19 citations · 57 across the 20 of their papers we have counts for
20 papers
LLaVA-c: Continual Improved Visual Instruction Tuning
Wenzhuo Liu, Fei Zhu, Haiyang Guo +2
Multimodal models like LLaVA-1.5 achieve state-of-the-art visual understanding through visual instruction tuning on multitask datasets, enabling strong instruction-following and mu…
ProtoGCD: Unified and Unbiased Prototype Learning for Generalized Category Discovery
Shijie Ma, Fei Zhu, Xu-Yao Zhang +1
Generalized category discovery (GCD) is a pragmatic but underexplored problem, which requires models to automatically cluster and discover novel categories by leveraging the labele…
DESIRE: Dynamic Knowledge Consolidation for Rehearsal-Free Continual Learning
Haiyang Guo, Fei Zhu, Fanhu Zeng +2
Continual learning aims to equip models with the ability to retain previously learned knowledge like a human. Recent work incorporating Parameter-Efficient Fine-Tuning has revitali…
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…
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.…
MSPE: Multi-Scale Patch Embedding Prompts Vision Transformers to Any Resolution
Wenzhuo Liu, Fei Zhu, Shijie Ma +1
Although Vision Transformers (ViTs) have recently advanced computer vision tasks significantly, an important real-world problem was overlooked: adapting to variable input resolutio…
Multi-scale Unified Network for Image Classification
Wenzhuo Liu, Fei Zhu, Cheng-Lin Liu
Convolutional Neural Networks (CNNs) have advanced significantly in visual representation learning and recognition. However, they face notable challenges in performance and computa…
Towards Non-Exemplar Semi-Supervised Class-Incremental Learning
Wenzhuo Liu, Fei Zhu, Cheng-Lin Liu
Deep neural networks perform remarkably well in close-world scenarios. However, novel classes emerged continually in real applications, making it necessary to learn incrementally.…
Branch-Tuning: Balancing Stability and Plasticity for Continual Self-Supervised Learning
Wenzhuo Liu, Fei Zhu, Cheng-Lin Liu
Self-supervised learning (SSL) has emerged as an effective paradigm for deriving general representations from vast amounts of unlabeled data. However, as real-world applications co…
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…
Open-world machine learning: A review and new outlooks
Fei Zhu, Shijie Ma, Zhen Cheng +4
Machine learning has achieved remarkable success in many applications. However, existing studies are largely based on the closed-world assumption, which assumes that the environmen…
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…
Class Incremental Learning with Self-Supervised Pre-Training and Prototype Learning
Wenzhuo Liu, Xinjian Wu, Fei Zhu +3
Deep Neural Network (DNN) has achieved great success on datasets of closed class set. However, new classes, like new categories of social media topics, are continuously added to th…
Towards Trustworthy Dataset Distillation
Shijie Ma, Fei Zhu, Zhen Cheng +1
Efficiency and trustworthiness are two eternal pursuits when applying deep learning in real-world applications. With regard to efficiency, dataset distillation (DD) endeavors to re…
OpenMix: Exploring Outlier Samples for Misclassification Detection
Fei Zhu, Zhen Cheng, Xu-Yao Zhang +1
Reliable confidence estimation for deep neural classifiers is a challenging yet fundamental requirement in high-stakes applications. Unfortunately, modern deep neural networks are…
Rethinking Confidence Calibration for Failure Prediction
Fei Zhu, Zhen Cheng, Xu-Yao Zhang +1
Reliable confidence estimation for the predictions is important in many safety-critical applications. However, modern deep neural networks are often overconfident for their incorre…
Average of Pruning: Improving Performance and Stability of Out-of-Distribution Detection
Zhen Cheng, Fei Zhu, Xu-Yao Zhang +1
Detecting Out-of-distribution (OOD) inputs have been a critical issue for neural networks in the open world. However, the unstable behavior of OOD detection along the optimization…
Dynamics-Aware Loss for Learning with Label Noise
Xiu-Chuan Li, Xiaobo Xia, Fei Zhu +3
Label noise poses a serious threat to deep neural networks (DNNs). Employing robust loss functions which reconcile fitting ability with robustness is a simple but effective strateg…