Search Results for author:
Found 13 papers, 7 papers with code
Few-Shot 3D Point Cloud Semantic Segmentation via Stratified Class-Specific Attention Based Transformer Network
While a few-shot learning method was proposed recently to address these two problems, it suffers from high computational complexity caused by graph construction and inability to learn fine-grained relationships among points due to the use of pooling operations.
Few-shot 3D Point Cloud Semantic Segmentation
Few-Shot Learning
+4
Parametric Surface Constrained Upsampler Network for Point Cloud
Designing a point cloud upsampler, which aims to generate a clean and dense point cloud given a sparse point representation, is a fundamental and challenging problem in computer vision.
Ranked #3 on
Point Cloud Completion
on ShapeNet
Cross-domain Few-shot Segmentation with Transductive Fine-tuning
Few-shot segmentation (FSS) expects models trained on base classes to work on novel classes with the help of a few support images.
Style-Guided Shadow Removal
To address this problem, we propose a style-guided shadow removal network (SG-ShadowNet) for better image-style consistency after shadow removal.
Ranked #6 on
Shadow Removal
on ISTD+
PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification
A special case is to annotate only one positive label in each training image.
CRFormer: A Cross-Region Transformer for Shadow Removal
Aiming to restore the original intensity of shadow regions in an image and make them compatible with the remaining non-shadow regions without a trace, shadow removal is a very challenging problem that benefits many downstream image/video-related tasks.
Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints
Discovering new materials is a challenging task in materials science crucial to the progress of human society.
Style Mixing and Patchwise Prototypical Matching for One-Shot Unsupervised Domain Adaptive Semantic Segmentation
In this paper, we tackle the problem of one-shot unsupervised domain adaptation (OSUDA) for semantic segmentation where the segmentors only see one unlabeled target image during training.
One-shot Unsupervised Domain Adaptation
Semantic Segmentation
+2
ATLANTIS: A Benchmark for Semantic Segmentation of Waterbody Images
We claim that ATLANTIS is the largest waterbody image dataset for semantic segmentation providing a wide range of water and water-related classes and it will benefit researchers of both computer vision and water resources engineering.
Ranked #1 on
Semantic Segmentation
on ATLANTIS
DANNet: A One-Stage Domain Adaptation Network for Unsupervised Nighttime Semantic Segmentation
We further design a re-weighting strategy to handle the inaccuracy caused by misalignment between day-night image pairs and wrong predictions of daytime images, as well as boost the prediction accuracy of small objects.
Ranked #6 on
Semantic Segmentation
on Nighttime Driving
From Shadow Generation to Shadow Removal
Shadow removal is a computer-vision task that aims to restore the image content in shadow regions.
Ranked #11 on
Shadow Removal
on ISTD+
Spatial Correspondence With Generative Adversarial Network: Learning Depth From Monocular Videos
Depth estimation from monocular videos has important applications in many areas such as autonomous driving and robot navigation.
Semantic Stereo Matching With Pyramid Cost Volumes
To further capture the details of disparity maps, in this paper, we propose a novel semantic stereo network named SSPCV-Net, which includes newly designed pyramid cost volumes for describing semantic and spatial information on multiple levels.
