Search Results for author:
Found 13 papers, 4 papers with code
GeoSAM: Fine-tuning SAM with Sparse and Dense Visual Prompting for Automated Segmentation of Mobility Infrastructure
The Segment Anything Model (SAM) has shown impressive performance when applied to natural image segmentation.
FAS-UNet: A Novel FAS-driven Unet to Learn Variational Image Segmentation
Based on the variational theory and FAS algorithm, we first design a feature extraction sub-network (FAS-Solution module) to solve the model-driven nonlinear systems, where a skip-connection is employed to fuse the multi-scale features.
Cross-Skeleton Interaction Graph Aggregation Network for Representation Learning of Mouse Social Behaviour
Furthermore, we design a novel Interaction-Aware Transformer (IAT) to dynamically learn the graph-level representation of social behaviours and update the node-level representation, guided by our proposed interaction-aware self-attention mechanism.
Laser Data Based Automatic Generation of Lane-Level Road Map for Intelligent Vehicles
In this paper, an automatic lane-level road map generation system is proposed.
TargetDrop: A Targeted Regularization Method for Convolutional Neural Networks
Dropout regularization has been widely used in deep learning but performs less effective for convolutional neural networks since the spatially correlated features allow dropped information to still flow through the networks.
Mitigating Query-Flooding Parameter Duplication Attack on Regression Models with High-Dimensional Gaussian Mechanism
Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal confidential information of the learning models through public queries.
Localizing Interpretable Multi-scale informative Patches Derived from Media Classification Task
Deep convolutional neural networks (CNN) always depend on wider receptive field (RF) and more complex non-linearity to achieve state-of-the-art performance, while suffering the increased difficult to interpret how relevant patches contribute the final prediction.
Towards More Efficient and Effective Inference: The Joint Decision of Multi-Participants
Existing approaches to improve the performances of convolutional neural networks by optimizing the local architectures or deepening the networks tend to increase the size of models significantly.
DRNet: Dissect and Reconstruct the Convolutional Neural Network via Interpretable Manners
For VGG16 pre-trained on ImageNet, our method averagely gains 14. 29\% accuracy promotion for two-classes sub-tasks.
Rethinking the Number of Channels for the Convolutional Neural Network
In this paper, we propose a method for efficient automatic architecture search which is special to the widths of networks instead of the connections of neural architecture.
Gated Convolutional Networks with Hybrid Connectivity for Image Classification
We propose a simple yet effective method to reduce the redundancy of DenseNet by substantially decreasing the number of stacked modules by replacing the original bottleneck by our SMG module, which is augmented by local residual.
Ranked #60 on
Image Classification
on CIFAR-10
EENA: Efficient Evolution of Neural Architecture
Latest algorithms for automatic neural architecture search perform remarkable but are basically directionless in search space and computational expensive in training of every intermediate architecture.
Propagation of singularities for gravity-capillary water waves
To obtain these results, we generalize the paradifferential calculus of Bony to weighted Sobolev spaces and develop a semiclassical paradifferential calculus.
Analysis of PDEs
