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Found 6 papers, 3 papers with code
Few-shot Segmentation with Optimal Transport Matching and Message Flow
In this work, we argue that every support pixel's information is desired to be transferred to all query pixels and propose a Correspondence Matching Network (CMNet) with an Optimal Transport Matching module to mine out the correspondence between the query and support images.
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentation
To improve the generality of the objective activation maps, we propose a region prototypical network RPNet to explore the cross-image object diversity of the training set.
Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Clouds
While dense labeling on 3D data is expensive and time-consuming, only a few works address weakly supervised semantic point cloud segmentation methods to relieve the labeling cost by learning from simpler and cheaper labels.
Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection
In addition, the synthesized defect samples demonstrate their effectiveness in training better defect inspection networks.
Learning Regional Purity for Instance Segmentation on 3D Point Clouds
In this paper, we define a novel concept of “regional purity” as the percentage of neighboring points belonging to the same instance within a fixed-radius 3D space.
Ranked #15 on
3D Instance Segmentation
on ScanNet(v2)
Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds
To the best of our knowledge, this is the first method that uses cloud-level weak labels on raw 3D space to train a point cloud semantic segmentation network.
