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- Zhengzhou UniversityCN9 papers
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14 papers · 1 filter
On Success and Simplicity: A Second Look at Transferable Vision-Language Attack Pipeline
Yuchen Ren, Zhengyu Zhao, Chenhao Lin +2
Vision-Language Pre-training Models (VLPMs) are known to be vulnerable to adversarial attacks. Recent transferable attacks on VLPMs have followed a common pipeline with complicated…
MAPE: Defending Against Transferable Adversarial Attacks Using Multi-Source Adversarial Perturbations Elimination
Xinlei Liu, Jichao Xie, Tao Hu +4
Neural networks are vulnerable to meticulously crafted adversarial examples, leading to high-confidence misclassifications in image classification tasks. Due to their consistency w…
Sequential Difference Maximization: Generating Adversarial Examples via Multi-Stage Optimization
Xinlei Liu, Tao Hu, Peng Yi +3
Efficient adversarial attack methods are critical for assessing the robustness of computer vision models. In this paper, we reconstruct the optimization objective for generating ad…
BFA-YOLO: A balanced multiscale object detection network for building façade attachments detection
Yangguang Chen, Tong Wang, Guanzhou Chen +7
The detection of façade elements on buildings, such as doors, windows, balconies, air conditioning units, billboards, and glass curtain walls, is a critical step in automating the…
Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images
Lei Ding, Kun Zhu, Daifeng Peng +3
Vision Foundation Models (VFMs) such as the Segment Anything Model (SAM) allow zero-shot or interactive segmentation of visual contents, thus they are quickly applied in a variety…
Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images
Lei Ding, Jing Zhang, Kai Zhang +3
Semantic Change Detection (SCD) refers to the task of simultaneously extracting the changed areas and the semantic categories (before and after the changes) in Remote Sensing Image…