no code implementations • 29 Jan 2024 • Jie Liu, Wenzhe Yin, Haochen Wang, Yunlu Chen, Jan-Jakob Sonke, Efstratios Gavves
Existing prototype-based methods rely on support prototypes to guide the segmentation of query point clouds, but they encounter challenges when significant object variations exist between the support prototypes and query features.
no code implementations • 14 Dec 2023 • Vincent Tao Hu, Wenzhe Yin, Pingchuan Ma, Yunlu Chen, Basura Fernando, Yuki M Asano, Efstratios Gavves, Pascal Mettes, Bjorn Ommer, Cees G. M. Snoek
In this paper, we propose \emph{Motion Flow Matching}, a novel generative model designed for human motion generation featuring efficient sampling and effectiveness in motion editing applications.
no code implementations • 9 Jan 2023 • Jie Liu, Yanqi Bao, Wenzhe Yin, Haochen Wang, Yang Gao, Jan-Jakob Sonke, Efstratios Gavves
However, the appearance variations between objects from the same category could be extremely large, leading to unreliable feature matching and query mask prediction.
Ranked #40 on Few-Shot Semantic Segmentation on PASCAL-5i (1-Shot)
1 code implementation • 31 May 2022 • Shujian Yu, Francesco Alesiani, Wenzhe Yin, Robert Jenssen, Jose C. Principe
Graph sparsification aims to reduce the number of edges of a graph while maintaining its structural properties.
no code implementations • 2 Nov 2020 • Ammar Shaker, Francesco Alesiani, Shujian Yu, Wenzhe Yin
This paper presents Bilevel Continual Learning (BiCL), a general framework for continual learning that fuses bilevel optimization and recent advances in meta-learning for deep neural networks.
no code implementations • 11 Sep 2020 • Shujian Yu, Francesco Alesiani, Ammar Shaker, Wenzhe Yin
We present a novel methodology to jointly perform multi-task learning and infer intrinsic relationship among tasks by an interpretable and sparse graph.
no code implementations • 11 Sep 2020 • Francesco Alesiani, Shujian Yu, Ammar Shaker, Wenzhe Yin
Interpretable Multi-Task Learning can be expressed as learning a sparse graph of the task relationship based on the prediction performance of the learned models.