Multi-Label Continual Learning Using Augmented Graph Convolutional ...
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Aug 16, 2023 · This study proposes an enhanced version of the Augmented Graph Convolutional Network (AGCN++), capable of constructing cross-task label relationships and ...
Nov 27, 2022 · The study proposes an Augmented Graph Convolutional Network (AGCN++) that can construct the cross-task label relationships in MLCL and sustain catastrophic ...
Feb 14, 2024 · Abstract— Multi-Label Continual Learning (MLCL) is a framework designed for class-incremental multi-label image recognition.
Dec 7, 2022 · Multi-Label Continual Learning (MLCL) builds a class-incremental framework in a sequential multi-label image recognition data stream.
To address these challenges, this study proposes an enhanced version of the Augmented Graph Convolutional Network (AGCN++), capable of constructing cross-task ...
An Augmented Graph Convolutional Network is proposed to build an Augmented Correlation Matrix (ACM) across the sequential partial-label tasks and sustain ...
Mar 10, 2022 · The study proposes an Augmented Graph Convolutional Network (AGCN) model that can construct the label relationships across the sequential recognition tasks.
Missing: Continual | Show results with:Continual
Abstract. Multi-Label Continual Learning (MLCL) is a framework designed for class-incremental multi-label image recognition.
Multi-Label Continual Learning using Augmented Graph Convolutional Network ... In contrast, the inter-task relationships leverage hard and soft labels from data ...
In this paper, we aim to refine multi-label confidence calibration in MLCIL and propose a Confidence Self-Calibration (CSC) approach.