13 citations · 20 across the 5 of their papers we have counts for
6 papers
ContextFusion and Bootstrap: An Effective Approach to Improve Slot Attention-Based Object-Centric Learning
Pinzhuo Tian, Shengjie Yang, Hang Yu +1
A key human ability is to decompose a scene into distinct objects and use their relationships to understand the environment. Object-centric learning aims to mimic this process in a…
DeCo: Task Decomposition and Skill Composition for Zero-Shot Generalization in Long-Horizon 3D Manipulation
Zixuan Chen, Junhui Yin, Yangtao Chen +6
Generalizing language-conditioned multi-task imitation learning (IL) models to novel long-horizon 3D manipulation tasks is challenging. To address this, we propose DeCo (Task Decom…
GravMAD: Grounded Spatial Value Maps Guided Action Diffusion for Generalized 3D Manipulation
Yangtao Chen, Zixuan Chen, Junhui Yin +4
Robots' ability to follow language instructions and execute diverse 3D manipulation tasks is vital in robot learning. Traditional imitation learning-based methods perform well on s…
LibFewShot: A Comprehensive Library for Few-shot Learning
Wenbin Li, Ziyi, Wang +9
Few-shot learning, especially few-shot image classification, has received increasing attention and witnessed significant advances in recent years. Some recent studies implicitly sh…
Improving the Generalization of Meta-learning on Unseen Domains via Adversarial Shift
Pinzhuo Tian, Yao Gao
Meta-learning provides a promising way for learning to efficiently learn and achieves great success in many applications. However, most meta-learning literature focuses on dealing…
Differentiable Meta-learning Model for Few-shot Semantic Segmentation
Pinzhuo Tian, Zhangkai Wu, Lei Qi +3
To address the annotation scarcity issue in some cases of semantic segmentation, there have been a few attempts to develop the segmentation model in the few-shot learning paradigm.…