1 citations · 1 across the 4 of their papers we have counts for
5 papers
COMPrompter: reconceptualized segment anything model with multiprompt network for camouflaged object detection
Xiaoqin Zhang, Zhenni Yu, Li Zhao +2
We rethink the segment anything model (SAM) and propose a novel multiprompt network called COMPrompter for camouflaged object detection (COD). SAM has zero-shot generalization abil…
Latent Semantic Consensus For Deterministic Geometric Model Fitting
Guobao Xiao, Jun Yu, Jiayi Ma +2
Estimating reliable geometric model parameters from the data with severe outliers is a fundamental and important task in computer vision. This paper attempts to sample high-quality…
Vanishing-Point-Guided Video Semantic Segmentation of Driving Scenes
Diandian Guo, Deng-Ping Fan, Tongyu Lu +2
The estimation of implicit cross-frame correspondences and the high computational cost have long been major challenges in video semantic segmentation (VSS) for driving scenes. Prio…
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…
Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data
Yu-Cheng Chou, Bowen Li, Deng-Ping Fan +2
Creating large-scale and well-annotated datasets to train AI algorithms is crucial for automated tumor detection and localization. However, with limited resources, it is challengin…