To address this problem, we propose a novel replay strategy called Manifold Expansion Replay (MaER).
Experimental results demonstrate that GeqBevNet can extract more rotational equivariant features in the 3D object detection of the actual road scene and improve the performance of object orientation prediction.
Motivated by the important role of ID, in this paper, we propose a novel deep representation learning approach with autoencoder, which incorporates regularization of the global and local ID constraints into the reconstruction of data representations.
At first, the MGT divides point cloud data into patches with multiple scales.
We propose an algorithmic framework that leverages the advantages of the DNNs for data self-expression and task-specific predictions, to improve image classification.
To address these problems, a novel method, namely, Vision Reservoir computing (ViR), is proposed here for image classification, as a parallel to ViT.
Learning deep models with both lightweight and robustness is necessary for these equipments.
There are some inadequacies in the language description of this paper that require further improvement.
The complex structure of CNNs results in prohibitive training efforts.