most citedSAIL-VL2 Technical Report

1 citations · 1 across the 2 of their papers we have counts for

collaborators

8 papers

cs.CV2026

GPD: Guided Progressive Distillation for Fast and High-Quality Video Generation

Xiao Liang, Yunzhu Zhang, Linchao Zhu

Diffusion models have achieved remarkable success in video generation; however, the high computational cost of the denoising process remains a major bottleneck. Existing approaches…

cs.CV2025

Burst Image Quality Assessment: A New Benchmark and Unified Framework for Multiple Downstream Tasks

Xiaoye Liang, Lai Jiang, Minglang Qiao +6

In recent years, the development of burst imaging technology has improved the capture and processing capabilities of visual data, enabling a wide range of applications. However, th…

cs.IR2025

SAIL-Embedding Technical Report: Omni-modal Embedding Foundation Model

Lin Lin, Jiefeng Long, Zhihe Wan +15

Multimodal embedding models aim to yield informative unified representations that empower diverse cross-modal tasks. Despite promising developments in the evolution from CLIP-based…

cs.CV2025

SAIL-RL: Guiding MLLMs in When and How to Think via Dual-Reward RL Tuning

Fangxun Shu, Yongjie Ye, Yue Liao +6

We introduce SAIL-RL, a reinforcement learning (RL) post-training framework that enhances the reasoning capabilities of multimodal large language models (MLLMs) by teaching them wh…

cs.CV20251 cited

SAIL-VL2 Technical Report

Weijie Yin, Yongjie Ye, Fangxun Shu +11

We introduce SAIL-VL2, an open-suite vision-language foundation model (LVM) for comprehensive multimodal understanding and reasoning. As the successor to SAIL-VL, SAIL-VL2 achieves…

cs.CV2025

SAILViT: Towards Robust and Generalizable Visual Backbones for MLLMs via Gradual Feature Refinement

Weijie Yin, Dingkang Yang, Hongyuan Dong +5

Vision Transformers (ViTs) are essential as foundation backbones in establishing the visual comprehension capabilities of Multimodal Large Language Models (MLLMs). Although most Vi…