D
Deng-Ping Fan
Researcher at Nankai University
Publications - 45
Citations - 2392
Deng-Ping Fan is an academic researcher from Nankai University. The author has contributed to research in topics: Computer science & Object detection. The author has an hindex of 11, co-authored 45 publications receiving 465 citations.
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Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
Wenhai Wang,Enze Xie,Xiang Li,Deng-Ping Fan,Kaitao Song,Ding Liang,Tong Lu,Ping Luo,Ling Shao +8 more
TL;DR: Huang et al. as discussed by the authors proposed Pyramid Vision Transformer (PVT), which is a simple backbone network useful for many dense prediction tasks without convolutions, and achieved state-of-the-art performance on the COCO dataset.
Posted Content
PVTv2: Improved Baselines with Pyramid Vision Transformer
Wenhai Wang,Enze Xie,Xiang Li,Deng-Ping Fan,Kaitao Song,Ding Liang,Tong Lu,Ping Luo,Ling Shao +8 more
TL;DR: Huang et al. as mentioned in this paper improved the Pyramid Vision Transformer (abbreviated as PVTv1) by adding three designs, including overlapping patch embedding, convolutional feed-forward networks and linear complexity attention layers.
Proceedings ArticleDOI
Camouflaged Object Segmentation with Distraction Mining
TL;DR: Zhang et al. as mentioned in this paper developed a bio-inspired framework, termed Positioning and Focus Network (PFNet), which mimics the process of predation in nature, which contains two key modules, i.e., the positioning module (PM) and the focus module (FM).
Journal ArticleDOI
Concealed Object Detection.
TL;DR: Li et al. as discussed by the authors presented the first systematic study on concealed object detection (COD), which aims to identify objects that are?perfectly? embedded in their background, and designed a simple but strong baseline for COD, termed the Search Identification Network (SINet).
Proceedings ArticleDOI
Simultaneously Localize, Segment and Rank the Camouflaged Objects
TL;DR: Zhang et al. as mentioned in this paper proposed a ranking-based COD network (Rank-Net) to simultaneously localize, segment and rank camouflaged objects, where the localization model is proposed to find the discriminative regions that make the camouflaged object obvious.