Search Results for author:
Found 20 papers, 6 papers with code
DreamDA: Generative Data Augmentation with Diffusion Models
The acquisition of large-scale, high-quality data is a resource-intensive and time-consuming endeavor.
Progressive Conservative Adaptation for Evolving Target Domains
Moreover, as adjusting to the most recent target domain can interfere with the features learned from previous target domains, we develop a conservative sparse attention mechanism.
Activate and Reject: Towards Safe Domain Generalization under Category Shift
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 domains and object classes often occur.
Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision
Considering the privacy-preservation issues and security concerns, in this work, we study a practical problem of Source-Free Domain Adaptation (SFDA), which eliminates the reliance on annotated source data.
WaveDM: Wavelet-Based Diffusion Models for Image Restoration
Latest diffusion-based methods for many image restoration tasks outperform traditional models, but they encounter the long-time inference problem.
PCRLv2: A Unified Visual Information Preservation Framework for Self-supervised Pre-training in Medical Image Analysis
Recent advances in self-supervised learning (SSL) in computer vision are primarily comparative, whose goal is to preserve invariant and discriminative semantics in latent representations by comparing siamese image views.
Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization
Domain generalization (DG) enables generalizing a learning machine from multiple seen source domains to an unseen target one.
A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective
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 network analysis and recommender systems), computer vision (\emph{e. g.,} object detection and point cloud learning), and natural language processing (\emph{e. g.,} relation extraction and sequence learning), to name a few.
Towards Higher-order Topological Consistency for Unsupervised Network Alignment
Network alignment task, which aims to identify corresponding nodes in different networks, is of great significance for many subsequent applications.
Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis
On the LIDC-IDRI benchmark dataset for benign-malignant classification of pulmonary nodules in CT images, our method achieves a new state-of-the-art accuracy of 95. 36\% and an AUC of 96. 54\%.
Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection
However, both the global and local alignment approaches fail to capture the topological relations among different foreground objects as the explicit dependencies and interactions between and within domains are neglected.
Compound Domain Generalization via Meta-Knowledge Encoding
Such holistic semantic structure, referred to as meta-knowledge here, is crucial for learning generalizable representations.
Act Like a Radiologist: Towards Reliable Multi-view Correspondence Reasoning for Mammogram Mass Detection
In this paper, we propose an Anatomy-aware Graph convolutional Network (AGN), which is tailored for mammogram mass detection and endows existing detection methods with multi-view reasoning ability.
I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors
Motivated by this, we propose an Implicit Instance-Invariant Network (I3Net), which is tailored for adapting one-stage detectors and implicitly learns instance-invariant features via exploiting the natural characteristics of deep features in different layers.
Dual Bipartite Graph Learning: A General Approach for Domain Adaptive Object Detection
Domain Adaptive Object Detection (DAOD) relieves the reliance on large-scale annotated data by transferring the knowledge learned from a labeled source domain to a new unlabeled target domain.
Hard Class Rectification for Domain Adaptation
Second, we further consider that the predictions of target samples belonging to the hard class are vulnerable to perturbations.
Semi-supervised Domain Adaptation
Unsupervised Domain Adaptation
Harmonizing Transferability and Discriminability for Adapting Object Detectors
Recent advances in adaptive object detection have achieved compelling results in virtue of adversarial feature adaptation to mitigate the distributional shifts along the detection pipeline.
Multi-sequence Cardiac MR Segmentation with Adversarial Domain Adaptation Network
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 suffering from myocardial infarction (MI).
Unsupervised Adversarial Graph Alignment with Graph Embedding
In this paper, we propose an Unsupervised Adversarial Graph Alignment (UAGA) framework to learn a cross-graph alignment between two embedding spaces of different graphs in a fully unsupervised fashion (\emph{i. e.,} no existing anchor links and no users' personal profile or attribute information is available).
Progressive Feature Alignment for Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a fully-unlabeled target domain.
Ranked #7 on
Domain Adaptation
on SVHN-to-MNIST
