no code implementations • ECCV 2020 • Shuo Wang, Jun Yue, Jianzhuang Liu, Qi Tian, Meng Wang
It is a challenging problem since (1) the identifying process is susceptible to over-fitting with limited samples of an object, and (2) the sample imbalance between a base (known knowledge) category and a novel category is easy to bias the recognition results.
no code implementations • 13 Apr 2024 • Yidan Liu, Jun Yue, Shaobo Xia, Pedram Ghamisi, Weiying Xie, Leyuan Fang
As a newly emerging advance in deep generative models, diffusion models have achieved state-of-the-art results in many fields, including computer vision, natural language processing, and molecule design.
no code implementations • 11 Dec 2023 • Shaobo Xia, Jun Yue, Kacper Kania, Leyuan Fang, Andrea Tagliasacchi, Kwang Moo Yi, Weiwei Sun
We propose a weakly supervised semantic segmentation method for point clouds that predicts "per-point" labels from just "whole-scene" annotations while achieving the performance of recent fully supervised approaches.
1 code implementation • 12 Apr 2023 • Ning Chen, Jun Yue, Leyuan Fang, Shaobo Xia
The framework consists of a spectral-spatial diffusion module, and an attention-based classification module.
no code implementations • 19 Jan 2023 • Jun Yue, Leyuan Fang, Shaobo Xia, Yue Deng, Jiayi Ma
In specific, instead of converting multi-channel images into single-channel data in existing fusion methods, we create the multi-channel data distribution with a denoising network in a latent space with forward and reverse diffusion process.
no code implementations • 18 Apr 2022 • Jun Yue, Leyuan Fang, Pedram Ghamisi, Weiying Xie, Jun Li, Jocelyn Chanussot, Antonio J Plaza
Therefore, remote sensing image understanding often faces the problems of incomplete, inexact, and inaccurate supervised information, which will affect the breadth and depth of remote sensing applications.
1 code implementation • 8 Aug 2021 • Ruohao Guo, Liao Qu, Dantong Niu, Zhenbo Li, Jun Yue
In this work, we present the LeafMask neural network, a new end-to-end model to delineate each leaf region and count the number of leaves, with two main components: 1) the mask assembly module merging position-sensitive bases of each predicted box after non-maximum suppression (NMS) and corresponding coefficients to generate original masks; 2) the mask refining module elaborating leaf boundaries from the mask assembly module by the point selection strategy and predictor.
Ranked #1 on Instance Segmentation on Leaf Segmentation Challenge
no code implementations • 9 Jan 2016 • Qingjun Wang, Haiyan Lv, Jun Yue, Eugene Mitchell
We define an intact vector for each data point, and a view-conditional transformation matrix for each view, and propose to reconstruct the multiple view feature vectors by the product of the corresponding intact vectors and transformation matrices.