no code implementations • 22 Aug 2023 • Xin Duan, Yan Yang, Liyuan Pan, Xiabi Liu
With a backbone segmentation network that independently processes each image from the set, we introduce semantics from CLIP into the backbone features, refining them in a coarse-to-fine manner with three key modules: i) an image set feature correspondence module, encoding global consistent semantic information of the image set; ii) a CLIP interaction module, using CLIP-mined common semantics of the image set to refine the backbone feature; iii) a CLIP regularization module, drawing CLIP towards this co-segmentation task, identifying the best CLIP semantic and using it to regularize the backbone feature.
no code implementations • 12 Apr 2022 • Xin Duan, Xiabi Liu, Xiaopeng Gong, Mengqiao Han
For the image co-segmentation problem, we propose a collaborative RL algorithm based on the A3C model.
no code implementations • 11 Oct 2021 • Mengqiao Han, Xiabi Liu, Zhaoyang Hai, Xin Duan
We introduce a switcher neural network (SNN) that uses as inputs the weights of a task-specific neural network (called TNN for short).
1 code implementation • 25 Aug 2020 • Dorin Ungureanu, Federica Bogo, Silvano Galliani, Pooja Sama, Xin Duan, Casey Meekhof, Jan Stühmer, Thomas J. Cashman, Bugra Tekin, Johannes L. Schönberger, Pawel Olszta, Marc Pollefeys
Mixed reality headsets, such as the Microsoft HoloLens 2, are powerful sensing devices with integrated compute capabilities, which makes it an ideal platform for computer vision research.
no code implementations • 18 Nov 2019 • Xiabi Liu, Xin Duan
Then we describe the traditional methods in three categories of object elements based, object regions/contours based, common object model based.