Search Results for author: Tzu-Yi Hung

Found 6 papers, 3 papers with code

Few-shot Segmentation with Optimal Transport Matching and Message Flow

no code implementations19 Aug 2021 Weide Liu, Chi Zhang, Henghui Ding, Tzu-Yi Hung, Guosheng Lin

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.

Few-Shot Semantic Segmentation Multi-Task Learning +2

Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentation

1 code implementation17 Aug 2021 Weide Liu, Xiangfei Kong, Tzu-Yi Hung, Guosheng Lin

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.

Diversity Image Segmentation +3

Dense Supervision Propagation for Weakly Supervised Semantic Segmentation on 3D Point Clouds

no code implementations23 Jul 2021 Jiacheng Wei, Guosheng Lin, Kim-Hui Yap, Fayao Liu, Tzu-Yi Hung

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.

Point Cloud Segmentation Scene Understanding +3

Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection

no code implementations28 Mar 2021 Gongjie Zhang, Kaiwen Cui, Tzu-Yi Hung, Shijian Lu

In addition, the synthesized defect samples demonstrate their effectiveness in training better defect inspection networks.

Diversity Vocal Bursts Intensity Prediction

Multi-Path Region Mining For Weakly Supervised 3D Semantic Segmentation on Point Clouds

1 code implementation CVPR 2020 Jiacheng Wei, Guosheng Lin, Kim-Hui Yap, Tzu-Yi Hung, Lihua Xie

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.

3D Semantic Segmentation Point Cloud Segmentation +2

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