16 citations · 18 across the 5 of their papers we have counts for
6 papers
KVE-KD: Key Visual Evidence-Guided Knowledge Distillation for Vision-Language Models
Jianbin Zhang, Xin Sun, Shanwen Wang +2
Knowledge distillation is crucial for deploying vision-language models on resource-constrained devices. However, existing methods typically impose uniform supervision across visual…
Bridging the Gap between Labeled and Unlabeled Data via Unified Flow with Feature Memory Bank
Shanwen Wang, Xin Sun, Danfeng Hong +2
Although semi-supervised semantic segmentation () utilizes abundant unlabeled data to reduce manual labeling burdens, independent training of labeled and unlabeled data…
RSGPNet: Geometric Prompting for Remote Sensing Open-Vocabulary Semantic Segmentation
Shanwen Wang, Xin Sun, Sirui Wang +1
Open-vocabulary semantic segmentation (OVSS) enables text-guided segmentation of unseen objects, breaking fixed-class limitations to achieve open-world understanding. However, exis…
Vision-Language Model Purified Semi-Supervised Semantic Segmentation for Remote Sensing Images
Shanwen Wang, Xin Sun, Danfeng Hong +1
The semi-supervised semantic segmentation (S4) can learn rich visual knowledge from low-cost unlabeled images. However, traditional S4 architectures all face the challenge of low-q…
PRISM: Progressive Rain removal with Integrated State-space Modeling
Pengze Xue, Shanwen Wang, Fei Zhou +2
Image deraining is an essential vision technique that removes rain streaks and water droplets, enhancing clarity for critical vision tasks like autonomous driving. However, current…
Semi-supervised Semantic Segmentation for Remote Sensing Images via Multi-scale Uncertainty Consistency and Cross-Teacher-Student Attention
Shanwen Wang, Xin Sun, Changrui Chen +2
Semi-supervised learning offers an appealing solution for remote sensing (RS) image segmentation to relieve the burden of labor-intensive pixel-level labeling. However, RS images p…