41 citations · 50 across the 4 of their papers we have counts for
4 papers
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…
Deep3DSketch+\+: High-Fidelity 3D Modeling from Single Free-hand Sketches
Ying Zang, Chaotao Ding, Tianrun Chen +2
The rise of AR/VR has led to an increased demand for 3D content. However, the traditional method of creating 3D content using Computer-Aided Design (CAD) is a labor-intensive and s…
Deep3DSketch+: Rapid 3D Modeling from Single Free-hand Sketches
Tianrun Chen, Chenglong Fu, Ying Zang +4
The rapid development of AR/VR brings tremendous demands for 3D content. While the widely-used Computer-Aided Design (CAD) method requires a time-consuming and labor-intensive mode…
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…