Search Results for author: Weide Liu

Found 16 papers, 4 papers with code

Splitting vs. Merging: Mining Object Regions with Discrepancy and Intersection Loss for Weakly Supervised Semantic Segmentation

no code implementations ECCV 2020 Tianyi Zhang, Guosheng Lin, Weide Liu, Jianfei Cai, Alex Kot

Finally, by training the segmentation model with the masks generated by our Splitting vs Merging strategy, we achieve the state-of-the-art weakly-supervised segmentation results on the Pascal VOC 2012 benchmark.

Segmentation Weakly supervised segmentation +2

Enhancing Incomplete Multi-modal Brain Tumor Segmentation with Intra-modal Asymmetry and Inter-modal Dependency

no code implementations14 Jun 2024 Weide Liu, Jingwen Hou, Xiaoyang Zhong, Huijing Zhan, Jun Cheng, Yuming Fang, Guanghui Yue

Secondly, we propose a post-training stage that enables the model to reconstruct missing modalities in the prediction results when only partial modalities are available.

Anatomy Brain Tumor Segmentation +3

Multimodal Sentiment Analysis with Missing Modality: A Knowledge-Transfer Approach

no code implementations28 Dec 2023 Weide Liu, Huijing Zhan, Hao Chen, Fengmao Lv

Multimodal sentiment analysis aims to identify the emotions expressed by individuals through visual, language, and acoustic cues.

Multimodal Sentiment Analysis Transfer Learning

ELFNet: Evidential Local-global Fusion for Stereo Matching

1 code implementation ICCV 2023 Jieming Lou, Weide Liu, Zhuo Chen, Fayao Liu, Jun Cheng

Although existing stereo matching models have achieved continuous improvement, they often face issues related to trustworthiness due to the absence of uncertainty estimation.

Domain Generalization Stereo Matching

CRCNet: Few-shot Segmentation with Cross-Reference and Region-Global Conditional Networks

no code implementations23 Aug 2022 Weide Liu, Chi Zhang, Guosheng Lin, Fayao Liu

Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images.

Segmentation

Long-tailed Recognition by Learning from Latent Categories

no code implementations2 Jun 2022 Weide Liu, Zhonghua Wu, Yiming Wang, Henghui Ding, Fayao Liu, Jie Lin, Guosheng Lin

Previous long-tailed recognition methods commonly focus on the data augmentation or re-balancing strategy of the tail classes to give more attention to tail classes during the model training.

Data Augmentation Diversity +1

Distilling Knowledge from Object Classification to Aesthetics Assessment

no code implementations2 Jun 2022 Jingwen Hou, Henghui Ding, Weisi Lin, Weide Liu, Yuming Fang

To deal with this dilemma, we propose to distill knowledge on semantic patterns for a vast variety of image contents from multiple pre-trained object classification (POC) models to an IAA model.

Classification Object

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

Few-Shot Segmentation with Global and Local Contrastive Learning

1 code implementation11 Aug 2021 Weide Liu, Zhonghua Wu, Henghui Ding, Fayao Liu, Jie Lin, Guosheng Lin

To this end, we first propose a prior extractor to learn the query information from the unlabeled images with our proposed global-local contrastive learning.

Contrastive Learning Image Segmentation +2

Cannot find the paper you are looking for? You can Submit a new open access paper.