activity
20232025
most citedM3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models

18 citations · 53 across the 9 of their papers we have counts for

collaborators

9 papers

cs.RO2025

RoboBrain 2.0 Technical Report

BAAI RoboBrain Team, Mingyu Cao, Huajie Tan +50

We introduce RoboBrain 2.0, our latest generation of embodied vision-language foundation models, designed to unify perception, reasoning, and planning for complex embodied tasks in…

cs.CV2024★ 2 cited

Touchstone Benchmark: Are We on the Right Way for Evaluating AI Algorithms for Medical Segmentation?

Pedro R. A. S. Bassi, Wenxuan Li, Yucheng Tang +50

How can we test AI performance? This question seems trivial, but it isn't. Standard benchmarks often have problems such as in-distribution and small-size test sets, oversimplified…

cs.CV2024

Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding

Yan Shu, Zheng Liu, Peitian Zhang +5

Long video understanding poses a significant challenge for current Multi-modal Large Language Models (MLLMs). Notably, the MLLMs are constrained by their limited context lengths an…

cs.CV2024★ 1 cited

OmniGen: Unified Image Generation

Shitao Xiao, Yueze Wang, Junjie Zhou +7

The emergence of Large Language Models (LLMs) has unified language generation tasks and revolutionized human-machine interaction. However, in the realm of image generation, a unifi…

cs.CV2024★ 4 cited

MLVU: Benchmarking Multi-task Long Video Understanding

Junjie Zhou, Yan Shu, Bo Zhao +9

The evaluation of Long Video Understanding (LVU) performance poses an important but challenging research problem. Despite previous efforts, the existing video understanding benchma…

cs.CV2024★ 18 cited

M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models

Fan Bai, Yuxin Du, Tiejun Huang +2

Medical image analysis is essential to clinical diagnosis and treatment, which is increasingly supported by multi-modal large language models (MLLMs). However, previous research ha…

cs.CV2024

Pushing Auto-regressive Models for 3D Shape Generation at Capacity and Scalability

Xuelin Qian, Yu Wang, Simian Luo +9

Auto-regressive models have achieved impressive results in 2D image generation by modeling joint distributions in grid space. In this paper, we extend auto-regressive models to 3D…

cs.CV2024★ 13 cited

Efficient Multimodal Learning from Data-centric Perspective

Muyang He, Yexin Liu, Boya Wu +4

Multimodal Large Language Models (MLLMs) have demonstrated notable capabilities in general visual understanding and reasoning tasks. However, their deployment is hindered by substa…

cs.CV2023★ 15 cited

SegVol: Universal and Interactive Volumetric Medical Image Segmentation

Yuxin Du, Fan Bai, Tiejun Huang +1

Precise image segmentation provides clinical study with instructive information. Despite the remarkable progress achieved in medical image segmentation, there is still an absence o…