1 code implementation • 15 Nov 2022 • Yue Guo, Joseph Campbell, Simon Stepputtis, Ruiyu Li, Dana Hughes, Fei Fang, Katia Sycara
This allows the student to self-reflect on what it has learned, enabling advice generalization and leading to improved sample efficiency and learning performance - even in environments where the teacher is sub-optimal.
Multi-agent Reinforcement Learning reinforcement-learning +2
1 code implementation • CVPR 2022 • Xufeng Yao, Yang Bai, Xinyun Zhang, Yuechen Zhang, Qi Sun, Ran Chen, Ruiyu Li, Bei Yu
Domain generalization refers to the problem of training a model from a collection of different source domains that can directly generalize to the unseen target domains.
Ranked #17 on Domain Generalization on PACS
1 code implementation • 9 Nov 2021 • Fatemeh Vahedian, Ruiyu Li, Puja Trivedi, Di Jin, Danai Koutra
Understanding the training dynamics of deep neural networks (DNNs) is important as it can lead to improved training efficiency and task performance.
3 code implementations • 4 Aug 2020 • Zhuotao Tian, Hengshuang Zhao, Michelle Shu, Zhicheng Yang, Ruiyu Li, Jiaya Jia
It consists of novel designs of (1) a training-free prior mask generation method that not only retains generalization power but also improves model performance and (2) Feature Enrichment Module (FEM) that overcomes spatial inconsistency by adaptively enriching query features with support features and prior masks.
Ranked #66 on Few-Shot Semantic Segmentation on COCO-20i (1-shot)
no code implementations • ECCV 2020 • Wanli Chen, Xinge Zhu, Ruoqi Sun, Junjun He, Ruiyu Li, Xiaoyong Shen, Bei Yu
Then we use these rank-1 tensors to recover the high-rank context features through our proposed tensor reconstruction module (TRM).
no code implementations • 18 Dec 2019 • Jihan Yang, Ruijia Xu, Ruiyu Li, Xiaojuan Qi, Xiaoyong Shen, Guanbin Li, Liang Lin
In contrast to adversarial alignment, we propose to explicitly train a domain-invariant classifier by generating and defensing against pointwise feature space adversarial perturbations.
no code implementations • 18 Oct 2019 • Xin Yao, Tianchi Huang, Rui-Xiao Zhang, Ruiyu Li, Lifeng Sun
Federated learning (FL) aims to train machine learning models in the decentralized system consisting of an enormous amount of smart edge devices.
no code implementations • ICCV 2019 • Lei Ke, Wenjie Pei, Ruiyu Li, Xiaoyong Shen, Yu-Wing Tai
State-of-the-art image captioning methods mostly focus on improving visual features, less attention has been paid to utilizing the inherent properties of language to boost captioning performance.
Ranked #4 on Image Captioning on MS COCO
no code implementations • 27 Jun 2019 • Zhuotao Tian, Hengshuang Zhao, Michelle Shu, Jiaze Wang, Ruiyu Li, Xiaoyong Shen, Jiaya Jia
Albeit intensively studied, false prediction and unclear boundaries are still major issues of salient object detection.
no code implementations • 13 Jun 2019 • Pengyuan Lyu, Zhicheng Yang, Xinhang Leng, Xiao-Jun Wu, Ruiyu Li, Xiaoyong Shen
Irregular scene text, which has complex layout in 2D space, is challenging to most previous scene text recognizers.
1 code implementation • CVPR 2018 • Ruiyu Li, Kaican Li, Yi-Chun Kuo, Michelle Shu, Xiaojuan Qi, Xiaoyong Shen, Jiaya Jia
We address the problem of image segmentation from natural language descriptions.
no code implementations • CVPR 2018 • Ying-Cong Chen, Huaijia Lin, Michelle Shu, Ruiyu Li, Xin Tao, Yangang Ye, Xiaoyong Shen, Jiaya Jia
Digital face manipulation has become a popular and fascinating way to touch images with the prevalence of smartphones and social networks.
1 code implementation • ICCV 2017 • Ruiyu Li, Makarand Tapaswi, Renjie Liao, Jiaya Jia, Raquel Urtasun, Sanja Fidler
We address the problem of recognizing situations in images.
Ranked #9 on Situation Recognition on imSitu
no code implementations • NeurIPS 2016 • Ruiyu Li, Jiaya Jia
Our method aims at reasoning over natural language questions and visual images.
no code implementations • ICCV 2015 • Renjie Liao, Xin Tao, Ruiyu Li, Ziyang Ma, Jiaya Jia
We propose a new direction for fast video super-resolution (VideoSR) via a SR draft ensemble, which is defined as the set of high-resolution patch candidates before final image deconvolution.