State-of-the-art deep learning systems (eg, ImageNet image classification) typically require
very large training sets to achieve high accuracies. Therefore, one of the grand challenges is …

Deep learning-based univariate time series classification can improve the user experience
of Open Radio Access Network (ORAN)-based Cellular Vehicle-to-Everything (CV2x) …

Abstract Domain Adaptive Few-Shot Learning (DA-FSL) aims at accomplishing few-shot
classification tasks on a novel domain with the aid of a large number of source-style samples …

This study introduces a novel framework for optimizing energy efficiency and computational
load in safety-critical robotic systems operating in nonlinear domains. Leveraging Graph …

Open-world 3D instance segmentation is a recently introduced problem with diverse
applications, notably in continually learning embodied agents. This task involves …

Lifelong object re-identification incrementally learns from a stream of re-identification tasks.
The objective is to learn a representation that can be applied to all tasks and that …

Meta-learning approaches have recently achieved promising performance in multi-class
incremental learning. However, meta-learners still suffer from catastrophic forgetting, ie, they …

Intelligent fault diagnosis has attracted much attention in industrial processes. The difficulty
of collecting fault samples and high price of labeling data, has led to a relative scarcity of …

Abstract Multimodal Named Entity Recognition (MNER) models typically require a significant
volume of labeled data for effective training to extract relations between entities. In real …

Incremental few-shot semantic segmentation (IFSS) expands segmentation capacity of the
trained model to segment newclass images with few samples. However, semantic meanings …