7 citations · 10 across the 14 of their papers we have counts for
6 papers · 1 filter
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…