109 citations · 567 across the 38 of their papers we have counts for
39 papers
Online Attentive Kernel-Based Temporal Difference Learning
Guang Yang, Xingguo Chen, Shangdong Yang +3
With rising uncertainty in the real world, online Reinforcement Learning (RL) has been receiving increasing attention due to its fast learning capability and improving data efficie…
PLACE dropout: A Progressive Layer-wise and Channel-wise Dropout for Domain Generalization
Jintao Guo, Lei Qi, Yinghuan Shi +1
Domain generalization (DG) aims to learn a generic model from multiple observed source domains that generalizes well to arbitrary unseen target domains without further training. Th…
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration
Lulu Zheng, Jiarui Chen, Jianhao Wang +6
Efficient exploration in deep cooperative multi-agent reinforcement learning (MARL) still remains challenging in complex coordination problems. In this paper, we introduce a novel…
Inconsistency-aware Uncertainty Estimation for Semi-supervised Medical Image Segmentation
Yinghuan Shi, Jian Zhang, Tong Ling +5
In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty. In this paper, we investigate a novel…
Better Pseudo-label: Joint Domain-aware Label and Dual-classifier for Semi-supervised Domain Generalization
Ruiqi Wang, Lei Qi, Yinghuan Shi +1
With the goal of directly generalizing trained model to unseen target domains, domain generalization (DG), a newly proposed learning paradigm, has attracted considerable attention.…
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…
Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions
Qian Yu, Lei Qi, Luping Zhou +5
Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges of various shapes, diverse sizes, and blurry boundaries. To address these…
Trip-ROMA: Self-Supervised Learning with Triplets and Random Mappings
Wenbin Li, Xuesong Yang, Meihao Kong +4
Contrastive self-supervised learning (SSL) methods, such as MoCo and SimCLR, have achieved great success in unsupervised visual representation learning. They rely on a large number…
ST++: Make Self-training Work Better for Semi-supervised Semantic Segmentation
Lihe Yang, Wei Zhuo, Lei Qi +2
Self-training via pseudo labeling is a conventional, simple, and popular pipeline to leverage unlabeled data. In this work, we first construct a strong baseline of self-training (n…
Feature-based Style Randomization for Domain Generalization
Yue Wang, Lei Qi, Yinghuan Shi +1
As a recent noticeable topic, domain generalization (DG) aims to first learn a generic model on multiple source domains and then directly generalize to an arbitrary unseen target d…
Mining Latent Classes for Few-shot Segmentation
Lihe Yang, Wei Zhuo, Lei Qi +2
Few-shot segmentation (FSS) aims to segment unseen classes given only a few annotated samples. Existing methods suffer the problem of feature undermining, i.e. potential novel clas…
CariMe: Unpaired Caricature Generation with Multiple Exaggerations
Zheng Gu, Chuanqi Dong, Jing Huo +2
Caricature generation aims to translate real photos into caricatures with artistic styles and shape exaggerations while maintaining the identity of the subject. Different from the…
Learning-based Computer-aided Prescription Model for Parkinson's Disease: A Data-driven Perspective
Yinghuan Shi, Wanqi Yang, Kim-Han Thung +5
In this paper, we study a novel problem: "automatic prescription recommendation for PD patients." To realize this goal, we first build a dataset by collecting 1) symptoms of PD pat…
Unsupervised Domain Attention Adaptation Network for Caricature Attribute Recognition
Wen Ji, Kelei He, Jing Huo +2
Caricature attributes provide distinctive facial features to help research in Psychology and Neuroscience. However, unlike the facial photo attribute datasets that have a quantity…
Manifold Alignment for Semantically Aligned Style Transfer
Jing Huo, Shiyin Jin, Wenbin Li +4
Most existing style transfer methods follow the assumption that styles can be represented with global statistics (e.g., Gram matrices or covariance matrices), and thus address the…
Crossover-Net: Leveraging the Vertical-Horizontal Crossover Relation for Robust Segmentation
Qian Yu, Yinghuan Shi, Yefeng Zheng +3
Robust segmentation for non-elongated tissues in medical images is hard to realize due to the large variation of the shape, size, and appearance of these tissues in different patie…
Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation
Tiexin Qin, Wenbin Li, Yinghuan Shi +1
Few-shot learning aims to learn a new concept when only a few training examples are available, which has been extensively explored in recent years. However, most of the current wor…
Generalizable Model-agnostic Semantic Segmentation via Target-specific Normalization
Jian Zhang, Lei Qi, Yinghuan Shi +1
Semantic segmentation in a supervised learning manner has achieved significant progress in recent years. However, its performance usually drops dramatically due to the data-distrib…
Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation
Tiexin Qin, Ziyuan Wang, Kelei He +3
Conventional data augmentation realized by performing simple pre-processing operations (\eg, rotation, crop, \etc) has been validated for its advantage in enhancing the performance…
Asymmetric Distribution Measure for Few-shot Learning
Wenbin Li, Lei Wang, Jing Huo +3
The core idea of metric-based few-shot image classification is to directly measure the relations between query images and support classes to learn transferable feature embeddings.…
MW-GAN: Multi-Warping GAN for Caricature Generation with Multi-Style Geometric Exaggeration
Haodi Hou, Jing Huo, Jing Wu +2
Given an input face photo, the goal of caricature generation is to produce stylized, exaggerated caricatures that share the same identity as the photo. It requires simultaneous sty…
Multi-Agent Game Abstraction via Graph Attention Neural Network
Yong Liu, Weixun Wang, Yujing Hu +3
In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process…
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.…
Defensive Few-shot Learning
Wenbin Li, Lei Wang, Xingxing Zhang +4
This paper investigates a new challenging problem called defensive few-shot learning in order to learn a robust few-shot model against adversarial attacks. Simply applying the exis…