activity
20192025
most citedTransUNet: Transformers Make Strong Encoders for Medical Image Segmentation

4k citations · 5.5k across the 25 of their papers we have counts for

collaborators

25 papers

cs.CV2025

World-in-World: World Models in a Closed-Loop World

Jiahan Zhang, Muqing Jiang, Nanru Dai +14

Generative world models (WMs) can now simulate worlds with striking visual realism, which naturally raises the question of whether they can endow embodied agents with predictive pe…

cs.CV2025

SpatialLLM: A Compound 3D-Informed Design towards Spatially-Intelligent Large Multimodal Models

Wufei Ma, Luoxin Ye, Celso M de Melo +2

Humans naturally understand 3D spatial relationships, enabling complex reasoning like predicting collisions of vehicles from different directions. Current large multimodal models (…

cs.CV2025

Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Multimodal Models

Xingrui Wang, Wufei Ma, Tiezheng Zhang +3

Although large multimodal models (LMMs) have demonstrated remarkable capabilities in visual scene interpretation and reasoning, their capacity for complex and precise 3-dimensional…

cs.CV2024

Generative World Explorer

Taiming Lu, Tianmin Shu, Alan Yuille +2

Planning with partial observation is a central challenge in embodied AI. A majority of prior works have tackled this challenge by developing agents that physically explore their en…

cs.CV2023

Prompt-Based Exemplar Super-Compression and Regeneration for Class-Incremental Learning

Ruxiao Duan, Jieneng Chen, Adam Kortylewski +2

Replay-based methods in class-incremental learning (CIL) have attained remarkable success. Despite their effectiveness, the inherent memory restriction results in saving a limited…

cs.CV2023★ 38 cited

3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers

Jieneng Chen, Jieru Mei, Xianhang Li +12

Medical image segmentation plays a crucial role in advancing healthcare systems for disease diagnosis and treatment planning. The u-shaped architecture, popularly known as U-Net, h…