10 citations · 14 across the 2 of their papers we have counts for
5 papers · 1 filter
Deshadow-Anything: When Segment Anything Model Meets Zero-shot shadow removal
Xiao Feng Zhang, Tian Yi Song, Jia Wei Yao
Segment Anything (SAM), an advanced universal image segmentation model trained on an expansive visual dataset, has set a new benchmark in image segmentation and computer vision. Ho…
Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNN
Jiawei Yao, Tong Wu, Xiaofeng Zhang
Monocular depth estimation is an ongoing challenge in computer vision. Recent progress with Transformer models has demonstrated notable advantages over conventional CNNs in this ar…
Enlighten Anything: When Segment Anything Model Meets Low-Light Image Enhancement
Qihan Zhao, Xiaofeng Zhang, Hao Tang +2
Image restoration is a low-level visual task, and most CNN methods are designed as black boxes, lacking transparency and intrinsic aesthetics. Many unsupervised approaches ignore t…
SAM-helps-Shadow:When Segment Anything Model meet shadow removal
Xiaofeng Zhang, Chaochen Gu, Shanying Zhu
The challenges surrounding the application of image shadow removal to real-world images and not just constrained datasets like ISTD/SRD have highlighted an urgent need for zero-sho…
SiENet: Siamese Expansion Network for Image Extrapolation
Xiaofeng Zhang, Feng Chen, Cailing Wang +3
Different from image inpainting, image outpainting has relative less context in the image center to capture and more content at the image border to predict. Therefore, classical en…