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cs.CV2025
SAM3-UNet: Simplified Adaptation of Segment Anything Model 3
Xinyu Xiong, Zihuang Wu, Lei Lu +1
In this paper, we introduce SAM3-UNet, a simplified variant of Segment Anything Model 3 (SAM3), designed to adapt SAM3 for downstream tasks at a low cost. Our SAM3-UNet consists of…
cs.CV2025
RT-DETRv4: Painlessly Furthering Real-Time Object Detection with Vision Foundation Models
Zijun Liao, Yian Zhao, Xin Shan +5
Real-time object detection has achieved substantial progress through meticulously designed architectures and optimization strategies. However, the pursuit of high-speed inference v…
cs.CV2025
SAM2-UNeXT: An Improved High-Resolution Baseline for Adapting Foundation Models to Downstream Segmentation Tasks
Xinyu Xiong, Zihuang Wu, Lei Zhang +3
Recent studies have highlighted the potential of adapting the Segment Anything Model (SAM) for various downstream tasks. However, constructing a more powerful and generalizable enc…