Deploying high-performance convolutional neural network (CNN) models on
...
Multi-task learning (MTL) seeks to learn a single model to accomplish
mu...
Deploying high-performance vision transformer (ViT) models on ubiquitous...
Efforts to improve the adversarial robustness of convolutional neural
ne...
The architectural advancements in deep neural networks have led to remar...
The ongoing advancements in network architecture design have led to
rema...
Deploying deep convolutional neural network (CNN) models on ubiquitous
I...
Existing vision-language pre-training (VLP) methods primarily rely on pa...
This report describes the details of our approach for the event
dense-ca...
Color fundus photography and Optical Coherence Tomography (OCT) are the ...
For the goal of automated design of high-performance deep convolutional
...
Dense video captioning aims to generate multiple associated captions wit...
Recent advancements in deep neural networks have made remarkable
leap-fo...
Instance segmentation models today are very accurate when trained on lar...
Learning visual knowledge from massive weakly-labeled web videos has
att...
In the recent past, neural architecture search (NAS) has attracted incre...
In recent years, many works in the video action recognition literature h...
In this paper, we propose an efficient NAS algorithm for generating
task...
Neural architecture search (NAS) has emerged as a promising avenue for
a...
We propose DOPS, a fast single-stage 3D object detection method for LIDA...
Convolutional neural networks have witnessed remarkable improvements in
...
Traditionally multi-object tracking and object detection are performed u...
Convolutional neural networks (CNNs) are the backbones of deep learning
...
This paper introduces NSGA-Net, an evolutionary approach for neural
arch...