7 citations · 10 across the 14 of their papers we have counts for
16 papers
Are We Making Progress in Multimodal Domain Generalization? A Comprehensive Benchmark Study
Hao Dong, Hongzhao Li, Shupan Li +3
Despite the growing popularity of Multimodal Domain Generalization (MMDG) for enhancing model robustness, it remains unclear whether reported performance gains reflect genuine algo…
Adaptive Confidence Regularization for Multimodal Failure Detection
Moru Liu, Hao Dong, Olga Fink +1
The deployment of multimodal models in high-stakes domains, such as self-driving vehicles and medical diagnostics, demands not only strong predictive performance but also reliable…
Multimodal Learning for Arcing Detection in Pantograph-Catenary Systems
Hao Dong, Eleni Chatzi, Olga Fink
The pantograph-catenary interface is essential for ensuring uninterrupted and reliable power delivery in electrified rail systems. However, electrical arcing at this interface pose…
Adapting Vision-Language Models Without Labels: A Comprehensive Survey
Hao Dong, Lijun Sheng, Jian Liang +3
Vision-Language Models (VLMs) have demonstrated remarkable generalization capabilities across a wide range of tasks. However, their performance often remains suboptimal when direct…
To Trust Or Not To Trust Your Vision-Language Model's Prediction
Hao Dong, Moru Liu, Jian Liang +2
Vision-Language Models (VLMs) have demonstrated strong capabilities in aligning visual and textual modalities, enabling a wide range of applications in multimodal understanding and…
Extremely Simple Multimodal Outlier Synthesis for Out-of-Distribution Detection and Segmentation
Moru Liu, Hao Dong, Jessica Kelly +2
Out-of-distribution (OOD) detection and segmentation are crucial for deploying machine learning models in safety-critical applications such as autonomous driving and robot-assisted…
Interactive Symbolic Regression through Offline Reinforcement Learning: A Co-Design Framework
Yuan Tian, Wenqi Zhou, Michele Viscione +3
Symbolic Regression (SR) holds great potential for uncovering underlying mathematical and physical relationships from observed data. However, the vast combinatorial space of possib…
Towards Robust Multimodal Open-set Test-time Adaptation via Adaptive Entropy-aware Optimization
Hao Dong, Eleni Chatzi, Olga Fink
Test-time adaptation (TTA) has demonstrated significant potential in addressing distribution shifts between training and testing data. Open-set test-time adaptation (OSTTA) aims to…
Advances in Multimodal Adaptation and Generalization: From Traditional Approaches to Foundation Models
Hao Dong, Moru Liu, Kaiyang Zhou +4
In real-world scenarios, achieving domain adaptation and generalization poses significant challenges, as models must adapt to or generalize across unknown target distributions. Ext…
Recall and Refine: A Simple but Effective Source-free Open-set Domain Adaptation Framework
Ismail Nejjar, Hao Dong, Olga Fink
Open-set Domain Adaptation (OSDA) aims to adapt a model from a labeled source domain to an unlabeled target domain, where novel classes - also referred to as target-private unknown…
Towards Multimodal Open-Set Domain Generalization and Adaptation through Self-supervision
Hao Dong, Eleni Chatzi, Olga Fink
The task of open-set domain generalization (OSDG) involves recognizing novel classes within unseen domains, which becomes more challenging with multiple modalities as input. Existi…
Unseen Visual Anomaly Generation
Han Sun, Yunkang Cao, Hao Dong +1
Visual anomaly detection (AD) presents significant challenges due to the scarcity of anomalous data samples. While numerous works have been proposed to synthesize anomalous samples…
MultiOOD: Scaling Out-of-Distribution Detection for Multiple Modalities
Hao Dong, Yue Zhao, Eleni Chatzi +1
Detecting out-of-distribution (OOD) samples is important for deploying machine learning models in safety-critical applications such as autonomous driving and robot-assisted surgery…
Interactive Symbolic Regression through Offline Reinforcement Learning: A Co-Design Framework
Yuan Tian, Wenqi Zhou, Michele Viscione +3
Symbolic Regression (SR) holds great potential for uncovering underlying mathematical and physical relationships from observed data. However, the vast combinatorial space of possib…
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation
Hao Dong, Gaëtan Frusque, Yue Zhao +2
Anomaly detection (AD) is essential in identifying rare and often critical events in complex systems, finding applications in fields such as network intrusion detection, financial…
SimMMDG: A Simple and Effective Framework for Multi-modal Domain Generalization
Hao Dong, Ismail Nejjar, Han Sun +2
In real-world scenarios, achieving domain generalization (DG) presents significant challenges as models are required to generalize to unknown target distributions. Generalizing to…