7 citations · 10 across the 4 of their papers we have counts for
4 papers
Source-Free Test-Time Adaptation For Online Surface-Defect Detection
Yiran Song, Qianyu Zhou, Lizhuang Ma
Surface defect detection is significant in industrial production. However, detecting defects with varying textures and anomaly classes during the test time is challenging. This ari…
SU-SAM: A Simple Unified Framework for Adapting Segment Anything Model in Underperformed Scenes
Yiran Song, Qianyu Zhou, Xuequan Lu +2
Segment anything model (SAM) has demonstrated excellent generalizability in common vision scenarios, yet falling short of the ability to understand specialized data. Recently, seve…
BA-SAM: Scalable Bias-Mode Attention Mask for Segment Anything Model
Yiran Song, Qianyu Zhou, Xiangtai Li +3
In this paper, we address the challenge of image resolution variation for the Segment Anything Model (SAM). SAM, known for its zero-shot generalizability, exhibits a performance de…
Rethinking Implicit Neural Representations for Vision Learners
Yiran Song, Qianyu Zhou, Lizhuang Ma
Implicit Neural Representations (INRs) are powerful to parameterize continuous signals in computer vision. However, almost all INRs methods are limited to low-level tasks, e.g., im…