19 citations · 43 across the 11 of their papers we have counts for
11 papers
LCReg: Long-Tailed Image Classification with Latent Categories based Recognition
Weide Liu, Zhonghua Wu, Yiming Wang +4
In this work, we tackle the challenging problem of long-tailed image recognition. Previous long-tailed recognition approaches mainly focus on data augmentation or re-balancing stra…
ELFNet: Evidential Local-global Fusion for Stereo Matching
Jieming Lou, Weide Liu, Zhuo Chen +2
Although existing stereo matching models have achieved continuous improvement, they often face issues related to trustworthiness due to the absence of uncertainty estimation. Addit…
Integrating Large Pre-trained Models into Multimodal Named Entity Recognition with Evidential Fusion
Weide Liu, Xiaoyang Zhong, Jingwen Hou +3
Multimodal Named Entity Recognition (MNER) is a crucial task for information extraction from social media platforms such as Twitter. Most current methods rely on attention weights…
Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation
Weide Liu, Zhonghua Wu, Yang Zhao +4
Current methods for few-shot segmentation (FSSeg) have mainly focused on improving the performance of novel classes while neglecting the performance of base classes. To overcome th…
CRCNet: Few-shot Segmentation with Cross-Reference and Region-Global Conditional Networks
Weide Liu, Chi Zhang, Guosheng Lin +1
Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images. In this paper, we propose a Cross-Reference and L…
Distilling Knowledge from Object Classification to Aesthetics Assessment
Jingwen Hou, Henghui Ding, Weisi Lin +2
In this work, we point out that the major dilemma of image aesthetics assessment (IAA) comes from the abstract nature of aesthetic labels. That is, a vast variety of distinct conte…
Long-tailed Recognition by Learning from Latent Categories
Weide Liu, Zhonghua Wu, Yiming Wang +4
In this work, we address the challenging task of long-tailed image recognition. Previous long-tailed recognition methods commonly focus on the data augmentation or re-balancing str…
Few-shot Segmentation with Optimal Transport Matching and Message Flow
Weide Liu, Chi Zhang, Henghui Ding +2
We tackle the challenging task of few-shot segmentation in this work. It is essential for few-shot semantic segmentation to fully utilize the support information. Previous methods…
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised Segmentation
Weide Liu, Xiangfei Kong, Tzu-Yi Hung +1
Weakly supervised image segmentation trained with image-level labels usually suffers from inaccurate coverage of object areas during the generation of the pseudo groundtruth. This…
Few-Shot Segmentation with Global and Local Contrastive Learning
Weide Liu, Zhonghua Wu, Henghui Ding +4
In this work, we address the challenging task of few-shot segmentation. Previous few-shot segmentation methods mainly employ the information of support images as guidance for query…
CRNet: Cross-Reference Networks for Few-Shot Segmentation
Weide Liu, Chi Zhang, Guosheng Lin +1
Over the past few years, state-of-the-art image segmentation algorithms are based on deep convolutional neural networks. To render a deep network with the ability to understand a c…