Search Results for author:
Found 10 papers, 2 papers with code
SIT: A Bionic and Non-Linear Neuron for Spiking Neural Network
Spiking Neural Networks (SNNs) have piqued researchers' interest because of their capacity to process temporal information and low power consumption.
SRPCN: Structure Retrieval based Point Completion Network
Besides, the missing patterns are diverse in reality, but existing methods can only handle fixed ones, which means a poor generalization ability.
Generate Point Clouds with Multiscale Details from Graph-Represented Structures
As details are missing in most representations of structures, the lack of controllability to details is one of the major weaknesses in structure-based controllable point cloud generation.
Attention-based Transformation from Latent Features to Point Clouds
The points generated by AXform do not have the strong 2-manifold constraint, which improves the generation of non-smooth surfaces.
Safe Distillation Box
In other words, all users may employ a model in SDB for inference, but only authorized users get access to KD from the model.
Document Layout Analysis with Aesthetic-Guided Image Augmentation
To measure the proposed image layer modeling method, we propose a manually-labeled non-Manhattan layout fine-grained segmentation dataset named FPD.
Adaptive Charging Networks: A Framework for Smart Electric Vehicle Charging
We describe the architecture and algorithms of the Adaptive Charging Network (ACN), which was first deployed on the Caltech campus in early 2016 and is currently operating at over 100 other sites in the United States.
One-sample Guided Object Representation Disassembling
In this paper, we introduce the One-sample Guided Object Representation Disassembling (One-GORD) method, which only requires one annotated sample for each object category to learn disassembled object representation from unannotated images.
GASNet: Weakly-supervised Framework for COVID-19 Lesion Segmentation
Segmentation of infected areas in chest CT volumes is of great significance for further diagnosis and treatment of COVID-19 patients.
TripletGAN: Training Generative Model with Triplet Loss
As an effective way of metric learning, triplet loss has been widely used in many deep learning tasks, including face recognition and person-ReID, leading to many states of the arts.
