10 citations · 10 across the 6 of their papers we have counts for
5 papers · 1 filter
Exploring Kernel Transformations for Implicit Neural Representations
Sheng Zheng, Chaoning Zhang, Dongshen Han +4
Implicit neural representations (INRs), which leverage neural networks to represent signals by mapping coordinates to their corresponding attributes, have garnered significant atte…
MobileSAMv2: Faster Segment Anything to Everything
Chaoning Zhang, Dongshen Han, Sheng Zheng +3
Segment anything model (SAM) addresses two practical yet challenging segmentation tasks: \textbf{segment anything (SegAny)}, which utilizes a certain point to predict the mask for…
Single Image Reflection Removal with Patch Reflectance Prior
Dongshen Han, Heechan Yoon, Hyukmin Kwon +4
Single Image Reflection Removal (SIRR) in real-world images is a challenging task due to diverse image degradations occurring on the glass surface during light transmission and ref…
SAM Meets UAP: Attacking Segment Anything Model With Universal Adversarial Perturbation
Dongshen Han, Chaoning Zhang, Sheng Zheng +3
As Segment Anything Model (SAM) becomes a popular foundation model in computer vision, its adversarial robustness has become a concern that cannot be ignored. This works investigat…
Internal-External Boundary Attention Fusion for Glass Surface Segmentation
Dongshen Han, Seungkyu Lee, Chaoning Zhang +4
Glass surfaces of transparent objects and mirrors are not able to be uniquely and explicitly characterized by their visual appearances because they contain the visual appearance of…