24 citations · 45 across the 18 of their papers we have counts for
18 papers
Towards Training-Free Open-World Classification with 3D Generative Models
Xinzhe Xia, Weiguang Zhao, Yuyao Yan +4
3D open-world classification is a challenging yet essential task in dynamic and unstructured real-world scenarios, requiring both open-category and open-pose recognition. To addres…
Revisiting Mutual Information Maximization for Generalized Category Discovery
Zhaorui Tan, Chengrui Zhang, Xi Yang +2
Generalized category discovery presents a challenge in a realistic scenario, which requires the model's generalization ability to recognize unlabeled samples from known and unknown…
SCMix: Stochastic Compound Mixing for Open Compound Domain Adaptation in Semantic Segmentation
Kai Yao, Zhaorui Tan, Zixian Su +3
Open compound domain adaptation (OCDA) aims to transfer knowledge from a labeled source domain to a mix of unlabeled homogeneous compound target domains while generalizing to open…
Rethinking Multi-domain Generalization with A General Learning Objective
Zhaorui Tan, Xi Yang, Kaizhu Huang
Multi-domain generalization (mDG) is universally aimed to minimize the discrepancy between training and testing distributions to enhance marginal-to-label distribution mapping. How…
Semantic-aware Data Augmentation for Text-to-image Synthesis
Zhaorui Tan, Xi Yang, Kaizhu Huang
Data augmentation has been recently leveraged as an effective regularizer in various vision-language deep neural networks. However, in text-to-image synthesis (T2Isyn), current aug…
Unraveling Batch Normalization for Realistic Test-Time Adaptation
Zixian Su, Jingwei Guo, Kai Yao +3
While recent test-time adaptations exhibit efficacy by adjusting batch normalization to narrow domain disparities, their effectiveness diminishes with realistic mini-batches due to…
SaliencyCut: Augmenting Plausible Anomalies for Anomaly Detection
Jianan Ye, Yijie Hu, Xi Yang +3
Anomaly detection under open-set scenario is a challenging task that requires learning discriminative fine-grained features to detect anomalies that were even unseen during trainin…
Towards Deeper and Better Multi-view Feature Fusion for 3D Semantic Segmentation
Chaolong Yang, Yuyao Yan, Weiguang Zhao +4
3D point clouds are rich in geometric structure information, while 2D images contain important and continuous texture information. Combining 2D information to achieve better 3D sem…
Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image Segmentation
Zixian Su, Kai Yao, Xi Yang +3
Single-source domain generalization (SDG) in medical image segmentation is a challenging yet essential task as domain shifts are quite common among clinical image datasets. Previou…
SSD: Towards Better Text-Image Consistency Metric in Text-to-Image Generation
Zhaorui Tan, Xi Yang, Zihan Ye +4
Generating consistent and high-quality images from given texts is essential for visual-language understanding. Although impressive results have been achieved in generating high-qua…
EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables
Penglei Gao, Xi Yang, Rui Zhang +3
While exogenous variables have a major impact on performance improvement in time series analysis, inter-series correlation and time dependence among them are rarely considered in t…
Outpainting by Queries
Kai Yao, Penglei Gao, Xi Yang +3
Image outpainting, which is well studied with Convolution Neural Network (CNN) based framework, has recently drawn more attention in computer vision. However, CNNs rely on inherent…
Normalized Feature Distillation for Semantic Segmentation
Tao Liu, Xi Yang, Chenshu Chen
As a promising approach in model compression, knowledge distillation improves the performance of a compact model by transferring the knowledge from a cumbersome one. The kind of kn…
Divide and Conquer: 3D Point Cloud Instance Segmentation With Point-Wise Binarization
Weiguang Zhao, Yuyao Yan, Chaolong Yang +3
Instance segmentation on point clouds is crucially important for 3D scene understanding. Most SOTAs adopt distance clustering, which is typically effective but does not perform wel…
Mind The Gap: Alleviating Local Imbalance for Unsupervised Cross-Modality Medical Image Segmentation
Zixian Su, Kai Yao, Xi Yang +4
Unsupervised cross-modality medical image adaptation aims to alleviate the severe domain gap between different imaging modalities without using the target domain label. A key in th…
From 2D Images to 3D Model:Weakly Supervised Multi-View Face Reconstruction with Deep Fusion
Weiguang Zhao, Chaolong Yang, Jianan Ye +6
While weakly supervised multi-view face reconstruction (MVR) is garnering increased attention, one critical issue still remains open: how to effectively interact and fuse multiple…
Generalised Image Outpainting with U-Transformer
Penglei Gao, Xi Yang, Rui Zhang +4
In this paper, we develop a novel transformer-based generative adversarial neural network called U-Transformer for generalised image outpainting problem. Different from most presen…
Explainable Tensorized Neural Ordinary Differential Equations forArbitrary-step Time Series Prediction
Penglei Gao, Xi Yang, Rui Zhang +1
We propose a continuous neural network architecture, termed Explainable Tensorized Neural Ordinary Differential Equations (ETN-ODE), for multi-step time series prediction at arbitr…