1 citations · 2 across the 5 of their papers we have counts for
6 papers
PerturbBridge: Direct Phenotypic Supervision for Cellular Perturbation Generation
Yucheng Yang, Xiaoxiao Sun, Huangqingbo Sun +3
Virtual cell models should capture phenotypes induced by specific perturbations. However, image-based models are typically trained and evaluated to match real-image distributions,…
DataComp-VLM: Improved Open Datasets for Vision-Language Models
Matteo Farina, Vishaal Udandarao, Thao Nguyen +33
Building performant Vision-Language Models (VLMs) requires carefully curating large-scale training datasets, yet the community lacks systematic benchmarks for evaluating such curat…
CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning
Dongxia Wu, Shiye Su, Yuhui Zhang +4
Building virtual cells with generative models to simulate cellular behavior in silico is emerging as a promising paradigm for accelerating drug discovery. However, prior image-base…
Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation
Yuhui Zhang, Yuchang Su, Yiming Liu +9
The rapid development of vision language models (VLMs) demands rigorous and reliable evaluation. However, current visual question answering (VQA) benchmarks often depend on open-en…
Just Shift It: Test-Time Prototype Shifting for Zero-Shot Generalization with Vision-Language Models
Elaine Sui, Xiaohan Wang, Serena Yeung-Levy
Advancements in vision-language models (VLMs) have propelled the field of computer vision, particularly in the zero-shot learning setting. Despite their promise, the effectiveness…
Connect, Collapse, Corrupt: Learning Cross-Modal Tasks with Uni-Modal Data
Yuhui Zhang, Elaine Sui, Serena Yeung-Levy
Building cross-modal applications is challenging due to limited paired multi-modal data. Recent works have shown that leveraging a pre-trained multi-modal contrastive representatio…