Search Results for author: Wenzhe Yin

Found 7 papers, 1 papers with code

Dynamic Prototype Adaptation with Distillation for Few-shot Point Cloud Segmentation

no code implementations29 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.

Point Cloud Segmentation Transfer Learning

Motion Flow Matching for Human Motion Synthesis and Editing

no code implementations14 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.

Motion Interpolation motion prediction +1

Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network

no code implementations9 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.

Few-Shot Semantic Segmentation

Principle of Relevant Information for Graph Sparsification

1 code implementation31 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.

Multi-Task Learning

Bilevel Continual Learning

no code implementations2 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.

Bilevel Optimization Continual Learning +1

Learning an Interpretable Graph Structure in Multi-Task Learning

no code implementations11 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.

Multi-Task Learning

Towards Interpretable Multi-Task Learning Using Bilevel Programming

no code implementations11 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.

Multi-Task Learning

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