activity
20242026
most citedExpanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

16 citations · 47 across the 16 of their papers we have counts for

collaborators
Showing cs.CVShow all

17 papers · 1 filter

cs.CV2026

JoyAI-VL-Interaction: Real-Time Vision-Language Interaction Intelligence

Dingyu Yao, Junhao Zhou, Chenxu Yang +12

Many moments in the real world do not wait for a user to ask. A fire starts on a security monitor, an expression flickers across a video call, or a product a viewer wants flashes b…

cs.CV2026

HSD: Training-Free Acceleration for Document Parsing Vision-Language Models with Hierarchical Speculative Decoding

Wenhui Liao, Hongliang Li, Pengyu Xie +15

Document parsing is a fundamental task in multimodal understanding, supporting a wide range of downstream applications such as information extraction and intelligent document analy…

cs.CV2025

Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal Generation and Understanding

Yi Xin, Qi Qin, Siqi Luo +29

We introduce Lumina-DiMOO, an open-source foundational model for seamless multi-modal generation and understanding. Lumina-DiMOO sets itself apart from prior unified models by util…

cs.CV2025

InternSVG: Towards Unified SVG Tasks with Multimodal Large Language Models

Haomin Wang, Jinhui Yin, Qi Wei +12

General SVG modeling remains challenging due to fragmented datasets, limited transferability of methods across tasks, and the difficulty of handling structural complexity. In respo…

cs.CV2025

ScaleCUA: Scaling Open-Source Computer Use Agents with Cross-Platform Data

Zhaoyang Liu, Jingjing Xie, Zichen Ding +27

Vision-Language Models (VLMs) have enabled computer use agents (CUAs) that operate GUIs autonomously, showing great potential, yet progress is limited by the lack of large-scale, o…

cs.CV20254 cited

InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

Weiyun Wang, Zhangwei Gao, Lixin Gu +72

We introduce InternVL 3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL…