer and Self-Refinement
Weakly-supervised instance segmentation (WSIS) has been considered as a more challenging task than weakly-supervised semantic segmentation (WSSS). Compared to WSSS, WSIS requires instance-wise localization, which is diff…
Image-level Supervised Instance SegmentationInstance SegmentationPoint-Supervised Instance SegmentationSegmentation+5