175 citations · 543 across the 42 of their papers we have counts for
42 papers
ElementCheck: Complexity-Aware Long-Form Text Factuality Evaluation via Sentence Elements
Xinming Wang, Haoran Du, Yi Chen +6
Existing long-form factuality evaluation relies on the decompose-retrieve-verify pipeline. However, the pipeline suffers from noise from claim decomposition and fixed verification…
PDE-Agent: A toolchain-augmented multi-agent framework for PDE solving
Jianming Liu, Ren Zhu, Jian Xu +4
Solving Partial Differential Equations (PDEs) is a cornerstone of engineering and scientific research. Traditional methods for PDE solving are cumbersome, relying on manual setup a…
MR-Align: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models
Xinming Wang, Jian Xu, Bin Yu +9
Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited. We find this limitation i…
Continual Learning for Generative AI: From LLMs to MLLMs and Beyond
Haiyang Guo, Fanhu Zeng, Fei Zhu +9
The rapid advancement of generative models has empowered modern AI systems to comprehend and produce highly sophisticated content, even achieving human-level performance in specifi…
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…
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…
HiDe-LLaVA: Hierarchical Decoupling for Continual Instruction Tuning of Multimodal Large Language Model
Haiyang Guo, Fanhu Zeng, Ziwei Xiang +4
Instruction tuning is widely used to improve a pre-trained Multimodal Large Language Model (MLLM) by training it on curated task-specific datasets, enabling better comprehension of…
Federated Continual Instruction Tuning
Haiyang Guo, Fanhu Zeng, Fei Zhu +5
A vast amount of instruction tuning data is crucial for the impressive performance of Large Multimodal Models (LMMs), but the associated computational costs and data collection dem…
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…
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…
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…
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…