3 citations · 6 across the 6 of their papers we have counts for
6 papers
Catastrophic Overfitting: A Potential Blessing in Disguise
Mengnan Zhao, Lihe Zhang, Yuqiu Kong +1
Fast Adversarial Training (FAT) has gained increasing attention within the research community owing to its efficacy in improving adversarial robustness. Particularly noteworthy is…
Separable Multi-Concept Erasure from Diffusion Models
Mengnan Zhao, Lihe Zhang, Tianhang Zheng +2
Large-scale diffusion models, known for their impressive image generation capabilities, have raised concerns among researchers regarding social impacts, such as the imitation of co…
EipFormer: Emphasizing Instance Positions in 3D Instance Segmentation
Mengnan Zhao, Lihe Zhang, Yuqiu Kong +1
3D instance segmentation plays a crucial role in comprehending 3D scenes. Despite recent advancements in this field, existing approaches exhibit certain limitations. These methods…
Referring Image Segmentation Using Text Supervision
Fang Liu, Yuhao Liu, Yuqiu Kong +5
Existing Referring Image Segmentation (RIS) methods typically require expensive pixel-level or box-level annotations for supervision. In this paper, we observe that the referring t…
Fast Adversarial Training with Smooth Convergence
Mengnan Zhao, Lihe Zhang, Yuqiu Kong +1
Fast adversarial training (FAT) is beneficial for improving the adversarial robustness of neural networks. However, previous FAT work has encountered a significant issue known as c…
Temporal Knowledge Graph Reasoning Triggered by Memories
Mengnan Zhao, Lihe Zhang, Yuqiu Kong +1
Inferring missing facts in temporal knowledge graphs is a critical task and has been widely explored. Extrapolation in temporal reasoning tasks is more challenging and gradually at…