24 citations · 61 across the 9 of their papers we have counts for
10 papers
MM-RLHF: The Next Step Forward in Multimodal LLM Alignment
Yi-Fan Zhang, Tao Yu, Haochen Tian +17
Despite notable advancements in Multimodal Large Language Models (MLLMs), most state-of-the-art models have not undergone thorough alignment with human preferences. This gap exists…
Beyond LLaVA-HD: Diving into High-Resolution Large Multimodal Models
Yi-Fan Zhang, Qingsong Wen, Chaoyou Fu +4
Seeing clearly with high resolution is a foundation of Large Multimodal Models (LMMs), which has been proven to be vital for visual perception and reasoning. Existing works usually…
Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt
YiFan Zhang, Weiqi Chen, Zhaoyang Zhu +7
Online updating of time series forecasting models aims to tackle the challenge of concept drifting by adjusting forecasting models based on streaming data. While numerous algorithm…
Debiasing Multimodal Large Language Models via Penalization of Language Priors
YiFan Zhang, Yang Shi, Weichen Yu +6
In the realms of computer vision and natural language processing, Multimodal Large Language Models (MLLMs) have become indispensable tools, proficient in generating textual respons…
Model-free Test Time Adaptation for Out-Of-Distribution Detection
YiFan Zhang, Xue Wang, Tian Zhou +5
Out-of-distribution (OOD) detection is essential for the reliability of ML models. Most existing methods for OOD detection learn a fixed decision criterion from a given in-distribu…
Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook
Ming Jin, Yaxuan Kong, Yuxuan Liang +13
Temporal data, including time series and spatio-temporal data, are pervasive in real-world applications. Generated in massive volumes by physical and virtual sensors, they record d…
OneNet: Enhancing Time Series Forecasting Models under Concept Drift by Online Ensembling
Yi-Fan Zhang, Qingsong Wen, Xue Wang +6
Online updating of time series forecasting models aims to address the concept drifting problem by efficiently updating forecasting models based on streaming data. Many algorithms a…
AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation
Yi-Fan Zhang, Xue Wang, Kexin Jin +5
Many recent machine learning tasks focus to develop models that can generalize to unseen distributions. Domain generalization (DG) has become one of the key topics in various field…
Free Lunch for Domain Adversarial Training: Environment Label Smoothing
YiFan Zhang, Xue Wang, Jian Liang +4
A fundamental challenge for machine learning models is how to generalize learned models for out-of-distribution (OOD) data. Among various approaches, exploiting invariant features…
Progressive Backdoor Erasing via connecting Backdoor and Adversarial Attacks
Bingxu Mu, Zhenxing Niu, Le Wang +3
Deep neural networks (DNNs) are known to be vulnerable to both backdoor attacks as well as adversarial attacks. In the literature, these two types of attacks are commonly treated a…