463 citations · 565 across the 20 of their papers we have counts for
20 papers
Revisiting Domain-Adaptive 3D Object Detection by Reliable, Diverse and Class-balanced Pseudo-Labeling
Zhuoxiao Chen, Yadan Luo, Zheng Wang +2
Unsupervised domain adaptation (DA) with the aid of pseudo labeling techniques has emerged as a crucial approach for domain-adaptive 3D object detection. While effective, existing…
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration Error
Zixin Wang, Yadan Luo, Zhi Chen +2
The prevalence of domain adaptive semantic segmentation has prompted concerns regarding source domain data leakage, where private information from the source domain could inadverte…
Zero-Shot Learning by Harnessing Adversarial Samples
Zhi Chen, Pengfei Zhang, Jingjing Li +2
Zero-Shot Learning (ZSL) aims to recognize unseen classes by generalizing the knowledge, i.e., visual and semantic relationships, obtained from seen classes, where image augmentati…
KECOR: Kernel Coding Rate Maximization for Active 3D Object Detection
Yadan Luo, Zhuoxiao Chen, Zhen Fang +3
Achieving a reliable LiDAR-based object detector in autonomous driving is paramount, but its success hinges on obtaining large amounts of precise 3D annotations. Active learning (A…
Exploring Active 3D Object Detection from a Generalization Perspective
Yadan Luo, Zhuoxiao Chen, Zijian Wang +3
To alleviate the high annotation cost in LiDAR-based 3D object detection, active learning is a promising solution that learns to select only a small portion of unlabeled data to an…
Improved Feature Distillation via Projector Ensemble
Yudong Chen, Sen Wang, Jiajun Liu +3
In knowledge distillation, previous feature distillation methods mainly focus on the design of loss functions and the selection of the distilled layers, while the effect of the fea…
Federated Zero-Shot Learning for Visual Recognition
Zhi Chen, Yadan Luo, Sen Wang +2
Zero-shot learning is a learning regime that recognizes unseen classes by generalizing the visual-semantic relationship learned from the seen classes. To obtain an effective ZSL mo…
Discovering Domain Disentanglement for Generalized Multi-source Domain Adaptation
Zixin Wang, Yadan Luo, Peng-Fei Zhang +2
A typical multi-source domain adaptation (MSDA) approach aims to transfer knowledge learned from a set of labeled source domains, to an unlabeled target domain. Nevertheless, prior…
GSMFlow: Generation Shifts Mitigating Flow for Generalized Zero-Shot Learning
Zhi Chen, Yadan Luo, Sen Wang +2
Generalized Zero-Shot Learning (GZSL) aims to recognize images from both the seen and unseen classes by transferring semantic knowledge from seen to unseen classes. It is a promisi…
Deepfake Network Architecture Attribution
Tianyun Yang, Ziyao Huang, Juan Cao +2
With the rapid progress of generation technology, it has become necessary to attribute the origin of fake images. Existing works on fake image attribution perform multi-class class…
Source-Free Progressive Graph Learning for Open-Set Domain Adaptation
Yadan Luo, Zijian Wang, Zhuoxiao Chen +2
Open-set domain adaptation (OSDA) has gained considerable attention in many visual recognition tasks. However, most existing OSDA approaches are limited due to three main reasons,…
Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation
Ruihong Qiu, Zi Huang, Hongzhi Yin +1
Recent advancements of sequential deep learning models such as Transformer and BERT have significantly facilitated the sequential recommendation. However, according to our study, t…
Domain Adaptive Semantic Segmentation without Source Data
Fuming You, Jingjing Li, Lei Zhu +3
Domain adaptive semantic segmentation is recognized as a promising technique to alleviate the domain shift between the labeled source domain and the unlabeled target domain in many…
Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation
Ruihong Qiu, Zi Huang, Hongzhi Yin
The sequential recommendation aims to recommend items, such as products, songs and places, to users based on the sequential patterns of their historical records. Most existing sequ…
Learning to Diversify for Single Domain Generalization
Zijian Wang, Yadan Luo, Ruihong Qiu +2
Domain generalization (DG) aims to generalize a model trained on multiple source (i.e., training) domains to a distributionally different target (i.e., test) domain. In contrast to…
Exploiting Positional Information for Session-based Recommendation
Ruihong Qiu, Zi Huang, Tong Chen +1
For present e-commerce platforms, session-based recommender systems are developed to predict users' preference for next-item recommendation. Although a session can usually reflect…
Mitigating Generation Shifts for Generalized Zero-Shot Learning
Zhi Chen, Yadan Luo, Sen Wang +3
Generalized Zero-Shot Learning (GZSL) is the task of leveraging semantic information (e.g., attributes) to recognize the seen and unseen samples, where unseen classes are not obser…
Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning
Zhi Chen, Zi Huang, Jingjing Li +1
Compared to conventional zero-shot learning (ZSL) where recognising unseen classes is the primary or only aim, the goal of generalized zero-shot learning (GZSL) is to recognise bot…
Semantics Disentangling for Generalized Zero-Shot Learning
Zhi Chen, Yadan Luo, Ruihong Qiu +4
Generalized zero-shot learning (GZSL) aims to classify samples under the assumption that some classes are not observable during training. To bridge the gap between the seen and uns…
Interpretable Signed Link Prediction with Signed Infomax Hyperbolic Graph
Yadan Luo, Zi Huang, Hongxu Chen +2
Signed link prediction in social networks aims to reveal the underlying relationships (i.e. links) among users (i.e. nodes) given their existing positive and negative interactions…