41 citations · 51 across the 5 of their papers we have counts for
5 papers
LAION-SG: An Enhanced Large-Scale Dataset for Training Complex Image-Text Models with Structural Annotations
Zejian Li, Chenye Meng, Yize Li +9
Recent advances in text-to-image (T2I) generation have shown remarkable success in producing high-quality images from text. However, existing T2I models show decayed performance in…
MindScratch: A Visual Programming Support Tool for Classroom Learning Based on Multimodal Generative AI
Yunnong Chen, Shuhong Xiao, Yaxuan Song +3
Programming has become an essential component of K-12 education and serves as a pathway for developing computational thinking skills. Given the complexity of programming and the ad…
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…
Reality3DSketch: Rapid 3D Modeling of Objects from Single Freehand Sketches
Tianrun Chen, Chaotao Ding, Lanyun Zhu +4
The emerging trend of AR/VR places great demands on 3D content. However, most existing software requires expertise and is difficult for novice users to use. In this paper, we aim t…
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…