Search Results for author:
Found 5 papers, 1 papers with code
LCCo: Lending CLIP to Co-Segmentation
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.
RL-CoSeg : A Novel Image Co-Segmentation Algorithm with Deep Reinforcement Learning
For the image co-segmentation problem, we propose a collaborative RL algorithm based on the A3C model.
Mining the Weights Knowledge for Optimizing Neural Network Structures
We introduce a switcher neural network (SNN) that uses as inputs the weights of a task-specific neural network (called TNN for short).
HoloLens 2 Research Mode as a Tool for Computer Vision Research
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.
Automatic Image Co-Segmentation: A Survey
Then we describe the traditional methods in three categories of object elements based, object regions/contours based, common object model based.
