6 papers
SCALER: SAM-Enhanced Collaborative Learning for Label-Deficient Concealed Object Segmentation
Chunming He, Rihan Zhang, Longxiang Tang +4
Existing methods for label-deficient concealed object segmentation (LDCOS) either rely on consistency constraints or Segment Anything Model (SAM)-based pseudo-labeling. However, th…
Segment Concealed Objects with Incomplete Supervision
Chunming He, Kai Li, Yachao Zhang +8
Incompletely-Supervised Concealed Object Segmentation (ISCOS) involves segmenting objects that seamlessly blend into their surrounding environments, utilizing incompletely annotate…
Real-world Image Dehazing with Coherence-based Pseudo Labeling and Cooperative Unfolding Network
Chengyu Fang, Chunming He, Fengyang Xiao +5
Real-world Image Dehazing (RID) aims to alleviate haze-induced degradation in real-world settings. This task remains challenging due to the complexities in accurately modeling real…
MultiBooth: Towards Generating All Your Concepts in an Image from Text
Chenyang Zhu, Kai Li, Yue Ma +2
This paper introduces MultiBooth, a novel and efficient technique for multi-concept customization in image generation from text. Despite the significant advancements in customized…
RUN: Reversible Unfolding Network for Concealed Object Segmentation
Chunming He, Rihan Zhang, Fengyang Xiao +7
Existing concealed object segmentation (COS) methods frequently utilize reversible strategies to address uncertain regions. However, these approaches are typically restricted to th…
InstantSwap: Fast Customized Concept Swapping across Sharp Shape Differences
Chenyang Zhu, Kai Li, Yue Ma +5
Recent advances in Customized Concept Swapping (CCS) enable a text-to-image model to swap a concept in the source image with a customized target concept. However, the existing meth…