1 citations · 2 across the 4 of their papers we have counts for
5 papers · 1 filter
Dynamic Prototype Adaptation with Distillation for Few-shot Point Cloud Segmentation
Jie Liu, Wenzhe Yin, Haochen Wang +3
Few-shot point cloud segmentation seeks to generate per-point masks for previously unseen categories, using only a minimal set of annotated point clouds as reference. Existing prot…
How to Train Neural Field Representations: A Comprehensive Study and Benchmark
Samuele Papa, Riccardo Valperga, David Knigge +4
Neural fields (NeFs) have recently emerged as a versatile method for modeling signals of various modalities, including images, shapes, and scenes. Subsequently, a number of works h…
Kandinsky Conformal Prediction: Efficient Calibration of Image Segmentation Algorithms
Joren Brunekreef, Eric Marcus, Ray Sheombarsing +2
Image segmentation algorithms can be understood as a collection of pixel classifiers, for which the outcomes of nearby pixels are correlated. Classifier models can be calibrated us…
Dynamic Prototype Convolution Network for Few-Shot Semantic Segmentation
Jie Liu, Yanqi Bao, Guo-Sen Xie +3
The key challenge for few-shot semantic segmentation (FSS) is how to tailor a desirable interaction among support and query features and/or their prototypes, under the episodic tra…
Subpixel object segmentation using wavelets and multi resolution analysis
Ray Sheombarsing, Nikita Moriakov, Jan-Jakob Sonke +1
We propose a novel deep learning framework for fast prediction of boundaries of two-dimensional simply connected domains using wavelets and Multi Resolution Analysis (MRA). The bou…