45 citations · 157 across the 24 of their papers we have counts for
24 papers
Identifying Latent Concepts and Structures for Generalized Category Discovery
Boyang Dai, Chaoqi Chen, Yizhou Yu
Generalized Category Discovery (GCD) aims to recognize known classes while autonomously discovering novel ones in open-world settings. However, current approaches primarily focus o…
Mitigating Simplicity Bias in OOD Detection through Object Co-occurrence Analysis
Boyang Dai, Chaoqi Chen, Yizhou Yu
Out-of-distribution (OOD) detection is crucial for ensuring the reliability of deep learning models. Existing methods mostly focus on regular entangled representations to discrimin…
Back to Source: Open-Set Continual Test-Time Adaptation via Domain Compensation
Yingkai Yang, Chaoqi Chen, Hui Huang
Test-Time Adaptation (TTA) aims to mitigate distributional shifts between training and test domains during inference time. However, existing TTA methods fall short in the realistic…
LaMamba-Diff: Linear-Time High-Fidelity Diffusion Models Based on Local Attention and Mamba
Yunxiang Fu, Chaoqi Chen, Yizhou Yu
Recent Transformer-based diffusion models have shown remarkable performance, largely attributed to the ability of the self-attention mechanism to accurately capture both global and…
Bootstrap Segmentation Foundation Model under Distribution Shift via Object-Centric Learning
Luyao Tang, Yuxuan Yuan, Chaoqi Chen +3
Foundation models have made incredible strides in achieving zero-shot or few-shot generalization, leveraging prompt engineering to mimic the problem-solving approach of human intel…
Mixstyle-Entropy: Domain Generalization with Causal Intervention and Perturbation
Luyao Tang, Yuxuan Yuan, Chaoqi Chen +2
Despite the considerable advancements achieved by deep neural networks, their performance tends to degenerate when the test environment diverges from the training ones. Domain gene…
DreamDA: Generative Data Augmentation with Diffusion Models
Yunxiang Fu, Chaoqi Chen, Yu Qiao +1
The acquisition of large-scale, high-quality data is a resource-intensive and time-consuming endeavor. Compared to conventional Data Augmentation (DA) techniques (e.g. cropping and…
Progressive Conservative Adaptation for Evolving Target Domains
Gangming Zhao, Chaoqi Chen, Wenhao He +5
Conventional domain adaptation typically transfers knowledge from a source domain to a stationary target domain. However, in many real-world cases, target data usually emerge seque…
Activate and Reject: Towards Safe Domain Generalization under Category Shift
Chaoqi Chen, Luyao Tang, Leitian Tao +4
Albeit the notable performance on in-domain test points, it is non-trivial for deep neural networks to attain satisfactory accuracy when deploying in the open world, where novel do…
Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision
Jiexiang Wang, Chaoqi Chen
Unsupervised Domain Adaptation~(UDA) has attracted a surge of interest over the past decade but is difficult to be used in real-world applications. Considering the privacy-preserva…
PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis
Hong-Yu Zhou, Chixiang Lu, Chaoqi Chen +2
Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent represent…
Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization
Chaoqi Chen, Luyao Tang, Feng Liu +3
Domain generalization (DG) enables generalizing a learning machine from multiple seen source domains to an unseen target one. The general objective of DG methods is to learn semant…
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective
Chaoqi Chen, Yushuang Wu, Qiyuan Dai +5
Graph Neural Networks (GNNs) have gained momentum in graph representation learning and boosted the state of the art in a variety of areas, such as data mining (\emph{e.g.,} social…
Towards Higher-order Topological Consistency for Unsupervised Network Alignment
Qingqiang Sun, Xuemin Lin, Ying Zhang +2
Network alignment task, which aims to identify corresponding nodes in different networks, is of great significance for many subsequent applications. Without the need for labeled an…
Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis
Gangming Zhao, Quanlong Feng, Chaoqi Chen +2
During clinical practice, radiologists often use attributes, e.g. morphological and appearance characteristics of a lesion, to aid disease diagnosis. Effectively modeling attribute…
Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection
Chaoqi Chen, Jiongcheng Li, Hong-Yu Zhou +4
Domain Adaptive Object Detection (DAOD) focuses on improving the generalization ability of object detectors via knowledge transfer. Recent advances in DAOD strive to change the emp…
Compound Domain Generalization via Meta-Knowledge Encoding
Chaoqi Chen, Jiongcheng Li, Xiaoguang Han +2
Domain generalization (DG) aims to improve the generalization performance for an unseen target domain by using the knowledge of multiple seen source domains. Mainstream DG methods…
Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection
Yuhang Liu, Fandong Zhang, Chaoqi Chen +3
Mammogram mass detection is crucial for diagnosing and preventing the breast cancers in clinical practice. The complementary effect of multi-view mammogram images provides valuable…
I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors
Chaoqi Chen, Zebiao Zheng, Yue Huang +2
Recent works on two-stage cross-domain detection have widely explored the local feature patterns to achieve more accurate adaptation results. These methods heavily rely on the regi…
Hard Class Rectification for Domain Adaptation
Yunlong Zhang, Changxing Jing, Huangxing Lin +4
Domain adaptation (DA) aims to transfer knowledge from a label-rich and related domain (source domain) to a label-scare domain (target domain). Pseudo-labeling has recently been wi…
Harmonizing Transferability and Discriminability for Adapting Object Detectors
Chaoqi Chen, Zebiao Zheng, Xinghao Ding +2
Recent advances in adaptive object detection have achieved compelling results in virtue of adversarial feature adaptation to mitigate the distributional shifts along the detection…
Multi-sequence Cardiac MR Segmentation with Adversarial Domain Adaptation Network
Jiexiang Wang, Hongyu Huang, Chaoqi Chen +3
Automatic and accurate segmentation of the ventricles and myocardium from multi-sequence cardiac MRI (CMR) is crucial for the diagnosis and treatment management for patients suffer…
Unsupervised Adversarial Graph Alignment with Graph Embedding
Chaoqi Chen, Weiping Xie, Tingyang Xu +5
Graph alignment, also known as network alignment, is a fundamental task in social network analysis. Many recent works have relied on partially labeled cross-graph node corresponden…
Progressive Feature Alignment for Unsupervised Domain Adaptation
Chaoqi Chen, Weiping Xie, Wenbing Huang +5
Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a fully-unlabeled target domain. To tackle this task, recent approaches resort to discri…