1 citations · 1 across the 8 of their papers we have counts for
9 papers
From Pairs to Sequences: Track-Aware Policy Gradients for Keypoint Detection
Yepeng Liu, Hao Li, Liwen Yang +8
Keypoint-based matching is a fundamental component of modern 3D vision systems, such as Structure-from-Motion (SfM) and SLAM. Most existing learning-based methods are trained on im…
Consistent-Point: Consistent Pseudo-Points for Semi-Supervised Crowd Counting and Localization
Yuda Zou, Zelong Liu, Yuliang Gu +2
Crowd counting and localization are important in applications such as public security and traffic management. Existing methods have achieved impressive results thanks to extensive…
RefDrone: A Challenging Benchmark for Referring Expression Comprehension in Drone Scenes
Zhichao Sun, Yepeng Liu, Zhiling Su +7
Drones have become prevalent robotic platforms with diverse applications, showing significant potential in Embodied Artificial Intelligence (Embodied AI). Referring Expression Comp…
Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentation
Yuliang Gu, Weilun Tsao, Bo Du +2
Annotating 3D medical images demands substantial time and expertise, driving the adoption of semi-supervised learning (SSL) for segmentation tasks. However, the complex anatomical…
Shape Transformation Driven by Active Contour for Class-Imbalanced Semi-Supervised Medical Image Segmentation
Yuliang Gu, Yepeng Liu, Zhichao Sun +3
Annotating 3D medical images demands expert knowledge and is time-consuming. As a result, semi-supervised learning (SSL) approaches have gained significant interest in 3D medical i…
Progressive Retinal Image Registration via Global and Local Deformable Transformations
Yepeng Liu, Baosheng Yu, Tian Chen +4
Retinal image registration plays an important role in the ophthalmological diagnosis process. Since there exist variances in viewing angles and anatomical structures across differe…
Prompting Segment Anything Model with Domain-Adaptive Prototype for Generalizable Medical Image Segmentation
Zhikai Wei, Wenhui Dong, Peilin Zhou +3
Deep learning based methods often suffer from performance degradation caused by domain shift. In recent years, many sophisticated network structures have been designed to tackle th…
Position-Guided Prompt Learning for Anomaly Detection in Chest X-Rays
Zhichao Sun, Yuliang Gu, Yepeng Liu +3
Anomaly detection in chest X-rays is a critical task. Most methods mainly model the distribution of normal images, and then regard significant deviation from normal distribution as…
WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising
Haoyu Zhao, Yuliang Gu, Zhou Zhao +3
In clinical examinations and diagnoses, low-dose computed tomography (LDCT) is crucial for minimizing health risks compared with normal-dose computed tomography (NDCT). However, re…