activity
20242026
most citedGroundingGPT:Language Enhanced Multi-modal Grounding Model

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Private and Stable Test-Time Adaptation with Differential Privacy

Zefeng Li, Qiaoyue Tang, Mathias Lecuyer +1

Test-time adaptation (TTA) can reduce error on new and different data by updating the model on these inputs during inference. However, these updates raise the issue of privacy w.r.…

cs.LG2026

A Closer Look at In-Distribution vs. Out-of-Distribution Accuracy for Open-Set Test-time Adaptation

Zefeng Li, Evan Shelhamer

Open-set test-time adaptation (TTA) updates models on new data in the presence of input shifts and unknown output classes. While recent methods have made progress on improving in-d…

cs.CV2026

Masked Generative Transformer Is What You Need for Image Editing

Wei Chow, Linfeng Li, Xian Sun +14

Diffusion models dominate image editing, yet their global denoising mechanism entangles edited regions with surrounding context, causing modifications to propagate into areas that…

cs.CV2025

EditMGT: Unleashing Potentials of Masked Generative Transformers in Image Editing

Wei Chow, Linfeng Li, Lingdong Kong +13

Recent advances in diffusion models (DMs) have achieved exceptional visual quality in image editing tasks. However, the global denoising dynamics of DMs inherently conflate local e…

cs.CV2024★ 1 cited

GroundingGPT:Language Enhanced Multi-modal Grounding Model

Zhaowei Li, Qi Xu, Dong Zhang +9

Multi-modal large language models have demonstrated impressive performance across various tasks in different modalities. However, existing multi-modal models primarily emphasize ca…