Typically, object detection methods for autonomous driving that rely on
...
Due to the lack of real multi-agent data and time-consuming of labeling,...
Deep learning-based intelligent vehicle perception has been developing
p...
Modern perception systems of autonomous vehicles are known to be sensiti...
Accurate detection of large-scale, elliptical-shape fibers, including th...
Deep learning has been widely used in the perception (e.g., 3D object
de...
Computer vision applications in intelligent transportation systems (ITS)...
Most object detection methods for autonomous driving usually assume a
co...
Existing multi-agent perception algorithms usually select to share deep
...
Most of the existing semantic segmentation approaches with image-level c...
Generating precise class-aware pseudo ground-truths, a.k.a, class activa...
Self-Supervised learning aims to eliminate the need for expensive annota...
Restoring and inpainting the visual memories that are present, but often...
Recent studies show that Graph Neural Networks (GNNs) are vulnerable to
...
Renovating the memories in old photos is an intriguing research topic in...
Existing document-level neural machine translation (NMT) models have
suf...
Deep neural networks such as BERT have made great progress in relation
c...
To handle different types of Many-Objective Optimization Problems (MaOPs...
Network embedding is a very important method for network data. However, ...
For machine learning task, lacking sufficient samples mean the trained m...