Semi-Supervised Instance Segmentation
11 papers with code • 4 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Semi-Supervised Instance Segmentation
BDD100K
COCO 10% labeled data
Most implemented papers
CenterMask : Real-Time Anchor-Free Instance Segmentation
youngwanLEE/CenterMask
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arXiv 2019
We hope that CenterMask and VoVNetV2 can serve as a solid baseline of real-time instance segmentation and backbone network for various vision tasks, respectively.
BDD100K: 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.
Learning Saliency Propagation for Semi-Supervised Instance Segmentation
ucbdrive/ShapeProp
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CVPR 2020
Due to the high annotation cost, modeling becomes more difficult because of the limited amount of supervision.
ContrastMask: Contrastive Learning to Segment Every Thing
huiserwang/ContrastMask
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CVPR 2022
In this framework, annotated masks of seen categories and pseudo masks of unseen categories serve as a prior for contrastive learning, where features from the mask regions (foreground) are pulled together, and are contrasted against those from the background, and vice versa.
Test-time Adaptation with Slot-Centric Models
In our work, we find evidence that these losses are insufficient for the task of scene decomposition, without also considering architectural inductive biases.
Noisy Boundaries: Lemon or Lemonade for Semi-supervised Instance Segmentation?
zhenyuw16/noisyboundaries
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CVPR 2022
We construct a framework for semi-supervised instance segmentation by assigning pixel-level pseudo labels.
The Devil is in the Points: Weakly Semi-Supervised Instance Segmentation via Point-Guided Mask Representation
clovaai/pointwssis
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CVPR 2023
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.
Cyclic Learning: Bridging Image-level Labels and Nuclei Instance Segmentation
Experiments on three datasets demonstrate the good generality of our method, which outperforms other image-level weakly supervised methods for nuclei instance segmentation, and achieves comparable performance to fully-supervised methods.
Guided Distillation for Semi-Supervised Instance Segmentation
facebookresearch/guideddistillation
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Although instance segmentation methods have improved considerably, the dominant paradigm is to rely on fully-annotated training images, which are tedious to obtain.
Pseudo-label Alignment for Semi-supervised Instance Segmentation
Through extensive experiments conducted on the COCO and Cityscapes datasets, we demonstrate that PAIS is a promising framework for semi-supervised instance segmentation, particularly in cases where labeled data is severely limited.
