4 citations · 6 across the 9 of their papers we have counts for
9 papers
When Preference Labels Fall Short: Aligning Diffusion Models from Real Data
Weiyan Chen, Weijian Deng, Yao Xiao +5
Preference alignment aims to guide generative models by learning from comparisons between preferred and non-preferred samples. In practice, most existing approaches rely on prefere…
Where Detectors Fail: Probing Generative Space for Generalizable AI-Generated Image Detection
Zijie Cao, Weijie Tu, Yao Xiao +3
Detecting AI-generated images (AIGI) remains challenging because detectors often fail to generalize to unseen generators. Although existing methods are trained on large datasets, t…
EEG-Based Brain-LLM Interface for Human Preference Aligned Generation
Junzi Zhang, Jianing Shen, Weijie Tu +5
Large language models (LLMs) are becoming an increasingly important component of human--computer interaction, enabling users to coordinate a wide range of intelligent agents throug…
Confidence and Dispersity as Signals: Unsupervised Model Evaluation and Ranking
Weijian Deng, Weijie Tu, Ibrahim Radwan +3
Assessing model generalization under distribution shift is essential for real-world deployment, particularly when labeled test data is unavailable. This paper presents a unified an…
Ranked from Within: Ranking Large Multimodal Models Without Labels
Weijie Tu, Weijian Deng, Dylan Campbell +4
Can the relative performance of a pre-trained large multimodal model (LMM) be predicted without access to labels? As LMMs proliferate, it becomes increasingly important to develop…
Toward a Holistic Evaluation of Robustness in CLIP Models
Weijie Tu, Weijian Deng, Tom Gedeon
Contrastive Language-Image Pre-training (CLIP) models have shown significant potential, particularly in zero-shot classification across diverse distribution shifts. Building on exi…
What Does Softmax Probability Tell Us about Classifiers Ranking Across Diverse Test Conditions?
Weijie Tu, Weijian Deng, Liang Zheng +1
This work aims to develop a measure that can accurately rank the performance of various classifiers when they are tested on unlabeled data from out-of-distribution (OOD) distributi…
A Closer Look at the Robustness of Contrastive Language-Image Pre-Training (CLIP)
Weijie Tu, Weijian Deng, Tom Gedeon
Contrastive Language-Image Pre-training (CLIP) models have demonstrated remarkable generalization capabilities across multiple challenging distribution shifts. However, there is st…
An Empirical Study Into What Matters for Calibrating Vision-Language Models
Weijie Tu, Weijian Deng, Dylan Campbell +2
Vision-Language Models (VLMs) have emerged as the dominant approach for zero-shot recognition, adept at handling diverse scenarios and significant distribution changes. However, th…