Large-scale joint training of multimodal models, e.g., CLIP, have
demons...
Symbolic regression (SR) is the process of discovering hidden relationsh...
Reducing communication overhead in federated learning (FL) is challengin...
Neural Architecture Search (NAS) has received increasing attention becau...
Deep neural networks (DNNs) are found to be vulnerable to adversarial
at...
Neural Architecture Search (NAS) is an automatic technique that can sear...
Capitalizing on the rapid development of neural networks, recent video f...
Evolutionary computation-based neural architecture search (ENAS) is a po...
Recently audio-driven talking face video generation has attracted
consid...
Neural Architecture Search (NAS) can automatically design architectures ...
This paper introduces a new matting task called human instance matting (...
Recently, talking-face video generation has received considerable attent...
3D point cloud understanding is an important component in autonomous dri...
Collaborative Filtering (CF) is widely used in recommender systems to mo...
Neural architecture search (NAS), which automatically designs the
archit...
Neural Architecture Search (NAS) can automatically design well-performed...
Arrhythmia is a cardiovascular disease that manifests irregular heartbea...
Despite the significant progress made by deep learning in natural image
...
Natural image matting separates the foreground from background in fracti...
Parameter updating is an important stage in parallelism-based distribute...
Evolutionary Neural Architecture Search (ENAS) can automatically design ...
Deep Neural Networks (DNNs) have achieved great success in many applicat...
Hyperspectral images (HSIs) are susceptible to various noise factors lea...
We present a novel end-to-end framework named as GSNet (Geometric and
Sc...
Synchronous strategies with data parallelism, such as the Synchronous
St...
Currently, there have been many kinds of voxel-based 3D single stage
det...
Numerous Convolutional Neural Network (CNN) models have demonstrated the...
Numerous Convolutional Neural Network (CNN) models have demonstrated the...
We present a new two-stage 3D object detection framework, named
sparse-t...
In recent years, convolutional neural networks (CNNs) have become deeper...
Image classification is a difficult machine learning task, where
Convolu...
We present a novel 3D object detection framework, named IPOD, based on r...
The performance of Convolutional Neural Networks (CNNs) highly relies on...
Convolutional Neural Networks (CNNs) have demonstrated their superiority...
Convolutional Neural Networks (CNNs) have demonstrated their superiority...
Convolutional Neural Networks (CNNs) have gained a remarkable success on...
Convolutional neural networks (CNNs) are one of the most effective deep
...
Inverted Generational Distance (IGD) has been widely considered as a rel...
The performance of multi-objective evolutionary algorithms deteriorates
...
Deep Learning (DL) aims at learning the meaningful representations. A
me...
Convolutional auto-encoders have shown their remarkable performance in
s...
Evolutionary computation methods have been successfully applied to neura...