activity
20232026
most citedSAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

41 citations · 58 across the 13 of their papers we have counts for

collaborators
Showing cs.CVShow all

10 papers · 1 filter

cs.CV2026

MoWorld: A Flash World Model

Team Moxin, Deyi Ji, Tianrun Chen +37

The future of World Models depends not only on scaling model capability, but also on scaling practicality and inference efficiency. High-frame-rate inference enables responsive per…

cs.CV2026

4DVGGT-D: 4D Visual Geometry Transformer with Improved Dynamic Depth Estimation

Ying Zang, Xuanyi Liu, Yidong Han +9

Reconstructing dynamic 4D scenes from monocular videos is a fundamental yet challenging task. While recent 3D foundation models provide strong geometric priors, their performance s…

cs.CV2026

Robust 4D Visual Geometry Transformer with Uncertainty-Aware Priors

Ying Zang, Yidong Han, Chaotao Ding +8

Reconstructing dynamic 4D scenes is an important yet challenging task. While 3D foundation models like VGGT excel in static settings, they often struggle with dynamic sequences whe…

cs.CV2026

HD-VGGT: High-Resolution Visual Geometry Transformer

Tianrun Chen, Yuanqi Hu, Yidong Han +11

High-resolution imagery is essential for accurate 3D reconstruction, as many geometric details only emerge at fine spatial scales. Recent feed-forward approaches, such as the Visua…

cs.CV2025

SAM3-Adapter: Efficient Adaptation of Segment Anything 3 for Camouflage Object Segmentation, Shadow Detection, and Medical Image Segmentation

Tianrun Chen, Runlong Cao, Xinda Yu +8

The rapid rise of large-scale foundation models has reshaped the landscape of image segmentation, with models such as Segment Anything achieving unprecedented versatility across di…

cs.CV2025

Syllables to Scenes: Literary-Guided Free-Viewpoint 3D Scene Synthesis from Japanese Haiku

Chunan Yu, Yidong Han, Chaotao Ding +6

In the era of the metaverse, where immersive technologies redefine human experiences, translating abstract literary concepts into navigable 3D environments presents a fundamental c…

cs.CV2024★ 9 cited

SAM2-Adapter: Evaluating & Adapting Segment Anything 2 in Downstream Tasks: Camouflage, Shadow, Medical Image Segmentation, and More

Tianrun Chen, Ankang Lu, Lanyun Zhu +7

The advent of large models, also known as foundation models, has significantly transformed the AI research landscape, with models like Segment Anything (SAM) achieving notable succ…

cs.CV2024★ 3 cited

Magic3DSketch: Create Colorful 3D Models From Sketch-Based 3D Modeling Guided by Text and Language-Image Pre-Training

Ying Zang, Yidong Han, Chaotao Ding +2

The requirement for 3D content is growing as AR/VR application emerges. At the same time, 3D modelling is only available for skillful experts, because traditional methods like Comp…

cs.CV2023

Video Frame Interpolation with Stereo Event and Intensity Camera

Chao Ding, Mingyuan Lin, Haijian Zhang +2

The stereo event-intensity camera setup is widely applied to leverage the advantages of both event cameras with low latency and intensity cameras that capture accurate brightness a…

cs.CV2023★ 41 cited

SAM Fails to Segment Anything? -- SAM-Adapter: Adapting SAM in Underperformed Scenes: Camouflage, Shadow, Medical Image Segmentation, and More

Tianrun Chen, Lanyun Zhu, Chaotao Ding +6

The emergence of large models, also known as foundation models, has brought significant advancements to AI research. One such model is Segment Anything (SAM), which is designed for…