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Kunyang Li

3 accepted papers

2025

All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages

CVPR 2025highlight

Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cultural contexts, respect local sensitivities, and support low-resource languages, all while effectively integrating corr…

2025

On the Robustness Tradeoff in Fine-Tuning

ICCV 2025poster

Fine-tuning has become the standard practice for adapting pre-trained models to downstream tasks. However, the impact on model robustness is not well understood. In this work, we characterize the robustness-accuracy trade-off in fine-tuning. We evaluate the robustness and accuracy of fine-tuned mode…

Cited by 0SourcePDFScholar
2023

The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning

ICLR 2023top-25%

Pre-training representations (a.k.a. foundation models) has recently become a prevalent learning paradigm, where one first pre-trains a representation using large-scale unlabeled data, and then learns simple predictors on top of the representation using small labeled data from the downstream tasks.…