most citedEfficient Segment Anything with Depth-Aware Fusion and Limited Training Data

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cs.CV2026

Principal Component Analysis-Based Terahertz Self-Supervised Denoising and Deblurring Deep Neural Networks

Pengfei Zhu, Stefano Sfarra, Hai Zhang +4

Terahertz (THz) systems inherently introduce frequency-dependent degradation effects, resulting in low-frequency blurring and high-frequency noise in amplitude images. Conventional…

cs.CV2026★ 1 cited

Efficient Segment Anything with Depth-Aware Fusion and Limited Training Data

Yiming Zhou, Xuenjie Xie, Panfeng Li +3

Segment Anything Models (SAM) achieve impressive universal segmentation performance but require massive datasets (e.g., 11M images) and rely solely on RGB inputs. Recent efficient…