17 Papers
Chunming He is an academic researcher from Nanjing University of Posts and Telecommunications. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 2, co-authored 4 publications.
Chat about Author
Papers
Camouflaged Object Detection with Feature Decomposition and Edge Reconstruction
Chunming He,Kaixuan Li,Yachao Zhang,Longxiang Tang,Yulun Zhang,Zhenhua Guo,Xiu Li +6 more
- 01 Jun 2023
TL;DR: By learning the auxiliary task in conjunction with the COD task, the FEDER model can generate precise prediction maps with accurate object boundaries and significantly outperforms state-of-the-art methods with cheaper computational and memory costs.
178
Weakly-Supervised Concealed Object Segmentation with SAM-based Pseudo Labeling and Multi-scale Feature Grouping
TL;DR: In this article , a multi-scale feature grouping module was proposed for weakly-supervised concealed object segmentation (WSCOS), which first groups features at different granularities and then aggregates these grouping results.
Strategic Preys Make Acute Predators: Enhancing Camouflaged Object Detectors by Generating Camouflaged Objects
Chunming He,Kaixuan Li,Yachao Zhang,Yulun Zhang,Zhenhua Guo,Xiu Li,Martin Danelljan,Fisher Yu +7 more
TL;DR: An adversarial training framework is proposed, Camouflageator, which introduces an auxiliary generator to generate more camouflaged objects that are harder for a COD method to detect and ICEG, which brings state-of-the-art COD performance.
DM-Fusion: Deep Model-Driven Network for Heterogeneous Image Fusion.
TL;DR: DM-fusion as mentioned in this paper is a deep model-driven HIF network that adaptively integrates the merits of model-based techniques for interpretability and deep learning-based methods for generalizability.
33
Multi-modal sequence learning for Alzheimer's disease progression prediction with incomplete variable-length longitudinal data
TL;DR: In this paper , a multi-modal sequence learning framework, highlighted by deep latent representation collaborated sequence learning strategy, is proposed to flexibly handle the incomplete variable-length longitudinal multimodal data.
29