2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.CV2025★ 2 cited
RFMedSAM 2: Automatic Prompt Refinement for Enhanced Volumetric Medical Image Segmentation with SAM 2
Bin Xie, Hao Tang, Yan Yan +1
Segment Anything Model 2 (SAM 2), a prompt-driven foundation model extending SAM to both image and video domains, has shown superior zero-shot performance compared to its predecess…
cs.LG2025
Rethinking Timesteps Samplers and Prediction Types
Bin Xie, Gady Agam
Diffusion models suffer from the huge consumption of time and resources to train. For example, diffusion models need hundreds of GPUs to train for several weeks for a high-resoluti…
cs.CV2025★ 1 cited
Self-Prompt SAM: Medical Image Segmentation via Automatic Prompt SAM Adaptation
Bin Xie, Hao Tang, Dawen Cai +2
Segment Anything Model (SAM) has demonstrated impressive zero-shot performance and brought a range of unexplored capabilities to natural image segmentation tasks. However, as a ver…