3 papers
cs.GR2025
Towards Understanding Graphical Perception in Large Multimodal Models
Kai Zhang, Jianwei Yang, Jeevana Priya Inala +4
Despite the promising results of large multimodal models (LMMs) in complex vision-language tasks that require knowledge, reasoning, and perception abilities together, we surprising…
cs.CV2025
Simplifying DINO via Coding Rate Regularization
Ziyang Wu, Jingyuan Zhang, Druv Pai +5
DINO and DINOv2 are two model families being widely used to learn representations from unlabeled imagery data at large scales. Their learned representations often enable state-of-t…
cs.CV2024
Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion
Jiuhai Chen, Jianwei Yang, Haiping Wu +4
We present Florence-VL, a new family of multimodal large language models (MLLMs) with enriched visual representations produced by Florence-2, a generative vision foundation model.…