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Found 8 papers, 4 papers with code
Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learning
Moreover, we design a multi-stage strategy for training S2C model, which mitigates the training challenges posed by limited data in the incremental process.
Dynamic V2X Autonomous Perception from Road-to-Vehicle Vision
Vehicle-to-everything (V2X) perception is an innovative technology that enhances vehicle perception accuracy, thereby elevating the security and reliability of autonomous systems.
Two-level Graph Network for Few-Shot Class-Incremental Learning
Class-level graph network aims to mitigate the semantic conflict between prototype features of new classes and old classes.
Multi-Label Continual Learning using Augmented Graph Convolutional Network
In contrast, the inter-task relationships leverage hard and soft labels from data and a constructed expert network.
Class-Incremental Lifelong Learning in Multi-Label Classification
This paper studies Lifelong Multi-Label (LML) classification, which builds an online class-incremental classifier in a sequential multi-label classification data stream.
AGCN: Augmented Graph Convolutional Network for Lifelong Multi-label Image Recognition
The key challenges of LML image recognition are the construction of label relationships on Partial Labels of training data and the Catastrophic Forgetting on old classes, resulting in poor generalization.
Each Attribute Matters: Contrastive Attention for Sentence-based Image Editing
Sentence-based Image Editing (SIE) aims to deploy natural language to edit an image.
Exploiting Color Name Space for Salient Object Detection
In this paper, we will investigate the contribution of color names for the task of salient object detection.
