75 citations · 109 across the 9 of their papers we have counts for
9 papers
Seeking Consensus: Geometric-Semantic On-the-Fly Recalibration for Open-Vocabulary Remote Sensing Semantic Segmentation
Guanchun Wang, Chenxiao Wu, Xiangrong Zhang +4
Open-vocabulary semantic segmentation (OVSS) in remote sensing images is a promising task that employs textual descriptions for identifying undefined land cover categories. Despite…
ACMamba: Fast Unsupervised Anomaly Detection via An Asymmetrical Consensus State Space Model
Guanchun Wang, Xiangrong Zhang, Yifei Zhang +4
Unsupervised anomaly detection in hyperspectral images (HSI), aiming to detect unknown targets from backgrounds, is challenging for earth surface monitoring. However, current studi…
SMamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification
Guanchun Wang, Xiangrong Zhang, Zelin Peng +2
Land cover analysis using hyperspectral images (HSI) remains an open problem due to their low spatial resolution and complex spectral information. Recent studies are primarily dedi…
Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model
Zelin Peng, Zhengqin Xu, Zhilin Zeng +3
Parameter-efficient fine-tuning (PEFT) is an effective methodology to unleash the potential of large foundation models in novel scenarios with limited training data. In the compute…
SAM-PARSER: Fine-tuning SAM Efficiently by Parameter Space Reconstruction
Zelin Peng, Zhengqin Xu, Zhilin Zeng +2
Segment Anything Model (SAM) has received remarkable attention as it offers a powerful and versatile solution for object segmentation in images. However, fine-tuning SAM for downst…
USAGE: A Unified Seed Area Generation Paradigm for Weakly Supervised Semantic Segmentation
Zelin Peng, Guanchun Wang, Lingxi Xie +3
Seed area generation is usually the starting point of weakly supervised semantic segmentation (WSSS). Computing the Class Activation Map (CAM) from a multi-label classification net…
A Survey on Label-efficient Deep Image Segmentation: Bridging the Gap between Weak Supervision and Dense Prediction
Wei Shen, Zelin Peng, Xuehui Wang +6
The rapid development of deep learning has made a great progress in image segmentation, one of the fundamental tasks of computer vision. However, the current segmentation algorithm…
Absolute Wrong Makes Better: Boosting Weakly Supervised Object Detection via Negative Deterministic Information
Guanchun Wang, Xiangrong Zhang, Zelin Peng +3
Weakly supervised object detection (WSOD) is a challenging task, in which image-level labels (e.g., categories of the instances in the whole image) are used to train an object dete…
Adaptive Affinity Loss and Erroneous Pseudo-Label Refinement for Weakly Supervised Semantic Segmentation
Xiangrong Zhang, Zelin Peng, Peng Zhu +4
Semantic segmentation has been continuously investigated in the last ten years, and majority of the established technologies are based on supervised models. In recent years, image-…