138 citations · 380 across the 38 of their papers we have counts for
9 papers · 2 filters
Generalizable Single-Source Cross-modality Medical Image Segmentation via Invariant Causal Mechanisms
Boqi Chen, Yuanzhi Zhu, Yunke Ao +5
Single-source domain generalization (SDG) aims to learn a model from a single source domain that can generalize well on unseen target domains. This is an important task in computer…
Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation
Zhaochong An, Guolei Sun, Yun Liu +5
Few-shot 3D point cloud segmentation (FS-PCS) aims at generalizing models to segment novel categories with minimal annotated support samples. While existing FS-PCS methods have sho…
Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?
Kerem Cekmeceli, Meva Himmetoglu, Guney I. Tombak +3
Neural networks achieve state-of-the-art performance in many supervised learning tasks when the training data distribution matches the test data distribution. However, their perfor…
When SAM2 Meets Video Camouflaged Object Segmentation: A Comprehensive Evaluation and Adaptation
Yuli Zhou, Guolei Sun, Yawei Li +3
This study investigates the application and performance of the Segment Anything Model 2 (SAM2) in the challenging task of video camouflaged object segmentation (VCOS). VCOS involve…
Image Segmentation in Foundation Model Era: A Survey
Tianfei Zhou, Wang Xia, Fei Zhang +5
Image segmentation is a long-standing challenge in computer vision, studied continuously over several decades, as evidenced by seminal algorithms such as N-Cut, FCN, and MaskFormer…
ShapeSplat: A Large-scale Dataset of Gaussian Splats and Their Self-Supervised Pretraining
Qi Ma, Yue Li, Bin Ren +5
3D Gaussian Splatting (3DGS) has become the de facto method of 3D representation in many vision tasks. This calls for the 3D understanding directly in this representation space. To…