Jintao Guo, Na Wang, Lei Qi, Yinghuan Shi
Domain generalization (DG) aims to learn a model that generalizes well to unseen target domains utilizing multiple source domains without re-training. Most existing DG works are based on convolutional neural networks (CNNs). However, the local operation of the convolution kernel makes the model focu...
Jintao Guo, Lei Qi, Yinghuan Shi
Deep Neural Networks have exhibited considerable success in various visual tasks. However, when applied to unseen test datasets, state-of-the-art models often suffer performance degradation due to domain shifts. In this paper, we introduce a novel approach for domain generalization from a novel pers...
Ziqi Zhou, Lei Qi, Yinghuan Shi
Jian Zhang, Lei Qi, Yinghuan Shi, Yang Gao
To deal with the domain shift between training and test samples, current methods have primarily focused on learning generalizable features during training and ignore the specificity of unseen samples that are also critical during the test. In this paper, we investigate a more challenging task that a...
Zekun Li, Lei Qi, Yinghuan Shi, Yang Gao
Semi-supervised learning (SSL) aims to leverage massive unlabeled data when labels are expensive to obtain. Unfortunately, in many real-world applications, the collected unlabeled data will inevitably contain unseen-class outliers not belonging to any of the labeled classes. To deal with the challen...
Zihan Cheng, Jintao Guo, Jian Zhang, Lei Qi et al.
To segment medical images with distribution shifts, domain generalization (DG) has emerged as a promising setting to train models on source domains that can generalize to unseen target domains. Existing DG methods are mainly based on CNN or ViT architectures. Recently, advanced state space models, r...
Guan Gui, Zhen Zhao, Lei Qi, Luping Zhou et al.
In semi-supervised learning, unlabeled samples can be utilized through augmentation and consistency regularization. However, we observed certain samples, even undergoing strong augmentation, are still correctly classified with high confidence, resulting in a loss close to zero. It indicates that the...
Lei Qi, Lei Wang, Jing Huo, Yinghuan Shi et al.
In person re-identification (Re-ID), supervised methods usually need a large amount of expensive label information, while unsupervised ones are still unable to deliver satisfactory identification performance. In this paper, we introduce a novel person Re-ID task called unsupervised cross-camera pers...
Jintao Guo, Lei Qi, Yinghuan Shi, Yang Gao
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. The major challenge in DG is that the model inevitably faces a severe overfitting issue due to the domain gap between sour...
Qian Yu, Lei Qi, Luping Zhou, Lei Wang et al.
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 difficulties, square kernel-based encoder-decoder architecture has been proposed and widely used, but its performance re...
Yang Liu, Kun Gao, DENG Hongbin -, Tong Ling et al.
Abstract Background: Organ dysfunction (OD) assessment is essential in intensive care units (ICUs). However, no OD scoring system has so far considered the duration of OD, which is clinically relevant. This study aimed to develop and validate an ICU mortality prediction model based on the Sequential...