123 citations · 232 across the 10 of their papers we have counts for
7 papers · 1 filter
CM-MaskSD: Cross-Modality Masked Self-Distillation for Referring Image Segmentation
Wenxuan Wang, Jing Liu, Xingjian He +5
Referring image segmentation (RIS) is a fundamental vision-language task that intends to segment a desired object from an image based on a given natural language expression. Due to…
FreMIM: Fourier Transform Meets Masked Image Modeling for Medical Image Segmentation
Wenxuan Wang, Jing Wang, Chen Chen +4
The research community has witnessed the powerful potential of self-supervised Masked Image Modeling (MIM), which enables the models capable of learning visual representation from…
MF2-MVQA: A Multi-stage Feature Fusion method for Medical Visual Question Answering
Shanshan Song, Jiangyun Li, Jing Wang +2
There is a key problem in the medical visual question answering task that how to effectively realize the feature fusion of language and medical images with limited datasets. In ord…
Positive-Negative Equal Contrastive Loss for Semantic Segmentation
Jing Wang, Jiangyun Li, Wei Li +3
The contextual information is critical for various computer vision tasks, previous works commonly design plug-and-play modules and structural losses to effectively extract and aggr…
Attention guided global enhancement and local refinement network for semantic segmentation
Jiangyun Li, Sen Zha, Chen Chen +3
The encoder-decoder architecture is widely used as a lightweight semantic segmentation network. However, it struggles with a limited performance compared to a well-designed Dilated…
TransBTS: Multimodal Brain Tumor Segmentation Using Transformer
Wenxuan Wang, Chen Chen, Meng Ding +3
Transformer, which can benefit from global (long-range) information modeling using self-attention mechanisms, has been successful in natural language processing and 2D image classi…
3D Dilated Multi-Fiber Network for Real-time Brain Tumor Segmentation in MRI
Chen Chen, Xiaopeng Liu, Meng Ding +2
Brain tumor segmentation plays a pivotal role in medical image processing. In this work, we aim to segment brain MRI volumes. 3D convolution neural networks (CNN) such as 3D U-Net…