Weakly-supervised instance segmentation
19 papers with code • 3 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Weakly-supervised instance segmentation
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MS COCO
Latest papers
Complete Instances Mining for Weakly Supervised Instance Segmentation
ZechengLi19/CIM
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International Joint Conference on Artificial Intelligence 2023
To address this problem, we propose a novel approach for WSIS that focuses on the online refinement of complete instances through the use of MaskIoU heads to predict the integrity scores of proposals and a Complete Instances Mining (CIM) strategy to explicitly model the redundant segmentation problem and generate refined pseudo labels.
MWSIS: 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.
PWISeg: Point-based Weakly-supervised Instance Segmentation for Surgical Instruments
To address this issue, we propose a novel yet effective weakly-supervised surgical instrument instance segmentation approach, named Point-based Weakly-supervised Instance Segmentation (PWISeg).
Synthetic Instance Segmentation from Semantic Image Segmentation Masks
ssyc123/tfiseg
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SISeg does not require training a semantic or/and instance segmentation model and avoids the need for instance-level image annotations.
SIM: 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.
Class-incremental Continual Learning for Instance Segmentation with Image-level Weak Supervision
ai-application-and-integration-lab/cl4wsis
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ICCV 2023
To our knowledge, this is the first work on weakly-supervised continual learning for instance segmentation of images.
EM-Paste: EM-guided Cut-Paste with DALL-E Augmentation for Image-level Weakly Supervised Instance Segmentation
gyhandy/text2image-for-detection
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Finally, the third component creates a large-scale pseudo-labeled instance segmentation training dataset by compositing the foreground object masks onto the original and generated background images.
LWSIS: LiDAR-guided Weakly Supervised Instance Segmentation for Autonomous Driving
serenos/lwsis
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In this paper, we present a more artful framework, LiDAR-guided Weakly Supervised Instance Segmentation (LWSIS), which leverages the off-the-shelf 3D data, i. e., Point Cloud, together with the 3D boxes, as natural weak supervisions for training the 2D image instance segmentation models.
BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation
hustvl/boxteacher
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CVPR 2023
Most existing methods for weakly supervised instance segmentation focus on designing heuristic losses with priors from bounding boxes.
Bounding Box Tightness Prior for Weakly Supervised Image Segmentation
wangjuan313/wsis-boundingbox
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Two variants of smooth maximum approximation, i. e., $\alpha$-softmax function and $\alpha$-quasimax function, are exploited to conquer the numeral instability introduced by maximum function of bag prediction.
