activity
20222024
most citedOn the Robustness of Segment Anything

7 citations · 15 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV2024

CosalPure: Learning Concept from Group Images for Robust Co-Saliency Detection

Jiayi Zhu, Qing Guo, Felix Juefei-Xu +3

Co-salient object detection (CoSOD) aims to identify the common and salient (usually in the foreground) regions across a given group of images. Although achieving significant progr…

cs.CV2024

MIP: CLIP-based Image Reconstruction from PEFT Gradients

Peiheng Zhou, Ming Hu, Xiaofei Xie +3

Contrastive Language-Image Pre-training (CLIP) model, as an effective pre-trained multimodal neural network, has been widely used in distributed machine learning tasks, especially…

cs.CV20242 cited

Improving Robustness of LiDAR-Camera Fusion Model against Weather Corruption from Fusion Strategy Perspective

Yihao Huang, Kaiyuan Yu, Qing Guo +5

In recent years, LiDAR-camera fusion models have markedly advanced 3D object detection tasks in autonomous driving. However, their robustness against common weather corruption such…

cs.LG20231 cited

Towards Better Fairness-Utility Trade-off: A Comprehensive Measurement-Based Reinforcement Learning Framework

Simiao Zhang, Jitao Bai, Menghong Guan +4

Machine learning is widely used to make decisions with societal impact such as bank loan approving, criminal sentencing, and resume filtering. How to ensure its fairness while main…

cs.SE2023

FREPA: An Automated and Formal Approach to Requirement Modeling and Analysis in Aircraft Control Domain

Jincao Feng, Weikai Miao, Hanyue Zheng +8

Formal methods are promising for modeling and analyzing system requirements. However, applying formal methods to large-scale industrial projects is a remaining challenge. The indus…

cs.CV20237 cited

On the Robustness of Segment Anything

Yihao Huang, Yue Cao, Tianlin Li +5

Segment anything model (SAM) has presented impressive objectness identification capability with the idea of prompt learning and a new collected large-scale dataset. Given a prompt…