4 papers
OpenVision 2: A Family of Generative Pretrained Visual Encoders for Multimodal Learning
Yanqing Liu, Xianhang Li, Letian Zhang +4
This paper provides a simplification on OpenVision's architecture and loss design for enhancing its training efficiency. Following the prior vision-language pretraining works CapPa…
OpenVision: A Fully-Open, Cost-Effective Family of Advanced Vision Encoders for Multimodal Learning
Xianhang Li, Yanqing Liu, Haoqin Tu +2
OpenAI's CLIP, released in early 2021, have long been the go-to choice of vision encoder for building multimodal foundation models. Although recent alternatives such as SigLIP have…
STAR-1: Safer Alignment of Reasoning LLMs with 1K Data
Zijun Wang, Haoqin Tu, Yuhan Wang +6
This paper introduces STAR-1, a high-quality, just-1k-scale safety dataset specifically designed for large reasoning models (LRMs) like DeepSeek-R1. Built on three core principles…
CLIPS: An Enhanced CLIP Framework for Learning with Synthetic Captions
Yanqing Liu, Xianhang Li, Zeyu Wang +2
Previous works show that noisy, web-crawled image-text pairs may limit vision-language pretraining like CLIP and propose learning with synthetic captions as a promising alternative…