47 citations · 85 across the 14 of their papers we have counts for
14 papers
Harnessing Lightweight Transformer with Contextual Synergic Enhancement for Efficient 3D Medical Image Segmentation
Xinyu Liu, Zhen Chen, Wuyang Li +2
Transformers have shown remarkable performance in 3D medical image segmentation, but their high computational requirements and need for large amounts of labeled data limit their ap…
X-GRM: Large Gaussian Reconstruction Model for Sparse-view X-rays to Computed Tomography
Yifan Liu, Wuyang Li, Weihao Yu +4
Computed Tomography serves as an indispensable tool in clinical workflows, providing non-invasive visualization of internal anatomical structures. Existing CT reconstruction works…
Bridge the Gap Between Visual and Linguistic Comprehension for Generalized Zero-shot Semantic Segmentation
Xiaoqing Guo, Wuyang Li, Yixuan Yuan
Generalized zero-shot semantic segmentation (GZS3) aims to achieve the human-level capability of segmenting not only seen classes but also novel class regions unseen in the trainin…
ConcealGS: Concealing Invisible Copyright Information in 3D Gaussian Splatting
Yifeng Yang, Hengyu Liu, Chenxin Li +6
With the rapid development of 3D reconstruction technology, the widespread distribution of 3D data has become a future trend. While traditional visual data (such as images and vide…
Universal Domain Adaptive Object Detection via Dual Probabilistic Alignment
Yuanfan Zheng, Jinlin Wu, Wuyang Li +1
Domain Adaptive Object Detection (DAOD) transfers knowledge from a labeled source domain to an unannotated target domain under closed-set assumption. Universal DAOD (UniDAOD) exten…
When 3D Partial Points Meets SAM: Tooth Point Cloud Segmentation with Sparse Labels
Yifan Liu, Wuyang Li, Cheng Wang +2
Tooth point cloud segmentation is a fundamental task in many orthodontic applications. Current research mainly focuses on fully supervised learning which demands expensive and tedi…
DiffRect: Latent Diffusion Label Rectification for Semi-supervised Medical Image Segmentation
Xinyu Liu, Wuyang Li, Yixuan Yuan
Semi-supervised medical image segmentation aims to leverage limited annotated data and rich unlabeled data to perform accurate segmentation. However, existing semi-supervised metho…
GaussianStego: A Generalizable Stenography Pipeline for Generative 3D Gaussians Splatting
Chenxin Li, Hengyu Liu, Zhiwen Fan +4
Recent advancements in large generative models and real-time neural rendering using point-based techniques pave the way for a future of widespread visual data distribution through…
LGS: A Light-weight 4D Gaussian Splatting for Efficient Surgical Scene Reconstruction
Hengyu Liu, Yifan Liu, Chenxin Li +2
The advent of 3D Gaussian Splatting (3D-GS) techniques and their dynamic scene modeling variants, 4D-GS, offers promising prospects for real-time rendering of dynamic surgical scen…
U-KAN Makes Strong Backbone for Medical Image Segmentation and Generation
Chenxin Li, Xinyu Liu, Wuyang Li +5
U-Net has become a cornerstone in various visual applications such as image segmentation and diffusion probability models. While numerous innovative designs and improvements have b…
Endora: Video Generation Models as Endoscopy Simulators
Chenxin Li, Hengyu Liu, Yifan Liu +6
Generative models hold promise for revolutionizing medical education, robot-assisted surgery, and data augmentation for machine learning. Despite progress in generating 2D medical…
Towards Robust Adaptive Object Detection under Noisy Annotations
Xinyu Liu, Wuyang Li, Qiushi Yang +2
Domain Adaptive Object Detection (DAOD) models a joint distribution of images and labels from an annotated source domain and learns a domain-invariant transformation to estimate th…
SIGMA: Semantic-complete Graph Matching for Domain Adaptive Object Detection
Wuyang Li, Xinyu Liu, Yixuan Yuan
Domain Adaptive Object Detection (DAOD) leverages a labeled domain to learn an object detector generalizing to a novel domain free of annotations. Recent advances align class-condi…
Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challenge
Sharib Ali, Noha Ghatwary, Debesh Jha +29
Polyps are well-known cancer precursors identified by colonoscopy. However, variability in their size, location, and surface largely affect identification, localisation, and charac…