7 citations · 8 across the 10 of their papers we have counts for
12 papers
RAP-WAM: Learning World-Action Models through Predictive Rehearsal and Action Perturbations
Yuanpeng He, Ceyao Zhang, Fangjing Li +8
Recent world action models draw on video generation priors to jointly predict future observations and robot actions. Adapting these priors to control requires determining how futur…
BAT3R: Robust Online 3D Reconstruction with Bayesian Adaptive State Updates
Lijian Li, Yuanpeng He, Yudian Zheng +6
Feed-forward transformer models have driven remarkable progress in 3D vision,yet their quadratic complexity renders them impractical for long video sequences. Streaming 3D reconstr…
Co-Evidential Fusion with Information Volume for Medical Image Segmentation
Yuanpeng He, Lijian Li, Tianxiang Zhan +3
Although existing semi-supervised image segmentation methods have achieved good performance, they cannot effectively utilize multiple sources of voxel-level uncertainty for targete…
Efficient Prototype Consistency Learning in Medical Image Segmentation via Joint Uncertainty and Data Augmentation
Lijian Li, Yuanpeng He, Chi-Man Pun
Recently, prototype learning has emerged in semi-supervised medical image segmentation and achieved remarkable performance. However, the scarcity of labeled data limits the express…
Mutual Evidential Deep Learning for Medical Image Segmentation
Yuanpeng He, Yali Bi, Lijian Li +3
Existing semi-supervised medical segmentation co-learning frameworks have realized that model performance can be diminished by the biases in model recognition caused by low-quality…
Multi-Prototype Embedding Refinement for Semi-Supervised Medical Image Segmentation
Yali Bi, Enyu Che, Yinan Chen +2
Medical image segmentation aims to identify anatomical structures at the voxel-level. Segmentation accuracy relies on distinguishing voxel differences. Compared to advancements ach…
Residual Feature-Reutilization Inception Network for Image Classification
Yuanpeng He, Wenjie Song, Lijian Li +2
Capturing feature information effectively is of great importance in the field of computer vision. With the development of convolutional neural networks (CNNs), concepts like residu…
Generalized Uncertainty-Based Evidential Fusion with Hybrid Multi-Head Attention for Weak-Supervised Temporal Action Localization
Yuanpeng He, Lijian Li, Tianxiang Zhan +2
Weakly supervised temporal action localization (WS-TAL) is a task of targeting at localizing complete action instances and categorizing them with video-level labels. Action-backgro…
Towards Realistic Long-tailed Semi-supervised Learning in an Open World
Yuanpeng He, Lijian Li
Open-world long-tailed semi-supervised learning (OLSSL) has increasingly attracted attention. However, existing OLSSL algorithms generally assume that the distributions between kno…
Time Evidence Fusion Network: Multi-source View in Long-Term Time Series Forecasting
Tianxiang Zhan, Yuanpeng He, Yong Deng +3
In practical scenarios, time series forecasting necessitates not only accuracy but also efficiency. Consequently, the exploration of model architectures remains a perennially trend…
Uncertainty-aware Evidential Fusion-based Learning for Semi-supervised Medical Image Segmentation
Yuanpeng He, Lijian Li
Although the existing uncertainty-based semi-supervised medical segmentation methods have achieved excellent performance, they usually only consider a single uncertainty evaluation…
EPL: Evidential Prototype Learning for Semi-supervised Medical Image Segmentation
Yuanpeng He
Although current semi-supervised medical segmentation methods can achieve decent performance, they are still affected by the uncertainty in unlabeled data and model predictions, an…