2 citations · 7 across the 13 of their papers we have counts for
13 papers
Scene Graph Thinking: Reinforcing Structured Visual Reasoning for Multimodal Large Language Models
Zhiwei Yang, Yuanchen Wu, Nan Zhang +3
Multimodal Large Language Models (MLLMs) have demonstrated strong perception and reasoning capabilities. However, most existing models focus on isolated objects and neglect structu…
DiCLIP: Diffusion Model Enhances CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation
Zhiwei Yang, Pengfei Song, Yucong Meng +3
Weakly Supervised Semantic Segmentation (WSSS) with image-level labels typically leverages Class Activation Maps (CAMs) to achieve pixel-level predictions. Recently, Contrastive La…
Exploring CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation
Zhiwei Yang, Yucong Meng, Kexue Fu +3
Weakly Supervised Semantic Segmentation (WSSS) with image-level labels aims to achieve pixel-level predictions using Class Activation Maps (CAMs). Recently, Contrastive Language-Im…
MoRe: Class Patch Attention Needs Regularization for Weakly Supervised Semantic Segmentation
Zhiwei Yang, Yucong Meng, Kexue Fu +2
Weakly Supervised Semantic Segmentation (WSSS) with image-level labels typically uses Class Activation Maps (CAM) to achieve dense predictions. Recently, Vision Transformer (ViT) h…
DH-Mamba: Exploring Dual-domain Hierarchical State Space Models for MRI Reconstruction
Yucong Meng, Zhiwei Yang, Zhijian Song +1
The accelerated MRI reconstruction poses a challenging ill-posed inverse problem due to the significant undersampling in k-space. Deep neural networks, such as CNNs and ViTs, have…
Boosting ViT-based MRI Reconstruction from the Perspectives of Frequency Modulation, Spatial Purification, and Scale Diversification
Yucong Meng, Zhiwei Yang, Yonghong Shi +1
The accelerated MRI reconstruction process presents a challenging ill-posed inverse problem due to the extensive under-sampling in k-space. Recently, Vision Transformers (ViTs) hav…
Continuous K-space Recovery Network with Image Guidance for Fast MRI Reconstruction
Yucong Meng, Zhiwei Yang, Minghong Duan +2
Magnetic resonance imaging (MRI) is a crucial tool for clinical diagnosis while facing the challenge of long scanning time. To reduce the acquisition time, fast MRI reconstruction…
Tackling Ambiguity from Perspective of Uncertainty Inference and Affinity Diversification for Weakly Supervised Semantic Segmentation
Zhiwei Yang, Yucong Meng, Kexue Fu +2
Weakly supervised semantic segmentation (WSSS) with image-level labels intends to achieve dense tasks without laborious annotations. However, due to the ambiguous contexts and fuzz…
Separate and Conquer: Decoupling Co-occurrence via Decomposition and Representation for Weakly Supervised Semantic Segmentation
Zhiwei Yang, Kexue Fu, Minghong Duan +3
Weakly supervised semantic segmentation (WSSS) with image-level labels aims to achieve segmentation tasks without dense annotations. However, attributed to the frequent coupling of…
An efficient dual-branch framework via implicit self-texture enhancement for arbitrary-scale histopathology image super-resolution
Minghong Duan, Linhao Qu, Zhiwei Yang +3
High-quality whole-slide scanning is expensive, complex, and time-consuming, thus limiting the acquisition and utilization of high-resolution histopathology images in daily clinica…
Knowledge Extraction and Distillation from Large-Scale Image-Text Colonoscopy Records Leveraging Large Language and Vision Models
Shuo Wang, Yan Zhu, Xiaoyuan Luo +8
The development of artificial intelligence systems for colonoscopy analysis often necessitates expert-annotated image datasets. However, limitations in dataset size and diversity i…
OpenAL: An Efficient Deep Active Learning Framework for Open-Set Pathology Image Classification
Linhao Qu, Yingfan Ma, Zhiwei Yang +2
Active learning (AL) is an effective approach to select the most informative samples to label so as to reduce the annotation cost. Existing AL methods typically work under the clos…
Towards Arbitrary-scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework based on Implicit Self-texture Enhancement
Linhao Qu, Minghong Duan, Zhiwei Yang +2
Existing super-resolution models for pathology images can only work in fixed integer magnifications and have limited performance. Though implicit neural network-based methods have…