most citedOn the Robustness of Segment Anything

7 citations · 14 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV20231 cited

Open Compound Domain Adaptation with Object Style Compensation for Semantic Segmentation

Tingliang Feng, Hao Shi, Xueyang Liu +4

Many methods of semantic image segmentation have borrowed the success of open compound domain adaptation. They minimize the style gap between the images of source and target domain…

cs.CV20234 cited

BEVControl: Accurately Controlling Street-view Elements with Multi-perspective Consistency via BEV Sketch Layout

Kairui Yang, Enhui Ma, Jibin Peng +3

Using synthesized images to boost the performance of perception models is a long-standing research challenge in computer vision. It becomes more eminent in visual-centric autonomou…

cs.CV20232 cited

Surface Geometry Processing: An Efficient Normal-based Detail Representation

Wuyuan Xie, Miaohui Wang, Di Lin +2

With the rapid development of high-resolution 3D vision applications, the traditional way of manipulating surface detail requires considerable memory and computing time. To address…

cs.CV2023

CVSformer: Cross-View Synthesis Transformer for Semantic Scene Completion

Haotian Dong, Enhui Ma, Lubo Wang +7

Semantic scene completion (SSC) requires an accurate understanding of the geometric and semantic relationships between the objects in the 3D scene for reasoning the occluded object…

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…