6 citations · 7 across the 7 of their papers we have counts for
7 papers
SceneForge: Structured World Supervision from 3D Interventions
Jizhizi Li, Jiayang Ao, Danny Wicks +1
Many multimodal learning tasks require supervision that remains consistent across edits, viewpoints, and scene-level interventions. However, such supervision is difficult to obtain…
Deep Image Matting: A Comprehensive Survey
Jizhizi Li, Jing Zhang, Dacheng Tao
Image matting refers to extracting precise alpha matte from natural images, and it plays a critical role in various downstream applications, such as image editing. Despite being an…
Referring Image Matting
Jizhizi Li, Jing Zhang, Dacheng Tao
Different from conventional image matting, which either requires user-defined scribbles/trimap to extract a specific foreground object or directly extracts all the foreground objec…
Rethinking Portrait Matting with Privacy Preserving
Sihan Ma, Jizhizi Li, Jing Zhang +2
Recently, there has been an increasing concern about the privacy issue raised by identifiable information in machine learning. However, previous portrait matting methods were all b…
Deep Automatic Natural Image Matting
Jizhizi Li, Jing Zhang, Dacheng Tao
Automatic image matting (AIM) refers to estimating the soft foreground from an arbitrary natural image without any auxiliary input like trimap, which is useful for image editing. P…
Privacy-Preserving Portrait Matting
Jizhizi Li, Sihan Ma, Jing Zhang +1
Recently, there has been an increasing concern about the privacy issue raised by using personally identifiable information in machine learning. However, previous portrait matting m…
Bridging Composite and Real: Towards End-to-end Deep Image Matting
Jizhizi Li, Jing Zhang, Stephen J. Maybank +1
Extracting accurate foregrounds from natural images benefits many downstream applications such as film production and augmented reality. However, the furry characteristics and vari…