175 citations · 543 across the 42 of their papers we have counts for
27 papers · 1 filter
Towards Efficient and General-Purpose Few-Shot Misclassification Detection for Vision-Language Models
Fanhu Zeng, Zhen Cheng, Fei Zhu +1
Reliable prediction by classifiers is crucial for their deployment in high security and dynamically changing situations. However, modern neural networks often exhibit overconfidenc…
Happy: A Debiased Learning Framework for Continual Generalized Category Discovery
Shijie Ma, Fei Zhu, Zhun Zhong +3
Constantly discovering novel concepts is crucial in evolving environments. This paper explores the underexplored task of Continual Generalized Category Discovery (C-GCD), which aim…
Local-Prompt: Extensible Local Prompts for Few-Shot Out-of-Distribution Detection
Fanhu Zeng, Zhen Cheng, Fei Zhu +2
Out-of-Distribution (OOD) detection, aiming to distinguish outliers from known categories, has gained prominence in practical scenarios. Recently, the advent of vision-language mod…
Recoverable Compression: A Multimodal Vision Token Recovery Mechanism Guided by Text Information
Yi Chen, Jian Xu, Xu-Yao Zhang +3
With the advancement of large-scale language modeling techniques, large multimodal models combining visual encoders with large language models have demonstrated exceptional perform…
Adapting Vision-Language Models to Open Classes via Test-Time Prompt Tuning
Zhengqing Gao, Xiang Ao, Xu-Yao Zhang +1
Adapting pre-trained models to open classes is a challenging problem in machine learning. Vision-language models fully explore the knowledge of text modality, demonstrating strong…
StylePrompter: Enhancing Domain Generalization with Test-Time Style Priors
Jiao Zhang, Jian Xu, Xu-Yao Zhang +1
In real-world applications, the sample distribution at the inference stage often differs from the one at the training stage, causing performance degradation of trained deep models.…
PASS++: A Dual Bias Reduction Framework for Non-Exemplar Class-Incremental Learning
Fei Zhu, Xu-Yao Zhang, Zhen Cheng +1
Class-incremental learning (CIL) aims to recognize new classes incrementally while maintaining the discriminability of old classes. Most existing CIL methods are exemplar-based, i.…
Differentiable Proximal Graph Matching
Haoru Tan, Chuang Wang, Xu-Yao Zhang +1
Graph matching is a fundamental tool in computer vision and pattern recognition. In this paper, we introduce an algorithm for graph matching based on the proximal operator, referre…
Unified Entropy Optimization for Open-Set Test-Time Adaptation
Zhengqing Gao, Xu-Yao Zhang, Cheng-Lin Liu
Test-time adaptation (TTA) aims at adapting a model pre-trained on the labeled source domain to the unlabeled target domain. Existing methods usually focus on improving TTA perform…
Ensemble Quadratic Assignment Network for Graph Matching
Haoru Tan, Chuang Wang, Sitong Wu +3
Graph matching is a commonly used technique in computer vision and pattern recognition. Recent data-driven approaches have improved the graph matching accuracy remarkably, whereas…
Active Generalized Category Discovery
Shijie Ma, Fei Zhu, Zhun Zhong +2
Generalized Category Discovery (GCD) is a pragmatic and challenging open-world task, which endeavors to cluster unlabeled samples from both novel and old classes, leveraging some l…
Revisiting Confidence Estimation: Towards Reliable Failure Prediction
Fei Zhu, Xu-Yao Zhang, Zhen Cheng +1
Reliable confidence estimation is a challenging yet fundamental requirement in many risk-sensitive applications. However, modern deep neural networks are often overconfident for th…
PILoRA: Prototype Guided Incremental LoRA for Federated Class-Incremental Learning
Haiyang Guo, Fei Zhu, Wenzhuo Liu +2
Existing federated learning methods have effectively dealt with decentralized learning in scenarios involving data privacy and non-IID data. However, in real-world situations, each…
Unified Classification and Rejection: A One-versus-All Framework
Zhen Cheng, Xu-Yao Zhang, Cheng-Lin Liu
Classifying patterns of known classes and rejecting ambiguous and novel (also called as out-of-distribution (OOD)) inputs are involved in open world pattern recognition. Deep neura…
Towards Reliable Domain Generalization: A New Dataset and Evaluations
Jiao Zhang, Xu-Yao Zhang, Cheng-Lin Liu
There are ubiquitous distribution shifts in the real world. However, deep neural networks (DNNs) are easily biased towards the training set, which causes severe performance degrada…
Document Dewarping with Control Points
Guo-Wang Xie, Fei Yin, Xu-Yao Zhang +1
Document images are now widely captured by handheld devices such as mobile phones. The OCR performance on these images are largely affected due to geometric distortion of the docum…
Emergence of Machine Language: Towards Symbolic Intelligence with Neural Networks
Yuqi Wang, Xu-Yao Zhang, Cheng-Lin Liu +1
Representation is a core issue in artificial intelligence. Humans use discrete language to communicate and learn from each other, while machines use continuous features (like vecto…
Dewarping Document Image By Displacement Flow Estimation with Fully Convolutional Network
Guo-Wang Xie, Fei Yin, Xu-Yao Zhang +1
As camera-based documents are increasingly used, the rectification of distorted document images becomes a need to improve the recognition performance. In this paper, we propose a n…
Weakly-Supervised Arbitrary-Shaped Text Detection with Expectation-Maximization Algorithm
Mengbiao Zhao, Wei Feng, Fei Yin +2
Arbitrary-shaped text detection is an important and challenging task in computer vision. Most existing methods require heavy data labeling efforts to produce polygon-level text reg…
Towards Robust Pattern Recognition: A Review
Xu-Yao Zhang, Cheng-Lin Liu, Ching Y. Suen
The accuracies for many pattern recognition tasks have increased rapidly year by year, achieving or even outperforming human performance. From the perspective of accuracy, pattern…
SCAN: Sliding Convolutional Attention Network for Scene Text Recognition
Yi-Chao Wu, Fei Yin, Xu-Yao Zhang +2
Scene text recognition has drawn great attentions in the community of computer vision and artificial intelligence due to its challenges and wide applications. State-of-the-art recu…
Robust Classification with Convolutional Prototype Learning
Hong-Ming Yang, Xu-Yao Zhang, Fei Yin +1
Convolutional neural networks (CNNs) have been widely used for image classification. Despite its high accuracies, CNN has been shown to be easily fooled by some adversarial example…
Scene Text Recognition with Sliding Convolutional Character Models
Fei Yin, Yi-Chao Wu, Xu-Yao Zhang +1
Scene text recognition has attracted great interests from the computer vision and pattern recognition community in recent years. State-of-the-art methods use concolutional neural n…
Deep Direct Regression for Multi-Oriented Scene Text Detection
Wenhao He, Xu-Yao Zhang, Fei Yin +1
In this paper, we first provide a new perspective to divide existing high performance object detection methods into direct and indirect regressions. Direct regression performs boun…